Optimizing the vaginal microbiome as a potential strategy to reduce heterosexual HIV transmission
Bibliographic record
Abstract
Abstract Bacterial vaginosis (BV) is a proinflammatory genital condition characterized by high vaginal bacterial diversity and a paucity of Lactobacillus species. BV has been linked to an elevated risk of HIV acquisition among HIV‐negative women and of forward HIV transmission to male sex partners among women living with HIV (adjusted hazard ratios of 1.69 and 3.17, respectively), potentially by eliciting genital inflammation in women with BV and their male sex partners. BV is also highly prevalent among women in sub‐Saharan Africa, suggesting that BV treatment may have potential as an HIV prevention strategy. BV is typically treated with antibiotics but recurrence rates are high, possibly because treatment does not directly promote Lactobacillus growth. More recently, BV treatment strategies incorporating live biotherapeutic lactobacilli have led to sustained optimization of the vaginal microbiome and a decrease in inflammatory biomarkers previously associated with HIV susceptibility. Future studies are urgently needed to evaluate BV treatment strategies that can optimize the vaginal microbiome in the long term through colonization with H2O2‐producing vaginal lactobacilli and to assess whether vaginal microbiota optimization is able to reduce the risk of HIV transmission.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".