Endocrinology and mammary development of lactating Genex-Meishan and Large White sows
Bibliographic record
Abstract
Endocrine and metabolic data as well as mammary tissue composition were obtained in Genex-Meishan (GM, containing 50% Chinese Meishan genes) and Large White (LW) lactating sows. Jugular vein cannulae were used to collect serial blood samples from 9 LW and 8 GM sows for 4 h every 15 min on days 6 and 19 of lactation. Concentrations of prolactin and cortisol were determined on all samples while those of insulin-like growth factor-I (IGFI), growth hormone (GH), glucose and free fatty acids (FFA) were measured in hourly samples. Milk samples were obtained from 19 GM and 16 LWsows on day 23 of lactation and all sows were slaughtered on day 25. Mammary glands were excised and analyzed for tissue composition and for number and affinity of prolactin receptors. Concentrations of plasma IGF-I were lower (P < 0.01) and plasma FFA greater (P < 0.001) in GM than in LW sows. On day 6 of lactation, serum prolactin (P < 0.05) and cortisol (P < 0.01) concentrations were greater and glucose values lower (P < 0.001) in GM than in LW sows. The concentration of IGFI in lactoserum was lower (P < 0.001) while that of prolactin was greater (P < 0.05) in GM compared to LW sows on day 23 of lactation. There was less (P < 0.001) residual milk and more (P < 0.05) parenchymal RNA in mammary glands from GM compared to LW sows. The affinity of prolactin receptors was also greater (P < 0.05) in GM than in LW sows. The better emptying of mammary glands by litters from GM sows and the greater circulating concentrations of prolactin in early lactation as well as the greater affinity of mammary prolactin receptors may be related to the great milking potential of Meishan-derived sows. Key words: Hormones, lactation, mammary gland, Meishan, prolactin, sows
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 | 0.001 | 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".