Prolactin and progesterone concentrations around farrowing influence sow colostrum yield
Bibliographic record
Abstract
In swine, colostrum production is induced by the drop of progesterone concentrations which leads to the prepartum peak of prolactin. Prolactin regulates mammary cell turnover and stimulates lacteal nutrient synthesis. Progesterone inhibits prolactin secretion and down-regulates the prolactin receptor in the mammary gland. The aim of the study was to determine if the relative peripartal concentrations of progesterone and prolactin (PRL/P4 ratio) influence sow colostrum production. Twenty-nine Landrace Large White primiparous sows were used. Colostrum yield was estimated during 24 h starting at the onset of parturition (T0) using litter weight gains. Colostrum was collected at T0 and 24 h later (T24). Repeated jugular blood samples were collected during the peripartum period (i.e. from -72 to +24 h related to farrowing) and were assayed for progesterone and prolactin. Sows were retrospectively categorized according to their PRL/P4 ratio 24 h before farrowing: 3 (HighPRL/P4, n=13). Data were analyzed by ANOVA using the MIXED procedure (SAS Inst.), except for piglet mortality (GENMOD procedure). During the peripartum period, the circulating concentrations of progesterone were lower (P<0.05) while those of prolactin tended to be greater (P<0.10) in HighPRL/P4 compared with LowPRL/P4 sows. Colostrum yield was greater in HighPRL/P4 compared with LowPRL/P4 sows (4.1 versus 3.5 kg [RMSE=0.7], P<0.05). Colostrum gross composition and IgG and IgA concentrations did not differ between the two groups of sows (P>0.10). Piglet mortality between birth and T24 averaged 10.0% in LowPRL/P4 litters and 7.0% in HighPRL/P4 litters (P=0.29). In conclusion, a higher PRL/P4 ratio 24 h prepartum, characterized by lower progesterone concentrations and a trend for higher prolactin concentrations peripartum, led to a greater colostrum yield.
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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 | 0.001 |
| 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 | 0.000 |
| 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".