Effect of Casein and Propionate Supply on Mammary Protein Metabolism in Lactating Dairy Cows
Bibliographic record
Abstract
The effects of casein (CN) and propionate (C3) on mammary AA metabolism were determined in 3 multiparous Holstein cows fitted with both duodenal and ruminal cannulas and used in a replicated Youden square with six 14-d periods. Casein (743 g/d in the duodenum) and C3 (1,041 g/d in the rumen) infusions were tested in a factorial arrangement. For each period, L-[1-(13)C]Leu (d 11) and NaH[13C]O3 (d 13) were infused into a jugular vein, and blood samples were taken from the carotid artery and the mammary vein to determine Leu kinetics and net uptake of AA. Both CN and C3 treatments separately increased milk protein concentration and yield. With CN there was a general response in mammary protein metabolism, involving increases in Leu net uptake (30%), the uptake:output ratio (8%), protein synthesis (11%), secretion in milk protein (21%), and oxidation (259%). In contrast, C3 treatments tended to increase only Leu in milk protein (7%) and, when in combination with CN, to reduce Leu used for protein synthesis (5%). Across all treatments, most Leu uptake by the mammary gland was accounted for as Leu in milk or oxidized, and the Leu balance was therefore achieved without involvement of either net peptide use or production. Mammary uptake of group 1 AA increased to match milk output with all infusions. In contrast, mammary uptake of group 2 AA exceeded output to a greater extent with CN than with C3 infusions, whereas the increment in uptake of group 3 AA increased with C3 treatments. Overall, these data suggest that different mechanisms operate to improve milk protein production when either protein or energy is supplied.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".