Endocervical Regulatory T Cells Are Associated With Decreased Genital Inflammation and Lower HIV Target Cell Abundance
Bibliographic record
Abstract
Regulatory T cells (Tregs) play important roles in tissue homeostasis, but few studies have investigated tissue Tregs in the context of genital inflammation, HIV target cell density, and vaginal microbiota in humans. In women from Nairobi (n=64), the proportion of CD4+ CD25+ CD127 low Tregs in the endocervix correlated with those in blood (r=0.31, p=0.01), with a higher Treg frequency observed in the endocervix (median 3.8 vs 2.0%, p<0.0001). Most Tregs expressed FOXP3 in both compartments, and CTLA-4 expression was higher on endocervical Tregs compared to blood (median 50.8 vs 6.0%, p<0.0001). More than half (34/62, 55%) of participants displayed a non- Lactobacillus dominant vaginal microbiota, which was not associated with endocervical Tregs or CD4+ T cell abundance. In a multivariable linear regression, endocervical Treg proportions were inversely associated with the number of elevated pro-inflammatory cytokines (p=0.03). Inverse Treg associations were also observed for specific cytokines including IL-1β, G-CSF, Eotaxin, IL-1RA, IL-8, and MIP-1 β. Higher endocervical Treg proportions were associated with lower abundance of endocervical CD4+ T cells (0.30 log 10 CD4+ T cells per log 10 Treg, p=0.00028), with a similar trend for Th17 cells (p=0.09). Selectively increasing endocervical Tregs may represent a pathway to reduce genital tract inflammation in women.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".