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Major issues

1. Lack of a consistent experimental design to dissect sex-dependent effects The study aims to investigate temperature-dependent immune responses, including potential sex differences; however, sex is not consistently incorporated across experimental cohorts. Some experiments are performed exclusively in one sex, while other cohorts do not clearly report sex distribution. This design makes it difficult to distinguish sex-specific effects from stimulus-specific responses. A balanced comparison of male and female samples under identical stimulation conditions is required.

2. Insufficient matched unstimulated controls across experimental settings The study relies on multiple immune stimuli (e.g., SEB, Poly(I:C), E. coli, LPS, R848), but comparable unstimulated baseline controls are not consistently described. Without matched baseline controls for each sex and temperature condition, it remains unclear whether observed transcriptional/protein changes represent true stimulus-induced effects or baseline differences.

3. Lack of harmonization between experimental cohorts Different experiments use distinct stimulus panels, sample sizes, and readout approaches, limiting direct comparison between datasets. A fixed core stimulus panel applied across all cohorts would allow clearer interpretation of temperature- and sex-dependent immune responses.

4. Limited mechanistic resolution of immune alterations The study mainly describes global transcriptional and cytokine changes but provides limited cell-type-specific mechanistic investigation. Single-cell or CITE-seq-based analyses should be leveraged to identify which immune populations drive the observed responses and define cell-specific transcriptional programs.

5. Limited temporal resolution of immune responses The current experimental design appears largely endpoint-based, making it difficult to distinguish early activation events from later adaptive or regulatory responses. Time-course experiments would strengthen the interpretation of temperature-driven immune modulation.

Minor issues

1. Stimulus selection rationale requires clearer justification The rationale for selecting different immune stimuli across experiments is not fully explained. A clearer biological framework linking each stimulus to specific immune pathways would improve interpretability.

2. Incomplete reporting of donor and sample metadata More detailed metadata (including sex distribution, age, and inter-donor variability) should be provided, particularly for human cohorts, to ensure reproducibility and correct interpretation of variability.

3. Limited linkage between transcriptional and functional outcomes While transcriptional and cytokine changes are described, functional immune consequences (e.g., cytotoxicity, antigen response, effector function) are not fully validated.

4. Need for validation of key molecular pathways Key pathways identified through transcriptomic analysis would benefit from additional experimental validation (e.g., qPCR, protein-level assays, or functional readouts).

5. Statistical modeling does not fully integrate biological variables Analytical models should explicitly incorporate key covariates such as sex, temperature, donor identity, and stimulus type, as well as potential interaction effects.

Future directions / Suggested improvements

1. Incorporation of perturbation-based validation approaches Future studies should include perturbation experiments (e.g., pathway inhibition, gene targeting, or receptor blockade) to determine whether identified transcriptional programs are causally involved in temperature- and stimulus-dependent immune responses.

2. Use of rescue or reversal experiments where applicable Where mechanistic pathways are proposed, rescue experiments (e.g., restoring signaling after inhibition or reversing temperature-induced effects) would strengthen causal interpretation.

3. Increased mechanistic resolution using cell-type-specific profiling Single-cell or CITE-seq-based approaches should be further leveraged to dissect immune responses at the level of individual cell populations, enabling identification of lineage-specific programs (e.g., CD8 T cells, NK cells, plasma cells)

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they used generative AI to come up with new ideas for their review.