Gene: environment interactions in immune and inflammatory responses to severe acute respiratory syndrome coronavirus 2 infection
Bibliographic record
Abstract
Despite its devastating human cost, the rapid spread and global establishment of the severe acute respiratory syndrome coronavirus (SARS-CoV-2) pandemic had the benefit of providing unique insights into the intricate interplay between genetic, environmental, and socioeconomic factors, which collectively impact susceptibility to infection with SARS-CoV-2. Preceding the implementation of broad vaccination programs and assuming the absence of significant acquired immunity, examining the innate vulnerability to the virus becomes essential. There is indeed considerable heterogeneity observed at both the population and individual levels for various SARS-CoV-2 infection phenotypes, including emergence, progression, and survival from the coronavirus disease 2019 (COVID-19) syndrome. Particularly intriguing is the seemingly milder course of COVID-19 disease reported for the African continent early during the pandemic. This was characterized by significantly lower mortality rates in SARS-CoV-2 patients compared with the European and American continents and globally. We will discuss some of the demographic and socioeconomic factors that may have contributed to these observations. We review the mapped COVID-19 genetic architecture, including the remarkable association of type I interferon as a single protective mechanism and a major determinant of susceptibility. Furthermore, we speculate on potential 'environmental' modulators of penetrance and expressivity of intrinsic vulnerability factors, with a focus on the microbiome and associated metabolomes. Additionally, this review explores the potential immunomodulatory contribution of helminth parasites to the human host immune and inflammatory responses to respiratory viral infections.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".