Progesterone and Gravidity Differentially Regulate Expression of Extracellular Matrix Components in the Pregnant Rat Myometrium1
Bibliographic record
Abstract
Myometrial growth and remodeling during pregnancy depends on increased synthesis of interstitial matrix proteins. We hypothesize that the presence of mechanical tension in a specific hormonal environment regulates the expression of extracellular matrix (ECM) components in the uterus. Myometrial tissue was collected from pregnant rats on Gestational Days 0, 12, 15, 17, 19, 21, 22, 23 (labor), and 1 day postpartum and ECM expression was analyzed by Northern blotting. Expression of fibronectin, laminin beta2, and collagen IV mRNA was low during early gestation but increased dramatically on Day 23 during labor. Expression of fibrillar collagens (type I and III) peaked Day 19 and decreased near term. In contrast, elastin mRNA remained elevated from midgestation onward. Injection of progesterone (P4) on Days 20-23 (to maintain elevated plasma P4 levels) delayed the onset of labor, caused dramatic reductions in the levels of fibronectin and laminin mRNA, and prevented the fall of collagen III mRNA levels on Day 23. Treatment of pregnant rats with the progesterone receptor antagonist RU486 on Day 19 induced preterm labor on Day 20 and a premature increase in mRNA levels of collagen IV, fibronectin, and laminin. Analysis of the uterine tissue from unilaterally pregnant rats revealed that most of the changes in ECM gene expression occurred specifically in the gravid horn. Our results show a decrease in expression of fibrillar collagens and a coordinated temporal increase in expression of components of the basement membrane near term associated with decreased P4 and increased mechanical tension. These ECM changes contribute to myometrial growth and remodeling during late pregnancy and the preparation for the synchronized contractions of labor.
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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".