Title: Unveiling the diagnostic and pro-inflammatory role of Crohn’s disease: insights from
16S-guided discovery and species-specific validation
Version: 4 Date: 11 Apr 2025
Reviewer's report:
1. Scientific Merit and Originality
The topic addressed by the manuscript is highly relevant given the increasing incidence of
Crohn’s disease (CD) in Asia and the recognized role of microbiota in inflammatory bowel
disease (IBD). The study’s focus on Ruminococcus gnavus as a possible pro-inflammatory
biomarker is justified by previous literature. However, while the originality is appreciable, the
novelty in the experimental setup remains limited due to the frequent use of DSS-induced colitis
models and 16S rRNA analyses in this research field. The incremental value could be enhanced
by expanding mechanistic validation.
2. Methodological Assessment
Study Design: The observational phase using 16S sequencing in three groups (active CD,
inactive CD, healthy controls) is sound in concept. However, the manuscript lacks a clear
description of inclusion/exclusion criteria for patient selection and the statistical power
calculation. These are crucial to assess representativeness and avoid selection bias.
Animal Model: The use of the DSS-induced colitis model is acceptable for mimicking intestinal
inflammation. However, the justification for the specific strain of R. gnavus (ATCC 29149) and
the choice of dosage/treatment duration in the murine experiments should be better detailed,
including ethical approval numbers and welfare conditions. Further, it is unclear whether the
experimental design included sufficient replicates to ensure statistical robustness.
Random Forest Model: While the application of a random forest classifier is suitable for feature
selection, the description of how the model was trained, validated, and tested (e.g., k-fold cross-
validation, avoidance of overfitting) remains insufficiently reported. I strongly recommend
providing receiver operating characteristic (ROC) curves, AUC values, and confusion matrices
to validate classifier performance beyond listing microbial taxa.
3. Statistical Analysis
The statistical analyses are generally underreported. While P-values and some confidence
intervals are provided, there is no mention of effect sizes or adjustments for multiple
comparisons (e.g., Bonferroni or Benjamini-Hochberg), which is essential given the high
dimensionality of microbiome data.
Additionally, correlation analyses between clinical indices and microbial abundance should
clearly report the type of correlation (Spearman vs. Pearson) and whether the data distribution
was assessed for normality.
In animal model data, there is insufficient explanation of whether parametric or non-parametric
tests were used, and whether the assumptions of ANOVA or t-tests were satisfied. Reporting of
standard deviation (SD) and sample sizes (n) per group is inconsistent throughout.
4. Critical Scientific Gaps and Biases
Microbial Causality vs. Association: The study describes associations between R. gnavus and
CD severity but does not explore causality convincingly. The animal model helps bridge this gap
but lacks molecular validation (e.g., cytokine mRNA/protein quantification, epithelial barrier
assays).
Microbiota Diversity Indices: The manuscript omits essential microbiome diversity metrics
(alpha and beta diversity), which are standard in 16S rRNA analyses. Their inclusion would
offer a more holistic view of microbial community shifts.
Host Immune Response: There is minimal integration of host immune profiling (e.g., IL-1#,
TNF-#, IL-10) in the DSS model. Integrating host-microbiome interactions would significantly
strengthen the translational value of the study.
External Validity: The study is limited to a single geographic region (China), which may
not generalize to Western populations with differing microbial baselines. This should be
acknowledged more transparently in the discussion.
5. Manuscript Presentation and Reporting
The structure is mostly clear, although several results are described in a narrative style that would
benefit from quantitative precision.
The figures (based on filenames) should be presented with more detailed legends, including
experimental conditions, statistical annotations, and whether the data are representative of
independent experiments.
The supplementary tables should be integrated or referenced more clearly within the main
manuscript to avoid information fragmentation.
The revised manuscript is improved in fluency and grammar, although minor revisions in
English style and consistency (e.g., use of British vs. American spelling) are still warranted.
6. Recommendations for Revision
To prepare the manuscript for acceptance, I recommend the following critical revisions:
Clarify patient selection criteria, ethical approvals, and perform a power analysis to justify
sample sizes.
Expand the statistical reporting, including effect sizes, adjusted P-values, and classifier metrics.
Incorporate alpha and beta diversity metrics to enrich microbiota analysis.
Add mechanistic immune response data (cytokines, histology scores, barrier function assays) to
support the inflammatory role of R. gnavus.
Improve reporting transparency by adopting STROBE and ARRIVE guidelines for human and
animal experiments, respectively.
7. Ethical Considerations and Reproducibility
Human Subjects: Although the manuscript mentions clinical data collection, there is insufficient
detail on ethical approval for the human study component. It is essential to explicitly state the
Institutional Review Board (IRB) protocol number, informed consent process, and any data
protection measures under local ethical standards (e.g., Declaration of Helsinki compliance).
Animal Ethics: Similarly, the manuscript must include the animal protocol number from the
institutional ethics committee. Details on housing, randomization, blinding procedures, humane
endpoints, and mortality (if any) during DSS-induced colitis must be disclosed to meet ARRIVE
2.0 guidelines.
Data Transparency: Reproducibility is compromised by the lack of access to raw sequencing
data and code. The authors should be required to deposit all 16S rRNA raw data in a public
repository such as the NCBI Sequence Read Archive (SRA), including accession numbers in the
manuscript. Additionally, the code and scripts for the machine learning models (random forest
pipeline) should be made available via GitHub or similar platforms with detailed documentation
to allow reproducibility.
8. Interpretation and Integration with Literature
Overstatement of Findings: While the increase of R. gnavus is statistically significant, the
manuscript tends to overstate its pathogenic role without sufficient mechanistic support. There is
a need to balance discussion between correlation and causation, clearly delineating hypothesis-
generating insights from confirmatory evidence.
Lack of Mechanistic Pathway Exploration: No molecular pathways (e.g., Toll-like receptors,
NF-#B, epithelial permeability markers) are investigated to mechanistically link R. gnavus
to inflammation. Incorporating such mechanistic pathways—perhaps via qPCR or Western
blotting for inflammatory mediators—would considerably strengthen causal inferences and
scientific depth.
Literature Contextualization: The discussion section underutilizes recent meta-analyses and
systematic reviews on microbiome signatures in IBD. Several key references are missing,
particularly global cohort studies from Europe and North America, which would offer a
comparative framework. It is advisable to incorporate and contrast findings with recent reviews
from Nature Reviews Gastroenterology & Hepatology, Gut, and Inflammatory Bowel Diseases
journals to contextualize geographic, demographic, and methodological differences.
9. Bias Assessment and Limitations Disclosure
Selection Bias: As noted earlier, there is no mention of matching for age, sex, diet, or medication
use (e.g., antibiotics, biologics, corticosteroids), all of which are significant confounders in
microbiome research. These factors should be explicitly addressed either via stratified analysis
or as covariates in multivariate models.
Detection Bias: Without standardized DNA extraction and library preparation methods, batch
effects could have influenced 16S results. A sensitivity analysis or methodological quality
control (e.g., rarefaction curves) should be presented to reassure readers.
Reporting Bias: Figures and results focus disproportionately on R. gnavus, potentially
overlooking other relevant taxa. A more systematic reporting of other top-ranked microbial
genera identified in the random forest model is warranted.
10. Suggested Future Directions
To expand the translational impact of this study and foster subsequent research efforts, I propose
the following:
Longitudinal Designs: Future work should include longitudinal follow-up of CD patients to
assess microbial shifts over time, especially across treatment regimens, relapses, and remission
phases.
Functional Metagenomics: Instead of relying solely on 16S rRNA taxonomy, metagenomic
sequencing (e.g., shotgun metagenomics or metatranscriptomics) should be considered to
explore gene expression profiles, metabolic pathways, and virulence determinants associated
with R. gnavus.
Microbial Intervention Studies: Validation of R. gnavus’s pathogenic role could be strengthened
through targeted intervention studies (e.g., antibiotic suppression, bacteriophage therapy, or
probiotic competition assays) in murine models.
Host-Microbiota Multiomics: Integration of host transcriptomics or metabolomics with
microbiome data would enhance mechanistic insights and support systems-level interpretations
of the inflammatory cascade in CD.
11. Conclusion and Editorial Recommendation
In conclusion, this manuscript addresses a clinically meaningful question in Crohn’s disease
research. However, it currently falls short of the methodological rigor and mechanistic depth
expected for publication in a high-impact journal. Key elements such as transparent statistical
modeling, ethical reporting, mechanistic validation, and data reproducibility need to be revised
and expanded.
Recommendation: Major Revisions Required
Title: Unveiling the diagnostic and pro-inflammatory role of Crohn’s disease: insights from
16S-guided discovery and species-specific validation
Version: 4 Date: 11 Apr 2025
Reviewer's report:
1. Scientific Merit and Originality
The topic addressed by the manuscript is highly relevant given the increasing incidence of
Crohn’s disease (CD) in Asia and the recognized role of microbiota in inflammatory bowel
disease (IBD). The study’s focus on Ruminococcus gnavus as a possible pro-inflammatory
biomarker is justified by previous literature. However, while the originality is appreciable, the
novelty in the experimental setup remains limited due to the frequent use of DSS-induced colitis
models and 16S rRNA analyses in this research field. The incremental value could be enhanced
by expanding mechanistic validation.
2. Methodological Assessment
Study Design: The observational phase using 16S sequencing in three groups (active CD,
inactive CD, healthy controls) is sound in concept. However, the manuscript lacks a clear
description of inclusion/exclusion criteria for patient selection and the statistical power
calculation. These are crucial to assess representativeness and avoid selection bias.
Animal Model: The use of the DSS-induced colitis model is acceptable for mimicking intestinal
inflammation. However, the justification for the specific strain of R. gnavus (ATCC 29149) and
the choice of dosage/treatment duration in the murine experiments should be better detailed,
including ethical approval numbers and welfare conditions. Further, it is unclear whether the
experimental design included sufficient replicates to ensure statistical robustness.
Random Forest Model: While the application of a random forest classifier is suitable for feature
selection, the description of how the model was trained, validated, and tested (e.g., k-fold cross-
validation, avoidance of overfitting) remains insufficiently reported. I strongly recommend
providing receiver operating characteristic (ROC) curves, AUC values, and confusion matrices
to validate classifier performance beyond listing microbial taxa.
3. Statistical Analysis
The statistical analyses are generally underreported. While P-values and some confidence
intervals are provided, there is no mention of effect sizes or adjustments for multiple
comparisons (e.g., Bonferroni or Benjamini-Hochberg), which is essential given the high
dimensionality of microbiome data.
Additionally, correlation analyses between clinical indices and microbial abundance should
clearly report the type of correlation (Spearman vs. Pearson) and whether the data distribution
was assessed for normality.
In animal model data, there is insufficient explanation of whether parametric or non-parametric
tests were used, and whether the assumptions of ANOVA or t-tests were satisfied. Reporting of
standard deviation (SD) and sample sizes (n) per group is inconsistent throughout.
4. Critical Scientific Gaps and Biases
Microbial Causality vs. Association: The study describes associations between R. gnavus and
CD severity but does not explore causality convincingly. The animal model helps bridge this gap
but lacks molecular validation (e.g., cytokine mRNA/protein quantification, epithelial barrier
assays).
Microbiota Diversity Indices: The manuscript omits essential microbiome diversity metrics
(alpha and beta diversity), which are standard in 16S rRNA analyses. Their inclusion would
offer a more holistic view of microbial community shifts.
Host Immune Response: There is minimal integration of host immune profiling (e.g., IL-1#,
TNF-#, IL-10) in the DSS model. Integrating host-microbiome interactions would significantly
strengthen the translational value of the study.
External Validity: The study is limited to a single geographic region (China), which may
not generalize to Western populations with differing microbial baselines. This should be
acknowledged more transparently in the discussion.
5. Manuscript Presentation and Reporting
The structure is mostly clear, although several results are described in a narrative style that would
benefit from quantitative precision.
The figures (based on filenames) should be presented with more detailed legends, including
experimental conditions, statistical annotations, and whether the data are representative of
independent experiments.
The supplementary tables should be integrated or referenced more clearly within the main
manuscript to avoid information fragmentation.
The revised manuscript is improved in fluency and grammar, although minor revisions in
English style and consistency (e.g., use of British vs. American spelling) are still warranted.
6. Recommendations for Revision
To prepare the manuscript for acceptance, I recommend the following critical revisions:
Clarify patient selection criteria, ethical approvals, and perform a power analysis to justify
sample sizes.
Expand the statistical reporting, including effect sizes, adjusted P-values, and classifier metrics.
Incorporate alpha and beta diversity metrics to enrich microbiota analysis.
Add mechanistic immune response data (cytokines, histology scores, barrier function assays) to
support the inflammatory role of R. gnavus.
Improve reporting transparency by adopting STROBE and ARRIVE guidelines for human and
animal experiments, respectively.
7. Ethical Considerations and Reproducibility
Human Subjects: Although the manuscript mentions clinical data collection, there is insufficient
detail on ethical approval for the human study component. It is essential to explicitly state the
Institutional Review Board (IRB) protocol number, informed consent process, and any data
protection measures under local ethical standards (e.g., Declaration of Helsinki compliance).
Animal Ethics: Similarly, the manuscript must include the animal protocol number from the
institutional ethics committee. Details on housing, randomization, blinding procedures, humane
endpoints, and mortality (if any) during DSS-induced colitis must be disclosed to meet ARRIVE
2.0 guidelines.
Data Transparency: Reproducibility is compromised by the lack of access to raw sequencing
data and code. The authors should be required to deposit all 16S rRNA raw data in a public
repository such as the NCBI Sequence Read Archive (SRA), including accession numbers in the
manuscript. Additionally, the code and scripts for the machine learning models (random forest
pipeline) should be made available via GitHub or similar platforms with detailed documentation
to allow reproducibility.
8. Interpretation and Integration with Literature
Overstatement of Findings: While the increase of R. gnavus is statistically significant, the
manuscript tends to overstate its pathogenic role without sufficient mechanistic support. There is
a need to balance discussion between correlation and causation, clearly delineating hypothesis-
generating insights from confirmatory evidence.
Lack of Mechanistic Pathway Exploration: No molecular pathways (e.g., Toll-like receptors,
NF-#B, epithelial permeability markers) are investigated to mechanistically link R. gnavus
to inflammation. Incorporating such mechanistic pathways—perhaps via qPCR or Western
blotting for inflammatory mediators—would considerably strengthen causal inferences and
scientific depth.
Literature Contextualization: The discussion section underutilizes recent meta-analyses and
systematic reviews on microbiome signatures in IBD. Several key references are missing,
particularly global cohort studies from Europe and North America, which would offer a
comparative framework. It is advisable to incorporate and contrast findings with recent reviews
from Nature Reviews Gastroenterology & Hepatology, Gut, and Inflammatory Bowel Diseases
journals to contextualize geographic, demographic, and methodological differences.
9. Bias Assessment and Limitations Disclosure
Selection Bias: As noted earlier, there is no mention of matching for age, sex, diet, or medication
use (e.g., antibiotics, biologics, corticosteroids), all of which are significant confounders in
microbiome research. These factors should be explicitly addressed either via stratified analysis
or as covariates in multivariate models.
Detection Bias: Without standardized DNA extraction and library preparation methods, batch
effects could have influenced 16S results. A sensitivity analysis or methodological quality
control (e.g., rarefaction curves) should be presented to reassure readers.
Reporting Bias: Figures and results focus disproportionately on R. gnavus, potentially
overlooking other relevant taxa. A more systematic reporting of other top-ranked microbial
genera identified in the random forest model is warranted.
10. Suggested Future Directions
To expand the translational impact of this study and foster subsequent research efforts, I propose
the following:
Longitudinal Designs: Future work should include longitudinal follow-up of CD patients to
assess microbial shifts over time, especially across treatment regimens, relapses, and remission
phases.
Functional Metagenomics: Instead of relying solely on 16S rRNA taxonomy, metagenomic
sequencing (e.g., shotgun metagenomics or metatranscriptomics) should be considered to
explore gene expression profiles, metabolic pathways, and virulence determinants associated
with R. gnavus.
Microbial Intervention Studies: Validation of R. gnavus’s pathogenic role could be strengthened
through targeted intervention studies (e.g., antibiotic suppression, bacteriophage therapy, or
probiotic competition assays) in murine models.
Host-Microbiota Multiomics: Integration of host transcriptomics or metabolomics with
microbiome data would enhance mechanistic insights and support systems-level interpretations
of the inflammatory cascade in CD.
11. Conclusion and Editorial Recommendation
In conclusion, this manuscript addresses a clinically meaningful question in Crohn’s disease
research. However, it currently falls short of the methodological rigor and mechanistic depth
expected for publication in a high-impact journal. Key elements such as transparent statistical
modeling, ethical reporting, mechanistic validation, and data reproducibility need to be revised
and expanded.
Recommendation: Major Revisions Required
Please review this paper.
user
Title: Unveiling the diagnostic and pro-inflammatory role of Crohn’s disease: insights from
16S-guided discovery and species-specific validation
Version: 4 Date: 11 Apr 2025
Reviewer's report:
1. Scientific Merit and Originality
The topic addressed by the manuscript is highly relevant given the increasing incidence of
Crohn’s disease (CD) in Asia and the recognized role of microbiota in inflammatory bowel
disease (IBD). The study’s focus on Ruminococcus gnavus as a possible pro-inflammatory
biomarker is justified by previous literature. However, while the originality is appreciable, the
novelty in the experimental setup remains limited due to the frequent use of DSS-induced colitis
models and 16S rRNA analyses in this research field. The incremental value could be enhanced
by expanding mechanistic validation.
2. Methodological Assessment
Study Design: The observational phase using 16S sequencing in three groups (active CD,
inactive CD, healthy controls) is sound in concept. However, the manuscript lacks a clear
description of inclusion/exclusion criteria for patient selection and the statistical power
calculation. These are crucial to assess representativeness and avoid selection bias.
Animal Model: The use of the DSS-induced colitis model is acceptable for mimicking intestinal
inflammation. However, the justification for the specific strain of R. gnavus (ATCC 29149) and
the choice of dosage/treatment duration in the murine experiments should be better detailed,
including ethical approval numbers and welfare conditions. Further, it is unclear whether the
experimental design included sufficient replicates to ensure statistical robustness.
Random Forest Model: While the application of a random forest classifier is suitable for feature
selection, the description of how the model was trained, validated, and tested (e.g., k-fold cross-
validation, avoidance of overfitting) remains insufficiently reported. I strongly recommend
providing receiver operating characteristic (ROC) curves, AUC values, and confusion matrices
to validate classifier performance beyond listing microbial taxa.
3. Statistical Analysis
The statistical analyses are generally underreported. While P-values and some confidence
intervals are provided, there is no mention of effect sizes or adjustments for multiple
comparisons (e.g., Bonferroni or Benjamini-Hochberg), which is essential given the high
dimensionality of microbiome data.
Additionally, correlation analyses between clinical indices and microbial abundance should
clearly report the type of correlation (Spearman vs. Pearson) and whether the data distribution
was assessed for normality.
In animal model data, there is insufficient explanation of whether parametric or non-parametric
tests were used, and whether the assumptions of ANOVA or t-tests were satisfied. Reporting of
standard deviation (SD) and sample sizes (n) per group is inconsistent throughout.
4. Critical Scientific Gaps and Biases
Microbial Causality vs. Association: The study describes associations between R. gnavus and
CD severity but does not explore causality convincingly. The animal model helps bridge this gap
but lacks molecular validation (e.g., cytokine mRNA/protein quantification, epithelial barrier
assays).
Microbiota Diversity Indices: The manuscript omits essential microbiome diversity metrics
(alpha and beta diversity), which are standard in 16S rRNA analyses. Their inclusion would
offer a more holistic view of microbial community shifts.
Host Immune Response: There is minimal integration of host immune profiling (e.g., IL-1#,
TNF-#, IL-10) in the DSS model. Integrating host-microbiome interactions would significantly
strengthen the translational value of the study.
External Validity: The study is limited to a single geographic region (China), which may
not generalize to Western populations with differing microbial baselines. This should be
acknowledged more transparently in the discussion.
5. Manuscript Presentation and Reporting
The structure is mostly clear, although several results are described in a narrative style that would
benefit from quantitative precision.
The figures (based on filenames) should be presented with more detailed legends, including
experimental conditions, statistical annotations, and whether the data are representative of
independent experiments.
The supplementary tables should be integrated or referenced more clearly within the main
manuscript to avoid information fragmentation.
The revised manuscript is improved in fluency and grammar, although minor revisions in
English style and consistency (e.g., use of British vs. American spelling) are still warranted.
6. Recommendations for Revision
To prepare the manuscript for acceptance, I recommend the following critical revisions:
Clarify patient selection criteria, ethical approvals, and perform a power analysis to justify
sample sizes.
Expand the statistical reporting, including effect sizes, adjusted P-values, and classifier metrics.
Incorporate alpha and beta diversity metrics to enrich microbiota analysis.
Add mechanistic immune response data (cytokines, histology scores, barrier function assays) to
support the inflammatory role of R. gnavus.
Improve reporting transparency by adopting STROBE and ARRIVE guidelines for human and
animal experiments, respectively.
7. Ethical Considerations and Reproducibility
Human Subjects: Although the manuscript mentions clinical data collection, there is insufficient
detail on ethical approval for the human study component. It is essential to explicitly state the
Institutional Review Board (IRB) protocol number, informed consent process, and any data
protection measures under local ethical standards (e.g., Declaration of Helsinki compliance).
Animal Ethics: Similarly, the manuscript must include the animal protocol number from the
institutional ethics committee. Details on housing, randomization, blinding procedures, humane
endpoints, and mortality (if any) during DSS-induced colitis must be disclosed to meet ARRIVE
2.0 guidelines.
Data Transparency: Reproducibility is compromised by the lack of access to raw sequencing
data and code. The authors should be required to deposit all 16S rRNA raw data in a public
repository such as the NCBI Sequence Read Archive (SRA), including accession numbers in the
manuscript. Additionally, the code and scripts for the machine learning models (random forest
pipeline) should be made available via GitHub or similar platforms with detailed documentation
to allow reproducibility.
8. Interpretation and Integration with Literature
Overstatement of Findings: While the increase of R. gnavus is statistically significant, the
manuscript tends to overstate its pathogenic role without sufficient mechanistic support. There is
a need to balance discussion between correlation and causation, clearly delineating hypothesis-
generating insights from confirmatory evidence.
Lack of Mechanistic Pathway Exploration: No molecular pathways (e.g., Toll-like receptors,
NF-#B, epithelial permeability markers) are investigated to mechanistically link R. gnavus
to inflammation. Incorporating such mechanistic pathways—perhaps via qPCR or Western
blotting for inflammatory mediators—would considerably strengthen causal inferences and
scientific depth.
Literature Contextualization: The discussion section underutilizes recent meta-analyses and
systematic reviews on microbiome signatures in IBD. Several key references are missing,
particularly global cohort studies from Europe and North America, which would offer a
comparative framework. It is advisable to incorporate and contrast findings with recent reviews
from Nature Reviews Gastroenterology & Hepatology, Gut, and Inflammatory Bowel Diseases
journals to contextualize geographic, demographic, and methodological differences.
9. Bias Assessment and Limitations Disclosure
Selection Bias: As noted earlier, there is no mention of matching for age, sex, diet, or medication
use (e.g., antibiotics, biologics, corticosteroids), all of which are significant confounders in
microbiome research. These factors should be explicitly addressed either via stratified analysis
or as covariates in multivariate models.
Detection Bias: Without standardized DNA extraction and library preparation methods, batch
effects could have influenced 16S results. A sensitivity analysis or methodological quality
control (e.g., rarefaction curves) should be presented to reassure readers.
Reporting Bias: Figures and results focus disproportionately on R. gnavus, potentially
overlooking other relevant taxa. A more systematic reporting of other top-ranked microbial
genera identified in the random forest model is warranted.
10. Suggested Future Directions
To expand the translational impact of this study and foster subsequent research efforts, I propose
the following:
Longitudinal Designs: Future work should include longitudinal follow-up of CD patients to
assess microbial shifts over time, especially across treatment regimens, relapses, and remission
phases.
Functional Metagenomics: Instead of relying solely on 16S rRNA taxonomy, metagenomic
sequencing (e.g., shotgun metagenomics or metatranscriptomics) should be considered to
explore gene expression profiles, metabolic pathways, and virulence determinants associated
with R. gnavus.
Microbial Intervention Studies: Validation of R. gnavus’s pathogenic role could be strengthened
through targeted intervention studies (e.g., antibiotic suppression, bacteriophage therapy, or
probiotic competition assays) in murine models.
Host-Microbiota Multiomics: Integration of host transcriptomics or metabolomics with
microbiome data would enhance mechanistic insights and support systems-level interpretations
of the inflammatory cascade in CD.
11. Conclusion and Editorial Recommendation
In conclusion, this manuscript addresses a clinically meaningful question in Crohn’s disease
research. However, it currently falls short of the methodological rigor and mechanistic depth
expected for publication in a high-impact journal. Key elements such as transparent statistical
modeling, ethical reporting, mechanistic validation, and data reproducibility need to be revised
and expanded.
Recommendation: Major Revisions Required
assistant