431 Increasing IGF-1 concentrations in late pregnancy stimulates mammary development of gilts
Bibliographic record
Abstract
Abstract The goal of this project was to determine if increasing insulin-like growth factor-1 (IGF-1) concentrations in late pregnancy can stimulate mammogenesis in gilts. Yorkshire x Landrace gilts (196.2 ± 6.2 kg BW on day 89 of gestation) were separated in two groups: 1) controls (CTL, n = 17) injected with sterile water, and 2) porcine somatotropin-treated (pST, n = 20) injected daily with 5 mg of pST (Reporcin®) from days 90 to 109 of gestation. Gilts were slaughtered on day 110 to collect mammary glands and blood samples were obtained on days 89, 96, 103 and 109 of gestation. Treated gilts gained more BW (P < 0.05) and lost more backfat (P < 0.05) than CTL gilts during treatment. There was a treatment x day effect (P < 0.01) on IGF-1, glucose and urea concentrations in blood. Concentrations of IGF-1 increased fourfold (P < 0.01) in pST compared with CTL gilts on days 96, 103 and 109 of gestation. Insulin values were also greater on days 96 (P < 0.01) and 103 (P = 0.01), and tended to be greater (P < 0.10) on day 109 of gestation in pST gilts. Glucose was greater in pST than CTL gilts on days 96 (P < 0.01), 103 (P < 0.01) and 109 (P = 0.01). Injections of pST did not affect weight of mammary extraparenchyma (P > 0.10) but increased mammary parenchymal mass (1922.2 vs 1576.1 ± 123.9 g, P < 0.05). Mammary parenchyma contained more (P < 0.05) protein, DNA and RNA and less fat (P < 0.05) and dry matter (P < 0.01) in pST than CTL gilts. These findings demonstrate that increasing circulating IGF-1 in late-pregnant gilts can stimulate mammary development both in terms of total parenchymal mass and of parenchymal tissue composition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".