Levels of innate immune factors in genital fluids: association of alpha defensins and LL-37 with genital infections and increased HIV acquisition
Bibliographic record
Abstract
BACKGROUND: Several mucosal innate immune proteins exhibit HIV inhibitory activity and their analogues are potential microbicide candidates. However, their clinical associations and in-vivo role in cervicovaginal host defense against HIV acquisition are poorly defined. METHODS: Cervicovaginal secretions (CVSs) were collected from HIV uninfected Kenyan sex workers at enrolment into an HIV prevention trial. After trial completion, CVS from participants acquiring HIV (cases) and matched controls were assessed for levels of innate immune factors and HIV neutralizing capacity, by blinded investigators. Cross-sectional and prospective associations of innate immune factors were examined. RESULTS: CVS contained high levels of defensins (human neutrophil peptide-1-3 and human beta defensin-2-3), LL-37 and secretory leukocyte protease inhibitor. Regulated upon activation normal T-cell expressed and secreted levels were lower, and IFNalpha was undetectable. CVS from 20% of participants neutralized a clade A primary HIV isolate, and 12% neutralized both clade A and C isolates. HIV neutralization was correlated with human neutrophil peptide-1-3 (alpha-defensins) and LL-37 levels. However, alpha-defensin and LL-37 levels were increased in participants with bacterial sexually transmitted infections and were independently associated with increased HIV acquisition in multivariate analysis. CONCLUSIONS: Despite significant HIV inhibitory activity, cervicovaginal levels of alpha-defensins and LL-37 were associated with increased HIV acquisition, perhaps due to their association with bacterial sexually transmitted infections.
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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) | 0.002 | 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".