Generation of a Dual-Target, Safe, Inexpensive Microbicide that Protects Against HIV-1 and HSV-2 Disease
Bibliographic record
Abstract
HSV-2 infection is a significant health problem and a major co-morbidity factor for HIV-1 acquisition, increasing risk of infection 2-4 fold. Condom based prevention strategies for HSV-2 and HIV-1 have not been effective at stopping the HIV-1 pandemic, indicating that alternative prevention strategies need to be investigated. We have previously developed an inexpensive HIV-1 specific microbicide that utilizes the S-layer mediated display capabilities of Caulobacter crescentus, and have shown that recombinant C. crescentus displaying HIV entry blocking proteins are able to provide significant protection from HIV-1 infection in vitro. Here we demonstrate that recombinant C. crescentus are safe for topical application and describe 5 new recombinant C. crescentus that provide protection from HIV-1 infection in vitro. Further, we demonstrate protection from disease following intravaginal infection with HSV-2 in a murine model using C. crescentus expressing the anti-viral lectins Cyanovirin-N and Griffithsin, as well as α-1-antitrypsin and indolicidin. Interestingly, C. crescentus alone significantly reduced HSV-2 replication in vaginal lavage fluid. Protection from HSV-2 disease was strongly associated with early cytokine production in the vaginal tract. Our data support the potential for a dual-target microbicide that can protect against both HIV-1 and HSV-2, which could have an enormous impact on public health.
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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".