The Molecular Basis of Gender Variations in Mortality Rates Associated With the Novel Coronavirus (COVID-19) Outbreak
Bibliographic record
Abstract
Since the outbreak of the novel coronavirus disease (COVID-19) at the end of 2019, the clinical presentation of the disease showed a great heterogeneity with a diverse impact among different subpopulations. Emerging evidence from different parts of the world showed that male patients usually had a longer disease course as well as worse outcome compared to female patients. A better understanding of the molecular mechanisms behind this difference might be a fundamental step for more effective and personalized response to this disease outbreak. For that reason, here we investigate the molecular basis of gender variations in mortality rates related to COVID-19 infection. To achieve this, we used publicly available lung transcriptomic data from 141 females and compare it to 286 male lung tissues. After excluding Y specific genes, our results showed a shortlist of 73 genes that are differentially expressed between the two groups. Further analysis using pathway enrichment analysis revealed downregulation of a group of genes that are involved in the regulation of hydrolase activity including (CHM, DDX3X, FGFR3, SFRP2, and NLRP2) in males lungs compared to females. This pathway is believed to be essential for immune response and antimicrobial activity in the lung tissues. In contrast, our results showed an increased upregulation of angiotensin II receptor type 1 (AGTR1), a member of the renin-angiotensin system (RAS) that plays a role in angiotensin-converting enzyme 2 (ACE2) activity modulation in male lungs compared to females. Finally, our results showed a differential expression of genes involved in the immune response including the NLRP2 and PTGDR2 in lung tissues of both genders, further supporting the notion of the sex-based immunological differences. Taken together, our results provide an initial evidence of the molecular mechanisms that might be involved in the differential outcomes observed in both genders during the COVID-19 outbreak. This maybe essential for the discovery of new targets and more precise therapeutic options to treat COVID-19 patients from different clinical and epidemiological characteristics with the aim of improving their outcome.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".