Vaginal microbiota associated with oncogenic HPV in a cohort of HPV-vaccinated women living with HIV
Bibliographic record
Abstract
BACKGROUND: Women living with HIV (WLWH) experience higher rates of human papillomavirus (HPV) infection and cervical cancer than women without HIV. Changes in the vaginal microbiome have been implicated in HPV-related disease processes such as persistence of high-risk HPV infection but this has not been well defined in a population living with HIV. METHODS: Four hundred and 20 girls and WLWH, age ≥9, across 14 clinical sites in Canada were enrolled to receive three doses of quadrivalent HPV vaccine for assessment of vaccine immunogenicity. Blood, cervical cytology, and cervico-vaginal swabs were collected. Cervico-vaginal samples were tested for HPV DNA and underwent microbiota sequencing. RESULTS: Principal component analysis (PCA) and hierarchical clustering generated community state types (CSTs). Relationships between taxa and CSTs with HPV infection were examined using mixed-effects logistic regressions, Poisson regressions, or generalized linear mixed-effects models, as appropriate. Three hundred and fifty-six cervico-vaginal microbiota samples from 172 women were sequenced. Human papillomavirus DNA was detected in 211 (59%) samples; 110 (31%) contained oncogenic HPV. Sixty-five samples (18%) were taken concurrently with incident oncogenic HPV infection and 56 (16%) were collected from women with concurrent persistent oncogenic HPV infection. CONCLUSIONS: species, with incident HPV infection.
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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 | 0.001 | 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) | 0.001 | 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".