The relationship between cytokine and neutrophil gene network distinguishes SARS-CoV-2–infected patients by sex and age
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The fact that the COVID-19 fatality rate varies by sex and age is poorly understood. Notably, the outcome of SARS-CoV-2 infections mostly depends on the control of cytokine storm and the increasingly recognized pathological role of uncontrolled neutrophil activation. Here, we used an integrative approach with publicly available RNA-Seq data sets of nasopharyngeal swabs and peripheral blood leukocytes from patients with SARS-CoV-2, according to sex and age. Female and young patients infected by SARS-CoV-2 exhibited a larger number of differentially expressed genes (DEGs) compared with male and elderly patients, indicating a stronger immune modulation. Among them, we found an association between upregulated cytokine/chemokine- and downregulated neutrophil-related DEGs. This was correlated with a closer relationship between female and young subjects, while the relationship between male and elderly patients was closer still. The association between these cytokine/chemokines and neutrophil DEGs is marked by a strongly correlated interferome network. Here, female patients exhibited reduced transcriptional levels of key proinflammatory/neutrophil-related genes, such as CXCL8 receptors (CXCR1 and CXCR2), IL-1β, S100A9, ITGAM, and DBNL, compared with male patients. These genes are well known to be protective against inflammatory damage. Therefore, our work suggests specific immune-regulatory pathways associated with sex and age of patients infected with SARS-CoV-2 and provides a possible association between inverse modulation of cytokine/chemokine and neutrophil transcriptional signatures.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it