Recent sexual violence exposure is associated with immune biomarkers of HIV susceptibility in women
Bibliographic record
Abstract
PROBLEM: HIV/AIDS and sexual violence act synergistically and compromise women's health. Yet, immuno-biological mechanisms linking sexual violence and increased HIV susceptibility are poorly understood. METHODS: We conducted a cross-sectional pilot study of HIV-uninfected women, comparing 13 women exposed to forced vaginal penetration within the past 12 weeks (Exposed) with 25 Non-Exposed women. ELISA assays were conducted for 49 biomarkers associated with HIV pathogenesis in plasma and cervicovaginal lavage (CVL). Differences between Exposed and Non-Exposed were analyzed by linear and logistic regression, using propensity score weighting to control for age, race, socioeconomic status, menstrual cycle, and contraceptive use. RESULTS: In CVL, Exposed women had significantly reduced chemokines MIP-3α (p < .01), MCP-1 (p < .01), and anti-HIV/wound-healing thrombospondin-1 (p = .03). They also had significantly increased inflammatory cytokine IL-1α (p < 0.01) and were more likely to have detectable wound-healing PDGF (p = .02). In plasma, Exposed women had reduced chemokines MIP-3α (p < .01) and IL-8 (p < .01), anti-inflammatory cytokine TGF-β (p = .02), anti-HIV/antimicrobial HBD-2 (p = .02), and wound-healing MMP-1 (p = 0.02). They also had increased thrombospondin-1 (p < .01) and Cathepsin B (p = .01). After applying the stringent method of false discovery rate adjustment, differences for IL-1α (p = .05) and MCP-1 (p = .03) in CVL and MIP-3α (p = .03) in plasma remained significant. CONCLUSIONS: We report systemic and mucosal immune dysregulation in women exposed to sexual violence. As these biomarkers have been associated with HIV pathogenesis, dysregulation may increase HIV susceptibility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".