Kinetics of HIV-1 CTL Epitopes Recognized by HLA I Alleles in HIV-Infected Individuals at Times near Primary Infection: The Provir/Latitude45 Study
Bibliographic record
Abstract
In patients responding successfully to ART, the next therapeutic step is viral cure. An interesting strategy is antiviral vaccination, particularly involving CD8 T cell epitopes. However, attempts at vaccination are dependent on the immunogenetic background of individuals. The Provir/Latitude 45 project aims to investigate which CTL epitopes in proviral HIV-1 will be recognized by the immune system when HLA alleles are taken into consideration. A prior study (Papuchon et al, PLoS ONE 2013) showed that chronically-infected patients under successful ART exhibited variations of proviral CTL epitopes compared to a reference viral strain (HXB2) and that a generic vaccine may not be efficient. Here, we investigated viral and/or proviral CTL epitopes at different time points in recently infected individuals of the Canadian primary HIV infection cohort and assessed the affinity of these epitopes for HLA alleles during the study period. An analysis of the results confirms that it is not possible to fully predict which epitopes will be recognized by the HLA alleles of the patients if the reference sequences and epitopes are taken as the basis of simulation. Epitopes may be seen to vary in circulating RNA and proviral DNA. Despite this confirmation, the overall variability of the epitopes was low in these patients who are temporally close to primary infection.
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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 | 0.000 |
| 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".