Quantitative profiling of the vaginal microbiota improves resolution of the microbiota-immune axis
Bibliographic record
Abstract
BACKGROUND: The composition of the vaginal microbiota is closely linked to adverse sexual and reproductive health outcomes, due in part to effects on genital immunology. Compositional approaches such as metagenomic sequencing provide a snapshot of all bacteria in a sample and have become the standard for characterizing the vaginal microbiota, but only provide microbial relative abundances. We hypothesized that the addition of absolute abundance data would provide a more complete picture of host-microbe interactions in the female genital tract. RESULTS: We analyzed cervicovaginal secretions from 196 female sex workers in Kenya and found that bacterial load was elevated among women with diverse, bacterial vaginosis (BV)-type microbiota and lower among women with Lactobacillus predominance. Bacterial load was also positively associated with proinflammatory cytokines, such as IL-1α, and negatively associated with chemokines, such as IP-10. The associations between bacterial load and immune factors differed across bacterial community states, but L. crispatus predominance was the only microbial community where higher bacterial load was not associated with higher proinflammatory cytokines. Total vaginal bacterial load was also a stronger predictor of the genital immune environment than BV by Nugent score, the current clinical standard, in the Kenya-based cohort and in a Uganda-based confirmatory cohort. CONCLUSIONS: Our results suggest that total vaginal bacterial load is at least as strong a predictor of the genital immune milieu as current BV clinical diagnostic tools, supporting exploration of the vaginal bacterial load as a predictor of adverse reproductive and sexual health outcomes. Video Abstract.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".