Major Histocompatibility Complex Class II (HLA-DRB and -DQB) Allele Frequencies in Botswana: Association with Human Immunodeficiency Virus Type 1 Infection
Bibliographic record
Abstract
Southern Africa is facing an unprecedented public health crisis due to the high prevalence of human immunodeficiency virus type 1 (HIV-1). Vaccine development and testing efforts, mainly based on elicitation of HIV-specific T cells, are under way. To understand the role of human leukocyte antigen (HLA) class II alleles in HIV pathogenesis and to facilitate HLA-based HIV-1 vaccine design, we analyzed the frequencies of HLA class II alleles within the southern African country of Botswana. Common HLA class II alleles were identified within the Botswana population through the molecular genotyping of DRB and DQB1 loci. The DRB1 allele groups DRB1*01, DRB1*02/15, DRB1*03, DRB1*11, and DRB1*13 were encountered at frequencies above 20%. Within the DQB1 locus, DQB1*06 (47.7%) was the most common allele group, followed by DQB1*03 (39.2%) and DQB1*04 (25.8%). We found that DRB1*01 was more common in HIV-negative than in HIV-positive individuals and that those who expressed DRB1*08 had lower median viral loads. We demonstrate that the frequencies of certain HLA class II alleles in this Botswana population differ substantially from those in North American populations, including African-Americans. Common allele groups within Botswana cover large percentages of other African populations and could be targeted in regional vaccine designs.
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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) | 0.001 | 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".