The expression of transforming growth factor β in pregnant rat myometrium is hormone and stretch dependent
Bibliographic record
Abstract
From a quiescent state in early pregnancy to a highly contractile state in labor, the myometrium displays tremendous growth and remodeling. We hypothesize that the transforming growth factor beta (TGFbeta) system is involved in the differentiation of pregnant myometrium throughout gestation and labor. Furthermore, we propose that during pregnancy the mechanical and hormonal stimuli play a role in regulating myometrial TGFbetas. The expression of TGFbeta1-3 mRNAs and proteins was examined by real-time PCR, Western immunoblot, and localized with immunohistochemistry in the rat uterus throughout pregnancy and labor. Tgfbeta1-3 genes were expressed differentially in pregnant myometrium. Tgfbeta2 gene was not affected by pregnancy, whereas the Tgfbeta1 gene showed a threefold increase during the second half of gestation. In contrast, we observed a dramatic bimodal change in Tgfbeta3 gene expression throughout pregnancy. Tgfbeta3 mRNA levels first transiently increased at mid-gestation (11-fold on day 14) and later at term (45-fold at labor, day 23). Protein expression levels paralleled the changes in mRNA. Treatment of pregnant rats with the progesterone (P4) receptor antagonist RU486 induced premature labor on day 19 and increased Tgfbeta3 mRNA, whereas artificial maintenance of elevated P4 levels at late gestation (days 20-23) caused a significant decrease in the expression of Tgfbeta3 gene. In addition, Tgfbeta3 was up-regulated specifically in the gravid horn of unilaterally pregnant rats subjected to a passive biological stretch imposed by the growing fetuses, but not in the empty horn. Collectively, these data indicate that the TGFbeta family contributes in the regulation of myometrial activation at term integrating mechanical and endocrine signals for successful labor contraction.
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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".