The Impact of Sex Work Interruption on Blood-Derived T Cells in Sex Workers from Nairobi, Kenya
Bibliographic record
Abstract
BACKGROUND: Unprotected sexual intercourse exposes the female genital tract (FGT) to semen-derived antigens, which leads to a proinflammatory response. Studies have shown that this postcoital inflammatory response can lead to recruitment of activated T cells to the FGT, thereby increasing risk of HIV infection. OBJECTIVE: The purpose of this study was to evaluate the impact of sex work on activation and memory phenotypes of peripheral T cells among female sex workers (FSW) from Nairobi, Kenya. SUBJECTS: Thirty FSW were recruited from the Pumwani Sex Workers Cohort, 10 in each of the following groups: HIV-exposed seronegative (at least 7 years in active sex work), HIV positive, and New Negative (HIV negative, less than 3 years in active sex work). Blood was obtained at three different phases (active sex work, abstinence from sex work-sex break, and following resumption of sex work). Peripheral blood mononuclear cells were isolated and stained for phenotypic markers (CD3, CD4, CD8, and CD161), memory phenotype markers (CD45RA and CCR7), activation markers (CD69, HLA-DR, and CD95), and the HIV coreceptor (CCR5). T-cell populations were compared between groups. RESULTS: In HIV-positive women, CD8+CCR5+ T cells declined at the sex break period, while CD4+CD161+ T cells increased when returning to sex work. All groups showed no significant changes in systemic T-cell activation markers following the interruption of sex work, however, significant reductions in naive CD8+ T cells were noted. For each of the study points, HIV positives had higher effector memory and CD8+CD95+ T cells and lower naive CD8+ T cells than the HIV-uninfected groups. CONCLUSIONS: Interruption of sex work had subtle effects on systemic T-cell memory phenotypes.
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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 | 0.001 | 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".