Genital and systemic immune effects of the injectable, contraceptive norethisterone enanthate (NET‐EN), in South African women
Bibliographic record
Abstract
PROBLEM: Injectable hormonal contraceptives (IHC) have been associated with altered mucosal and systemic milieu which might increase HIV risk, but most studies have focused on DMPA and not NET-EN, despite the growing popularity and lower HIV risk associated with the latter in observational studies. METHOD OF STUDY: We used high-performance liquid chromatography in combination with tandem triple quadrupole mass spectrometry (HPLC-LC-MS/MS) to measure steroid hormones in plasma samples of CAPRISA004 study participants. Concentrations of 48 cytokines were measured in the cervicovaginal lavage (CVL) and plasma, and their expression was compared between participants with detectable NET-EN (n = 201) versus non-detectable IHC (n = 90). Each log10 cytokine concentration was tested as an outcome in linear-mixed models, with NET-EN detection as the main explanatory variable. Multivariable models were adjusted for potential confounders. RESULTS: In bivariate analysis, detectable NET-EN was associated with reduced cervicovaginal M-CSF (P = 0.008), GM-CSF (P = 0.025) and G-CSF (P = 0.039), and elevated levels MIF (P = 0.008), IL-18 (P = 0.011), RANTES (P = 0.005) and IL-1Rα (P < 0.001). Lower G-CSF (P = 0.011) and elevated IL-1Rα (P = 0.008) remained significant in adjusted models. Multivariable analyses of plasma samples obtained from NET-EN-detectable women showed a significant increase in IP-10 (P = 0.026) and reductions in TNF-β (P = 0.037), RANTES (P = 0.009), and M-CSF (P < 0.001). While similar growth factor reduction in CVL was noted for both DMPA and NET-EN, similar trends were not observed for endogenous progesterone. CONCLUSIONS: Detectable NET-EN was associated with reduced growth factors in the plasma and genital tract; particularly G-CSF and M-CSF. Our results suggest that while NET-EN is not inflammatory, it may have important immunological effects.
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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 | 0.001 |
| 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".