Vaccine targeting SIVmac251 protease cleavage sites protects macaques against vaginal infection
Bibliographic record
Abstract
After over 3 decades of research, an effective anti-HIV vaccine remains elusive. The recently halted HVTN702 clinical trial not only further stresses the challenge to develop an effective HIV vaccine but also emphasizes that unconventional and novel vaccine strategies are urgently needed. Here, we report that a vaccine focusing the immune response on the sequences surrounding the 12 viral protease cleavage sites (PCSs) provided greater than 80% protection to Mauritian cynomolgus macaques against repeated intravaginal SIVmac251 challenges. The PCS-specific T cell responses correlated with vaccine efficacy. The PCS vaccine did not induce immune activation or inflammation known to be associated with increased susceptibility to HIV infection. Machine learning analyses revealed that the immune microenvironment generated by the PCS vaccine was predictive of vaccine efficacy. Our study demonstrates, for the first time to our knowledge, that a vaccine which targets only viral maturation, but lacks full-length Env and Gag immunogens, can prevent intravaginal infection in a stringent macaque/SIV challenge model. Targeting HIV maturation thus offers a potentially novel approach to developing an effective HIV 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 | 0.000 |
| 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".