A novel vaccine targeting the viral protease cleavage sites protects Mauritian cynomolgus macaques against vaginal SIVmac251 infection
Bibliographic record
Abstract
Abstract After over three decades of research, an effective anti-HIV vaccine remains elusive. Unconventional and novel vaccine strategies are needed. Here, we report that a vaccine focusing the immune response on the sequences surrounding the 12 viral protease cleavage sites (PCSs) provides greater than 80% protection of Mauritian cynomolgus macaques (MCMs) against repeated intravaginal SIVmac251 challenges. The PCS-specific T cell responses are correlated with vaccine efficacy. The PCS vaccine does not induce immune activation and inflammation known to be associated with increased susceptibility to HIV infection. Machine learning analyses revealed that the immune environment generated by the PCS vaccine predicts vaccine efficacy. Our study demonstrates for the first time that a novel vaccine which targets viral maturation, but lacks full Env and Gag proteins as immunogens, can prevent intravaginal infection in a highly stringent NHP/SIV challenge model. Targeting HIV maturation thus offers a novel approach to developing an effective HIV vaccine. One Sentence Summary The anti-PCS T cell responses and the immune environment induced by the novel PCS vaccine are key correlates of vaccine efficacy
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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 | 0.000 |
| 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) | 0.001 | 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".