Metronidazole Treatment Failure and Persistent BV Lead to Increased Frequencies of Activated T- and Dendritic-Cell Subsets
Bibliographic record
Abstract
Metronidazole (MDZ) treatment failure and bacterial vaginosis (BV) recurrence rates are high among African women. This cohort study identified genital immune parameters associated with treatment response by comparing vaginal microbiota and immune cell frequencies in endocervical cytobrushes obtained from 32 South African women with symptomatic BV pre- and post-metronidazole treatment. Cervical T- and dendritic-cell subsets were phenotyped using multiparameter flow cytometry and the composition of vaginal microbial communities was characterized using 16S rRNA gene sequencing. MDZ treatment led to a modest decrease in the relative abundance of BV-associated bacteria, but colonization with Lactobacillus species (other than L. iners) was rare. At 6 and 12 weeks, MDZ-treated women had a significant increase in the frequencies of CCR5+ CD4+ T cells and plasmacytoid dendritic cells compared to the pre-treatment timepoint. In addition, MDZ non-responders had significantly higher frequencies of activated CD4 T cells and monocytes compared to MDZ responders. We conclude that MDZ treatment failure was characterized by an increased expression of activated T- and dendritic-cell subsets that may enhance HIV susceptibility. These data suggest the need to further assess the long-term impact of MDZ treatment on mucosal immune response and the vaginal microbiota.
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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 | 0.000 | 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) | 0.002 | 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".