Greater milk yield is related to increased DNA and RNA content but not to mRNA abundance of selected genes in sow mammary tissue
Bibliographic record
Abstract
The relationship between greater sow milk yield and mammary development, expression of selected genes in mammary tissue and hormonal concentrations in lactating sows was studied. Crossbred sows were separated in two groups according to the weight gains of their piglets up to day 21. The groups were: (1) lower milk yield (LOW, n = 14) and (2) higher milk yield (HI, n = 14), representing lactation weight gains of 4.46 and 5.25 kg pig -1 , respectively. Jugular blood samples were obtained from all sows on day 3 (for prolactin determination) and day 23 (for measures of prolactin, leptin, insulin, glucose and free fatty acids) of lactation, and milk samples were collected on days 3 and 22. At weaning (day 23), sows were slaughtered and their mammary glands were collected, dissected and composition was determined. Mammary parenchymal tissue was analyzed for the mRNA abundance of selected genes. Hormone concentrations in blood did not differ between groups (P > 0.1) and on day 3 of lactation, dry matter and leptin contents in milk were lower (P < 0.05) in HI than in LOW sows. There was more DNA and RNA per teat in HI than LOW sows (P < 0.05), whereas the expression of selected genes within mammary tissue was unaffected (P > 0.1) by production group. Significant correlations (P < 0.01) existed between average weight gain of piglets during lactation and mammary RNA and DNA, expressed either as total amount or amount per teat, at weaning. Sow milk yield is therefore related to mammary gland composition in late lactation.Key words: Genes, lactation, mammary gland, milk yield, sow
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".