Genital Inflammation and the Risk of HIV Acquisition in Women
Bibliographic record
Abstract
BACKGROUND: Women in Africa, especially young women, have very high human immunodeficiency virus (HIV) incidence rates that cannot be fully explained by behavioral risks. We investigated whether genital inflammation influenced HIV acquisition in this group. METHODS: Twelve selected cytokines, including 9 inflammatory cytokines and chemokines (interleukin [IL]-1α, IL-1β, IL-6, tumor necrosis factor-α, IL-8, interferon-γ inducible protein-10 [IP-10], monocyte chemoattractant protein-1, macrophage inflammatory protein [MIP]-1α, MIP-1β), hematopoietic IL-7, and granulocyte macrophage colony-stimulating factor, and regulatory IL-10 were measured prior to HIV infection in cervicovaginal lavages from 58 HIV seroconverters and 58 matched uninfected controls and in plasma from a subset of 107 of these women from the Centre for the AIDS Programme of Research in South Africa 004 tenofovir gel trial. RESULTS: HIV seroconversion was associated with raised genital inflammatory cytokines (including chemokines MIP-1α, MIP-1β, and IP-10). The risk of HIV acquisition was significantly higher in women with evidence of genital inflammation, defined by at least 5 of 9 inflammatory cytokines being raised (odds ratio, 3.2; 95% confidence interval, 1.3-7.9; P = .014). Genital cytokine concentrations were persistently raised (for about 1 year before infection), with no readily identifiable cause despite extensive investigation of several potential factors, including sexually transmitted infections and systemic cytokines. CONCLUSIONS: Elevated genital concentrations of HIV target cell-recruiting chemokines and a genital inflammatory profile contributes to the high risk of HIV acquisition in these African 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 | 0.000 | 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".