Gene Polymorphisms of Epithelial Cell-Derived Alarmins and Their Effects on Protein Levels and Disease Severity in Patients with COVID-19
Bibliographic record
Abstract
Background: The immune response in COVID-19 is characterized by the release of alarmin cytokines, which play crucial roles in immune activation and inflammation. The interplay between these cytokines and genetic variations may influence disease severity and outcomes, while sex differences might further contribute to variations in the immune response. Methods: We measured the levels of alarmin cytokines in a cohort of COVID-19 and non-COVID-19 patients using a sensitive Meso Scale Discovery system. Additionally, we conducted an SNP analysis to identify genetic variations within the IL-33 and TSLP genes. The association between these genetic variations, cytokine production, and COVID-19 severity was examined. Results: Our findings revealed elevated levels of IL-33 and IL-25 in COVID-19-positive patients compared to COVID-19-negative patients (p < 0.05), indicating their potential as therapeutic targets for disease modulation. Moreover, a minor allele within the IL-33 gene (rs3939286) was found to be associated with a protective effect against severe COVID-19 (p < 0.05), and minor alleles of the TSLP gene (rs2289276 and rs13806933) were found to significantly reduce TSLP protein levels in serum (p < 0.05). Sex-specific effects of TSLP and IL-33 SNPs were observed, suggesting a potential influence of sex hormones and genetic variations on the regulation of cytokine production. Conclusion: The present study highlights the importance of alarmin cytokines and genetic variations in COVID-19 severity, providing valuable insights into personalized treatment approaches. Our results suggest that targeting alarmin cytokines may offer potential therapeutic benefits in managing COVID-19. Furthermore, the sex-specific effects of genetic variations emphasize the need to consider individual genetic profiles and sex differences when designing targeted interventions.
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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 | 0.001 |
| 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) | 0.002 | 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".