The vaginal microbiome amplifies sex hormone‐associated cyclic changes in cervicovaginal inflammation and epithelial barrier disruption
Bibliographic record
Abstract
PROBLEM: Susceptibility to HIV is associated with the menstrual cycle and vaginal microbiome, but their collective impact on vaginal inflammation remains unclear. Here, we characterized the cervicovaginal proteome, inflammation, and microbiome community structure and function during the menstrual cycle. METHOD OF STUDY: Cervicovaginal secretions were collected from regularly cycling women (n = 16) at median day 10, 16, and 24 of each menstrual cycle and analyzed by mass spectrometry, 16S rRNA gene sequencing, and a multiplex bead array immunoassay. Follicular, ovulatory, and luteal phases were defined by serum sex hormone levels. RESULTS: Ovulation showed the largest mucosal proteome changes, where 30% and 19% of the 406 human proteins identified differed compared to the luteal and follicular phases, respectively. Neutrophil/leukocyte migration pathways were lowest during ovulation and peaked in the luteal phase, while antimicrobial and epithelial barrier promoting proteins were highest during ovulation. Vaginal microbial community structure and function did not vary significantly during the menstrual cycle, with the majority consistently Lactobacillus-dominant (63%) or non-Lactobacillus-dominant (25%). Fluctuations in the epithelial barrier protein RPTN between the ovulatory and luteal phase were amplified in women with Gardnerella vaginalis and anaerobic bacteria and reduced when Lactobacillus was dominant. CONCLUSION: This small study demonstrates that sex hormones modulate neutrophil/leukocyte inflammation, barrier function, and antimicrobial pathways in the female genital tract with the strongest changes occurring during ovulation. The data further suggest a microbiome context for hormone-driven changes in vaginal immunity which may have implications for HIV susceptibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".