Insulin-like Growth Factors and Their Binding Proteins Define Specific Phases of Myometrial Differentiation During Pregnancy in the Rat1
Bibliographic record
Abstract
While the insulin-like growth factor (IGF) system is known to regulate uterine function during the estrous cycle, there are limited data on its role in myometrial growth and development during pregnancy. To address this issue, we defined the expression of the Igf hormones (1 and 2), their binding proteins (Igfbp 1-6), and Igf1r receptor genes in pregnant, laboring, and postpartum rat myometrium by real-time PCR. IGF family genes were differentially expressed throughout gestation. Igf1 and Igfbp1 mRNA levels were upregulated during proliferative phase (Days 6-12) of rat gestation. Igfbp3 gene expression also was elevated in proliferating smooth muscle cells (SMCs) and was highest at the time of transition between proliferative and synthetic phases (Days 12-15). Igfbp6 gene expression profile paralleled plasma progesterone (P4) concentrations, peaking during the synthetic phase (Days 17-19) and decreasing thereafter. Administration of P4 at late pregnancy (starting from Day 20) to maintain elevated plasma P4 concentrations blocked the onset of labor and prevented the fall in Igfbp6 mRNA levels. In contrast, the treatment of pregnant rats with the P4 receptor antagonist RU486 on Day 19 induced preterm labor and the premature decrease of Igfbp6 gene expression. Igfbp2 gene expression was transiently upregulated during the contractile phase of gestation (Days 21-23) solely in the gravid horn of unilaterally pregnant rats, but it was not affected in P4- or RU486-treated animals, supporting a role for mechanical stretch imposed by the growing fetuses. Igfbp5 gene was induced during postpartum involution. Our results suggest the importance of the IGF system in phenotypic and functional changes of myometrial SMCs throughout gestation in preparation for 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 | 0.001 | 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".