HLA and red blood cell antigen genotyping in SARS-CoV-2 convalescent plasma donors
Bibliographic record
Abstract
Abstract Introduction The SARS-CoV-2 pandemic has put significant additional pressure on healthcare systems throughout the world. The identification of at-risk population beyond age, pre-existing medical conditions and socioeconomic status has been the subject of only a small part of the global COVID-19 research so far. To this day, more data is required regarding the association between HLA allele and red blood cell (RBC) antigens’ expression in regard to SARS-CoV-2 infection susceptibility and virus clearance capability, and COVID-19 susceptibility, severity, and duration. Methods The phenotypes for ABO and RhD, and the genotypes for 37 RBC antigens and HLA-A, B, C, DRB1, DQB1 and DPB1 were determined using high throughput platforms (Luminex and Next-generation Sequencing) in 90 Caucasian convalescent plasma donors. The results were compared to expected reference frequencies, local and international databases, and literature. Results The AB group was significantly increased (1.5x, p=0.018) and a non-significant (2.2x, p=0.030) increase was observed for the FY*A allele frequency in the convalescent cohort (N=90) compared to reference frequencies. Some HLA alleles were found significantly overrepresented (HLA-B*44:02, C*05:01, DPB1*04:01, DRB1*04:01 and DRB1*07:01) or underrepresented (A*01:01, B51:01 and DPB1*04:02) in convalescent individuals compared to the local bone marrow registry population. Conclusion Our study of infection-susceptible but non-hospitalized Caucasian COVID-19 patients contributes to the global understanding of host genetic factors associated with SARS-CoV-2 infection susceptibility and severity of the associated disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
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".