Beyond bacterial vaginosis: vaginal lactobacilli and HIV risk
Bibliographic record
Abstract
HIV incidence continues to be unacceptably high in Eastern and Southern Africa, with women disproportionately affected. An increased per-contact risk of HIV acquisition among African, Caribbean, and other Black (ACB) women has been associated with the higher prevalence of bacterial vaginosis (BV) in these communities, wherein the vaginal microbiota is predominated by diverse pro-inflammatory anaerobic bacteria. However, while the vaginal microbiota in BV-free women is typically predominated by one of several different Lactobacillus spp., the degree of HIV protection afforded by a Lactobacillus-predominant vaginal microbiota also varies considerably. Specifically, L. crispatus is associated with an immunoregulatory genital immune environment, exclusion of BV-associated bacteria, and reduced HIV risk. In contrast, less HIV protection or exclusion of BV-associated bacteria and fewer immune benefits have been associated with L. iners-which is unfortunately the most common Lactobacillus species among ACB women. These species-specific clinical differences are underpinned by substantial genomic differences between Lactobacillus species: for instance, the much smaller genome of L. iners lacks the coding sequence for D-lactic acid dehydrogenase and cannot produce the D-lactate isomer that enhances HIV trapping in mucus but encodes for epithelial cell toxins and stress resistance proteins that may enhance bacterial survival in the context of microbiota and environmental fluctuations. While more studies are needed to elucidate whether differences in HIV protection between Lactobacillus species are due to direct genital immune effects or the exclusion of proinflammatory BV-associated bacteria, the current body of work suggests that for BV treatment to succeed as an HIV prevention strategy, it may be necessary to induce a vaginal microbiota that is predominated by specific (non-iners) Lactobacillus species. Video abstract.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".