Bacterial vaginosis, HIV serostatus and T-cell subset distribution in a cohort of East African commercial sex workers
Bibliographic record
Abstract
OBJECTIVE: Although bacterial vaginosis is a known correlate of HIV infection, no previous studies have investigated whether women defined as HIV-exposed seronegative (HESN) are less likely to have bacterial vaginosis. Little is known about the effects of bacterial vaginosis on systemic immune activation associated with HIV+ serostatus. DESIGN: Cohort-based retrospective analysis of bacterial vaginosis in relation to HESN status, HIV+ serostatus and peripheral T-helper cells, with cross-sectional analysis of bacterial vaginosis in relation to peripheral T-regulatory cells (Tregs). METHODS: Bacterial vaginosis diagnosis by Gram stain and determination of systemic CD4(+) and CD8(+) T-helper cell frequency by flow cytometry for 3504 vaginal samples from 988 commercial sex workers over 4 years. Treg phenotyping by FoxP3 staining and multiparameter flow cytometry in peripheral blood of 97 women at a single time-point. RESULTS: No differences in bacterial vaginosis diagnosis were observed between HESN and other HIV-negative (HIV-N) controls; however, HIV+ women were more likely to be diagnosed with bacterial vaginosis compared to all HIV-negative women (HESN/HIV-N combined). HIV+ women with bacterial vaginosis had significantly higher CD4(+)/CD8(+) T-helper cell counts and a lower CD4/CD8 ratio, as well as fewer Tregs as a proportion of total T-helper cells, compared to bacterial vaginosis-negative women. The number of bacterial vaginosis diagnoses in this cohort has decreased significantly over time. CONCLUSION: Bacterial vaginosis is associated with HIV serostatus and shifts in distribution of T-cell subsets. A concomitant reduction in bacterial vaginosis and HIV infections over time suggests that the elucidation of bacterial vaginosis-HIV interactions will be critical to further understanding of HIV pathogenesis and prevention in this high-risk group.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".