HLA, Immune Response, and Susceptibility to COVID-19
Bibliographic record
Abstract
The severe acute respiratory syndrome caused by Coronavirus 2 (SARS-CoV-2) that appeared in December 2019 has precipitated the global pandemic Coronavirus Disease 2019 (COVID-19). However, in many parts of Africa fewer than expected cases of COVID-19, with lower rates of mortality, have been reported. Individual human leukocyte antigen (HLA) alleles can affect both the susceptibility and the severity of viral infections. In the case of COVID-19 such an analysis may contribute to identifying individuals at higher risk of the disease and the epidemiological level to understanding the differences between countries in the epidemic patterns. It is also recognized that first antigen exposure influences the consequence of subsequent exposure. We thus propose a theory incorporating HLA antigens, the "original antigenic sin (OAS)" effect, and presentation of viral peptides which could explain with differential susceptibility or resistance to SARS-CoV-2 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".