MHC-driven HIV-1 control on the long run is not systematically determined at early times post-HIV-1 infection
Bibliographic record
Abstract
INTRODUCTION: Human leukocyte antigen (HLA) class I-driven long-term protection against HIV-1 is mainly associated with HLA-B*27 and HLA-B*57. This effect is observed early after infection. Clarification needs to be established concerning the moment of action for the other HLA-B or HLA-C alleles. METHODS: HLA-B and HLA-C alleles from 111 individuals that control HIV-1 disease for over 8 years and from 747 seroconverters frequencies were compared. Also, HLA-B and HLA-C influence on early levels of plasma HIV-RNA, cellular HIV-DNA, CD4, CD8 and CD4/CD8 ratio was evaluated among the seroconverters. We performed univariate, multivariate and haplotypic analyses in order to disentangle the respective contribution of the HLA-B and HLA-C genes. RESULTS: The haplotypes analysis shows three patterns of protective effects of HLA-B and HLA-C alleles or haplotypes. First, the HLA B*57, HLA-B*27, HLA-B*13 and HLA-C*14 alleles, which have a strong effect on long-term disease control, also influence at least one of the early infection phenotypes. Second, HLA-B*52 has a strong effect during early time points on HIV-RNA without significant effect on the long-term control of HIV-1. Finally, the HLA-B*14-C*08 haplotype has a strong effect on the long-term protection, without influencing early viral control. CONCLUSION: Our study highlighted independent effects of HLA-B and HLA-C alleles on HIV-disease progression. Furthermore, some alleles appeared to be specifically associated with either long-term control or early virological parameters, suggesting different immunological mechanisms according to the disease stages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.092 |
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; both teacher heads agree on what is shown here.
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".