Sex Work Is Associated With Increased Vaginal Microbiome Diversity in Young Women From Mombasa, Kenya
Bibliographic record
Abstract
BACKGROUND: Although nonoptimal vaginal bacteria and inflammation have been associated with increased HIV risk, the upstream drivers of these phenotypes are poorly defined in young African women. SETTING: Mombasa, Kenya. METHODS: We characterized vaginal microbiome and cytokine profiles of sexually active young women aged 14-24 years (n = 168) in 3 study groups: those engaging in formal sex work, in transactional sex, and nonsex workers. Vaginal secretions were collected using self-inserted SoftCup, and assayed for cytokines and vaginal microbiome through multiplex ELISA and 16S rRNA sequencing, respectively. Epidemiological data were captured using a validated questionnaire. RESULTS: The median age of participants was 20 years (interquartile range: 18-22 years). Approximately two-thirds of young women (105/168) had vaginal microbial communities characterized by Gardnerella and/or Prevotella spp. dominance; a further 29% (49/168) were predominantly Lactobacillus iners. Microbiome clustering explained a large proportion of cytokine variation (>50% by the first 2 principal components). Age was not associated with vaginal microbial profiles in bivariable or multivariable analyses. Women self-identifying as sex workers had increased alpha (intraindividual) diversity, independent of age, recent sexual activity, HIV, and other sexually transmitted infections (beta = 0.47, 95% confidence interval: 0.05 to 0.90, P = 0.03). Recent sex (number of partners or sex acts last week, time since last vaginal sex) correlated with increased alpha diversity, particularly in participants who were not involved in sex work. CONCLUSION: Nonoptimal vaginal microbiomes were common in young Kenyan women and associated with sex work and recent sexual activity, but independent of age. Restoring optimal vaginal microflora may represent a useful HIV prevention strategy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".