Clinical and evolutionary consequences of HIV adaptation to HLA
Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this review is to summarize recent advances in our understanding of HIV adaptation to human leukocyte antigen (HLA)-associated immune pressures and its relevance to HIV prevention and cure research. RECENT FINDINGS: Recent research has confirmed that HLA is a major driver of individual and population-level HIV evolution, that HIV strains are adapting to the immunogenetic profiles of the different human ethnic groups in which they circulate, and that HIV adaptation has substantial clinical and immunologic consequences. As such, adaptation represents a major challenge to HIV prevention and cure. At the same time, there are opportunities: Studies of HIV adaptation are revealing why certain HLA alleles are protective in some populations and not others; they are identifying immunogenic viral epitopes that harbor high mutational barriers to escape, and they may help illuminate novel, vaccine-relevant HIV epitopes in regions where circulating adaptation is extensive. Elucidation of HLA-driven adapted and nonadapted viral forms in different human populations and HIV subtypes also renders 'personalized' immunogen selection, as a component of HIV cure strategies, conceptually feasible. SUMMARY: Though adaptation represents a major challenge to HIV prevention and cure, achieving an in-depth understanding of this phenomenon can help move the design of such strategies forward.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".