Local oestrogen therapy modulates extracellular matrix and immune response in the vaginal tissue of post‐menopausal women with severe pelvic organ prolapse
Bibliographic record
Abstract
This study investigates the effect of local oestrogen therapy (LET) on the expression of proteins participating in collagen/elastin biogenesis and immune markers in vaginal tissues of post-menopausal women with severe pelvic organ prolapse (POP). Vaginal biopsies were collected from the anterior vaginal wall of informed and consented 52 post-menopausal women with severe POP undergoing total hysterectomy. Twenty-nine of the 52 women were treated with LET (in the form of vaginal oestrogen cream or tablet), while the remaining 23 untreated patients served as the controls. This study was approved by Sinai Health System REB. Vaginal tissue specimens were analysed for gene and protein expression using real-time RT-PCR and Luminex assays, protein localization and immune cell infiltration were assessed by immunohistochemistry. Forty-four cytokines were detected. We found that LET application: (a) significantly increased (P < 0.05) gene and protein expression levels of extracellular matrix (ECM) structural proteins, collagen and elastin, as well as the expression of ECM maturation enzyme BMP1; (b) decreased protein expression level of ECM degradation enzymes MMP1, MMP2 and MMP3 accompanied by an increase in their tissue inhibitors, TIMP1 and TIMP4; (c) significantly increased (P < 0.05) the gene and protein expression levels of 14 vaginal cytokines involved in leucocyte infiltration, which was confirmed by immunohistochemistry. Our results indicate that LET plays an important role in the activation of immune system within the local vaginal environment, limiting the undesirable ECM degradation, which supports the strengthening of vaginal ECM in post-menopausal women, therefore resisting menopause/age-related changes and inducing urogenital tract tissue regeneration.
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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 | 0.000 |
| 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".