Cytokine concentration and T cell subsets in the female genital tract in the presence of bacterial vaginosis and Trichomonas vaginalis
Bibliographic record
Abstract
(TV) and bacterial vaginosis (BV) are highly prevalent vaginal infections. Both are associated with pelvic inflammatory disease and HIV acquisition and transmission, though the underlying mechanisms are incompletely understood. We characterized the effect of TV and BV infection on inflammatory markers in the vagina among reproductive-aged women in Atlanta, Georgia. Cervicovaginal lavage specimens were collected from HIV seronegative women at a baseline visit and again three months later. Eighteen individual cytokines, 17 T cell subsets, BV, and TV were measured at both timepoints. After natural log transformation, the median cytokine concentration and number of T cells were compared by infection status statistically using the Kruskal-Wallis test. A cytokine inflammation score and a T cell score were created using principal components analysis. The scores were then used as outcomes in separate linear mixed regression models with a random intercept. Sixty women had baseline data and 43 were seen for follow-up. The median age was 30 years, 78% self-reported Black race. TV and BV prevalence at the baseline visit was 15% and 37%, respectively. The concentration of 16 out of 18 cytokines differed by infection status. In multivariable modeling, neither TV nor BV were associated with cytokine score. Most CD4+ T cell subsets (7 out of 9) differed by infection status. In a multivariable model, TV infection was associated with a higher T cell score (1.54; 95%CI 0.00, 3.08). BV was not associated with a higher T cell score. Increased concentration of vaginal mucosal T cells may explain the observed association between TV infection and HIV risk.
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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 | 0.002 |
| 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) | 0.001 | 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 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".