Effects of Bovine Somatotropin on β-Casein mRNA Levels in Mammary Tissue of Lactating Cows
Bibliographic record
Abstract
Bovine somatotropin (bST) increases milk production in lactating cows through its effect on nutrient partition and maintenance of mammary cell function. A positive relationship between bST treatment and abundance of beta-casein mRNA in mammary tissues from lactating cows was hypothesized. In mammary tissue isolated from 14 midlactation Holstein cows, beta-casein mRNA was 35.4% higher among 7 cows receiving continuous bST infusions at 29 mg/d for 63 d compared with tissue from 7 untreated control cows. To investigate whether increased beta-casein mRNA resulted from a direct effect of bST on the mammary gland, explants of mammary tissue from other lactating cows that had not received bST were incubated with bST and prolactin in 2 experiments. Mammary explant cultures taken from 2 lactating cows that had not been milked for 48 h were supplemented with either prolactin or bST. Both prolactin and bST stimulated higher levels of beta-casein mRNA in the mammary explants compared with their non-supplemented counterparts. Explant cultures from 4 additional lactating cows were prepared from rear quarter mammary tissue subjected to milking intervals of 6 h for right rear quarters or 20 h for left rear quarters. Both bST- and prolactin-mediated increases in beta-casein mRNA were dependent on milking intervals. That is, levels of beta-casein mRNA were increased by bST or prolactin supplementation in explants isolated from the mammary quarters biopsied 20 h after milking but not for those biopsied at 6 h after milking. Results are consistent with a potential role for bST in up-regulating or sparing beta-casein mRNA levels in lactating bovine mammary tissue in a manner similar to prolactin.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".