Metronidazole treatment rapidly reduces genital inflammation through effects on bacterial vaginosis-associated bacteria rather than lactobacilli
Bibliographic record
Abstract
Background Bacterial vaginosis (BV) causes genital inflammation and increases HIV risk, whereas a vaginal microbiota dominated by Lactobacillus species is associated with immune quiescence and relative HIV protection. BV treatment reduces genital inflammation, but it is unclear whether this reduction is driven by a decrease in BV-associated bacteria or an increase in Lactobacillus species.METHODS To evaluate the short-term effect of standard BV treatment on genital immunology and the vaginal microbiota, vaginal swabs were collected immediately before and after metronidazole treatment for BV and analyzed with multiplex ELISA, metagenomic sequencing, and quantitative PCR.RESULTS Topical metronidazole treatment rapidly reduced vaginal levels of proinflammatory cytokines, chemokines, and soluble immune markers of epithelial barrier disruption. Although the vaginal microbiota shifted to dominance by L. iners or L. jensenii, this proportional shift was primarily driven by a 2 to 4 log10–fold reduction in BV-associated bacteria absolute abundance. BV treatment induced no change in the absolute abundance of L. crispatus or L. iners and only minor (<1 log10–fold) increases in L. gasseri and L. jensenii that were not independently associated with reduced inflammation in multivariable models.CONCLUSION The genital immune benefits that are associated with Lactobacillus dominance after BV treatment were not directly attributable to an absolute increase in lactobacilli, but rather to the loss of BV-associated bacteria.Trial REGISTRATION Participants were recruited as part of a randomized controlled trial (ClinicalTrials.gov NCT02766023) from 2016 to 2019.FUNDING Canadian Institutes of Health Research (PJT-156123) and the National Institute of Allergy and Infectious Diseases (HHSN2722013000141 and HHSN27200007).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".