Metronidazole treatment rapidly reduces genital inflammation through effects on bacterial vaginosis–associated bacteria rather than lactobacilli
Bibliographic record
Abstract
BackgroundBacterial 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.METHODSTo 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.RESULTSTopical 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.CONCLUSIONThe 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 REGISTRATIONParticipants were recruited as part of a randomized controlled trial (ClinicalTrials.gov NCT02766023) from 2016 to 2019.FUNDINGCanadian 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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 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".