Bibliographic record
Abstract
Bacterial vaginosis (BV) is a vaginal condition characterized by a diverse vaginal microbiota and a paucity of lactobacilli and is believed to elevate HIV risk among women by eliciting genital inflammation. Although optimization of the vaginal microbiota has been proposed as an HIV prevention strategy, the impact of vaginal microbiota-targeting therapies on genital immune correlates of HIV risk must be defined in greater detail. First, I demonstrated that standard antibiotic treatment for BV resulted in immediate reductions in proinflammatory cytokines and epithelial disruption. These immune effects were accompanied by reductions in the absolute abundance of BV-associated bacteria but little change in lactobacilli, supporting a causal role for BV-associated bacteria in driving these immune effects. By measuring vaginal soluble immune factors and the vaginal microbiota prior to BV treatment, I explored predictors of BV treatment response in US- and Kenya-based cohorts. Although there were no consistent predictors of treatment response in either cohort, I observed a greater reduction in G. vaginalis absolute abundance immediately following treatment among treatment responders compared to non-responders in the US-based cohort. Next, I evaluated the long-term impact of LACTIN-V, an L. crispatus-based live biotherapeutic, on genital immunology and the vaginal microbiota. LACTIN-V was associated with sustained reductions in genital inflammation and epithelial disruption and increased levels of the chemokine IP-10. These immune effects were driven by elevated L. crispatus and reduced BV-associated bacteria in the LACTIN-V group, although increased chemokine levels (which may enhance HIV risk) were not linked to the L. crispatus CTV-05 strain that is present in LACTIN-V. Lastly, I explored predictors and genital immune implications of vaginal colonization of L. crispatus CTV-05 among women who received LACTIN-V. Elevated vaginal microbiota diversity prior to LACTIN-V administration was associated with resistance to CTV-05 colonization. Colonization resistance was associated with distinct genital immune and microbial profiles during LACTIN-V administration compared to colonization permissive women. My thesis demonstrates that therapeutic induction of an L. crispatus-predominant vaginal microbiota may be a potential strategy to reduce HIV risk among women and further work is needed to directly evaluate the impact of LACTIN-V and similar strategies on HIV incidence.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".