The HLA-C*04
Bibliographic record
Abstract
OBJECTIVE: The objective of this study is to identify human leukocyte antigen (HLA) class I and killer-cell immunoglobulin-like receptor (KIR) genotypes associated with different risks for HIV acquisition and HIV disease progression. DESIGN: A cross-sectional study of a cohort of 468 high-risk individuals (246 HIV-positive and 222 HIV-negative) from outpatient clinics in Lima (Perú). METHODS: The cohort was high-resolution HLA and KIR-typed and analysed for potential differences in single-allele frequencies and allele combinations between HIV-positive and HIV-negative individuals and for associations with HIV viral load and CD4 cell counts in infected individuals. RESULTS: HLA class I alleles associated with a lack of viral control had a significantly higher population frequency than relatively protective alleles (P = 0.0093), in line with a rare allele advantage. HLA-A02 : 01 and HLA-C04 : 01 were both associated with high viral loads (P = 0.0313 and 0.0001, respectively) and low CD4 cell counts (P = 0.0008 and 0.0087, respectively). Importantly, the association between HLA-C04 : 01 and poor viral control was not due to its linkage disequilibrium with other HLA alleles. Rather, the coexpression of its putative KIR ligand KIR2DS4f was critically linked to elevated viral loads. CONCLUSION: These results highlight the impact of population allele frequency on viral control and identify a novel association between HLA-C04 : 01 in combination with KIR2DS4f and uncontrolled HIV infection. Our data further support the importance of the interplay of markers of the adaptive and innate immune system in viral control.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".