Semen-mediated enhancement of HIV infection is donor-dependent and correlates with the levels of SEVI
Bibliographic record
Abstract
BACKGROUND: HIV-1 is usually transmitted in the presence of semen. We have shown that semen boosts HIV-1 infection and contains fragments of prostatic acid phosphatase (PAP) forming amyloid aggregates termed SEVI (semen-derived enhancer of viral infection) that promote virion attachment to target cells. Despite its importance for the global spread of HIV-1, however, the effect of semen on virus infection is controversial. RESULTS: Here, we established methods allowing the meaningful analysis of semen by minimizing its cytotoxic effects and partly recapitulating the conditions encountered during sexual HIV-1 transmission. We show that semen rapidly and effectively enhances the infectivity of HIV-1, HIV-2, and SIV. This enhancement occurs independently of the viral genotype and coreceptor tropism as well as the virus producer and target cell type. Semen-mediated enhancement of HIV-1 infection was also observed under acidic pH conditions and in the presence of vaginal fluid. We further show that the potency of semen in boosting HIV-1 infection is donor dependent and correlates with the levels of SEVI. CONCLUSIONS: Our results show that semen strongly enhances the infectivity of HIV-1 and other primate lentiviruses and that SEVI contributes to this effect. Thus, SEVI may play an important role in the sexual transmission of HIV-1 and addition of SEVI inhibitors to microbicides may improve their 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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".