Nonoptimal bacteria species induce neutrophil-driven inflammation and barrier disruption in the female genital tract
Bibliographic record
Abstract
Neutrophil recruitment and activation within the female genital tract are often associated with tissue inflammation, loss of vaginal epithelial barrier integrity, and increased risk for sexually transmitted infections, such as HIV-1. However, the direct role of neutrophils on vaginal epithelial barrier function during genital inflammation in vivo remains unclear. Using complementary proteome and immunological analyses, we show high neutrophil influx into the lower female genital tract in response to physiological surges in progesterone, stimulating distinct stromal, immunological, and metabolic signaling pathways. However, despite the release of extracellular matrix-modifying proteases and inflammatory mediators, neutrophils contributed little to physiological mucosal remodeling events such as epithelial shedding or re-epithelialization during transition from diestrus to estrus phase. In contrast, the presence of bacterial vaginosis-associated bacteria resulted in a rapid and sustained neutrophil recruitment, resulting in vaginal epithelial barrier leakage and decreased cell-cell junction protein expression in vivo. Thus, neutrophils are important mucosal sentinels that rapidly respond to various biological cues within the female genital tract, dictating the magnitude and duration of the ensuing inflammatory response at steady state and during disease processes.
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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 | 0.000 |
| 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 | 0.001 |
| 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".