HIV-1 Subtype C Gag-Specific T-Cell Responses in Relation to Human Leukocyte Antigens in a Diverse Population of HIV-Infected Ethiopians
Bibliographic record
Abstract
Knowledge of the most dominant T-cell epitopes in the context of the local human leukocyte antigen (HLA) background is a prerequisite for the development of an effective HIV vaccine. In 100 Ethiopian subjects, 16 different HLA-A, 23 HLA-B, and 12 HLA-C specificities were observed. Ninety-four percent of the population carried at least 1 of the 5 most common HLA-A and/or HLA-B specificities. HIV-specific T-cell responses were measured in 48 HIV-infected Ethiopian subjects representing a wide range of ethnicities in Ethiopia using the interferon (IFN)-gamma enzyme-linked immunospot (Elispot) assay and 49 clade C-specific synthetic Gag peptides. Fifty-eight percent of the HIV-positive study subjects showed T-cell responses directed to 1 or more HIV Gag peptides. Most Gag-specific responses were directed against the subset of peptides spanning Gag p24. The breadth of response ranged from 1 to 9 peptides, with most (78%) individuals showing detectable responses to <3 Gag peptides. The magnitude of HIV-specific T-cell responses was not associated with HIV viral load but correlated positively with CD4 T-cell counts. The most frequently targeted Gag peptides overlapped with those previously described for HIV-1 subtype C-infected southern Africans, and therefore can be used in a multiethnic vaccine.
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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 | 0.000 |
| 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) | 0.001 | 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".