Concurrent Pregnancy Retards Mammary Involution: Effects on Apoptosis and Proliferation of the Mammary Epithelium after Forced Weaning of Mice1
Bibliographic record
Abstract
The effect of pregnancy on postweaning mammary gland involution was investigated in mice. On the third day after forced weaning at Lactation Day 10, the apoptotic index was 56% lower in mammary tissue of mice that were pregnant at the time of weaning than in nonpregnant mice. Conversely, the bromodeoxyuridine-labeling index was increased sevenfold in pregnant mice compared to nonpregnant controls (3.5% vs. 0.5%, respectively). Structure of mammary alveoli was largely maintained in postweaning pregnant mice. The effect of pregnancy on three specific mammary epithelial cell survival pathways was also examined. First, pregnancy blocked the loss of Stat5a phosphorylation during involution. Significantly, loss of Stat5a phosphorylation during involution was not correlated with loss of Stat5a nuclear localization. Second, pregnancy maintained nuclear-localized progesterone receptor during lactation. Third, pregnancy was associated with increased expression of bfl-1 during involution but had little effect on the expression of other bcl-2 family members. The data indicate that pregnancy inhibits mammary cell apoptosis after weaning while permitting proliferation of the mammary epithelium, and they support the hypothesis that Stat5a and progesterone-signaling pathways act in concert to mediate this effect.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".