Overexpression of Des(1–3) Insulin-Like Growth Factor 1 in the Mammary Glands of Transgenic Mice Delays the Loss of Milk Production with Prolonged Lactation1
Bibliographic record
Abstract
During prolonged lactation, the mammary gland gradually loses the capacity to produce milk. In agricultural species, this decline can be slowed by administration of exogenous growth hormone (GH), which is believed to act through insulin-like growth factor 1 (IGF1). Our previous work demonstrated delayed natural mammary gland involution in des(1-3)IGF1-overexpressing transgenic mice (Tg[Wap-des{1-3}IGF1]8266 Jmr), hereafter referred to as WAP-DES mice. The present study tested the hypothesis that overexpressed des(1-3)IGF1 would delay the loss of milk production during prolonged lactation. Accordingly, we examined lactational performance in WAP-DES mice by artificially prolonging lactation with continual litter cross-fostering. Over time, lactational capacity and mammary development declined in both WAP-DES and control mice. However, the rate of decline was 40% slower in WAP-DES mice. Mammary cell apoptosis increased by 3-fold in both groups during prolonged lactation but was not different between genotypes. Plasma concentrations of murine IGF1 were decreased in WAP-DES mice, while those of the transgenic human IGF1 were elevated during prolonged lactation. Phosphorylation of the mammary IGF1 receptor was increased in the WAP-DES mice, but only during prolonged lactation. Plasma prolactin decreased with prolonged lactation in nontransgenic mice but remained high in WAP-DES mice. The WAP-DES mice maintained a higher body mass and a greater lean body mass during prolonged lactation. These data support the conclusion that overexpressed des(1-3)IGF1 enhanced milk synthesis and mammary development during prolonged lactation through localized and direct activation of the mammary gland IGF1 receptor and through systemic effects on prolactin secretion and possibly nutrient balance.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".