Molecular Understanding of ACE-2 and HLA-Conferred Differential Susceptibility to COVID-19: Host-Directed Insights Opening New Windows in COVID-19 Therapeutics
Bibliographic record
Abstract
The genetic variants of HLAs (human leukocyte antigens) play a crucial role in the virus-host interaction and pathology of COVID-19. The genetic variants of HLAs not only influence T cell immune responses but also B cell immune responses by presenting a variety of peptide fragments of invading pathogens. Peptide cocktail vaccines produced by using various conserved HLA-A2 epitopes provoke substantial specific CD8+ T cell responses in experimental animals. The HLA profiles vary among individuals and trigger different T cell-mediated immune responses in COVID-19 infections. Those with HLA-C*01 and HLA-B*44 are highly susceptible to the disease. However, HLA-A*02:01, HLA-DR*03:01, and HLA-Cw*15:02 alleles show resistance to SARS infection. Understanding the genetic association of HLA with COVID-19 susceptibility and severity is important because it can help in studying the transmission of COVID-19 and its physiopathogenesis. The HLA-C*01 and B*44 allele pathways can be studied to gain insight into disease transmission and physiopathogenesis. Therefore, integrating HLA testing is suggested in the ongoing pandemic, which will help in the rapid identification of highly susceptible populations worldwide and possibly acclimate vaccine development. Therefore, understanding the correlation between HLA and SARS-CoV-2 is critical in opening new insights into COVID-19 therapeutics, based on previous studies conducted.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".