Increased cervical neutrophil survival during bacterial vaginosis in Canadian women from the THRIVE study
Bibliographic record
Abstract
Abstract Introduction Bacterial vaginosis (BV), characterized by Lactobacillus depletion and replacement by facultative or anaerobic bacteria, impacts up to 30% of women and associates with negative reproductive health outcomes. We used a systems immunology approach to investigate cellular and molecular inflammation associated with BV. Methods Matched cervical cytobrush and cervicovaginal lavage were collected from BV− (n=16) and BV+ (n=11) women. Cytobrushes were immunophenotyped by flow cytometry and lavages were analyzed by tandem mass spectrometry. Differences in immune cell levels were evaluated with Mann-Whitney U tests, and assessed against host and bacterial proteins using Spearman’s Rank correlations. Results There were no clinical or demographic differences between BV+ and BV− women, including age (range 22–49), birth control use and STI history. Proteomic analysis identified 1085 human proteins and 516 bacterial proteins from 14 genera, including Lactobacillus, Gardnerella, and Mobiluncus. Long living neutrophils (CD49d+) were significantly increased in BV+ women (p=0.0005), and associated with cell adhesion (p=4.04E-5), T cell mediated immunity (p=0.004), and regulation of myeloid differentiation (p=8.63E-16) pathways. CD49+ neutrophils correlated positively with Mobiluncus (p=0.040, R2=0.49) and negatively with Lactobacillus (p=0.035, R2=−0.498). Discussion This data indicates that cervical neutrophil survival is increased in women with BV. These cells associated with anaerobic bacteria and molecular pathways of immune activation. Studies are ongoing to delineate the relationship between BV-associated bacteria and neutrophil functionality, which may have implications for BV treatment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".