Higher mucosal antibody concentrations in women with genital tract inflammation
Bibliographic record
Abstract
Inflammatory cytokines augment humoral responses by stimulating antibody production and inducing class-switching. In women, genital inflammation (GI) significantly modifies HIV risk. However, the impact of GI on mucosal antibodies remains undefined. We investigated the impact of GI, pre-HIV infection, on antibody isotypes and IgG subclasses in the female genital tract. Immunoglobulin (Ig) isotypes, IgG subclasses and 48 cytokines were measured prior to HIV infection in cervicovaginal lavages (CVL) from 66 HIV seroconverters (cases) and 66 matched HIV-uninfected women (controls) enrolled in the CAPRISA 004 and 008 1% tenofovir gel trials. Pre-HIV infection, cases had significantly higher genital IgM (4.13; IQR, 4.04-4.19) compared to controls (4.06; IQR, 3.90-4.20; p = 0.042). More than one-quarter of cases (27%) had GI compared to just over one-tenth (12%) in controls. Significantly higher IgG1, IgG3, IgG4 and IgM (all p < 0.05) were found in women stratified for GI compared to women without. Adjusted linear mixed models showed several pro-inflammatory, chemotactic, growth factors, and adaptive cytokines significantly correlated with higher titers of IgM, IgA and IgG subclasses (p < 0.05). The strong and significant positive correlations between mucosal antibodies and markers of GI suggest that GI may impact mucosal antibody profiles. These findings require further investigation to establish a plausible biological link between the local inflammatory milieu and its consequence on these genital antibodies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".