Addition of fish oil to diets for dairy cows. II. Effects on milk fat and gene expression of mammary lipogenic enzymes
Bibliographic record
Abstract
Sixteen Holstein cows in mid-lactation were used to determine whether alterations of mammary fatty acid metabolism are responsible for the milk fat depression associated with consumption of fish oil. Cows were given a total mixed ration with no added fish oil (control), unprotected fish oil (3.7 % of dry matter), or glutaraldehyde-protected microcapsules of fish oil (1.5% or 3.0% of dry matter) for 4 weeks. Milk samples were taken once a week and a mammary biopsy was taken from a rear quarter at the end of the treatment period. Milk fat content was lower in cows given unprotected fish oil (26.0 g/kg), 1.5% protected fish oil (24.6 g/kg) and 3% protected fish oil (20.4 g/kg) than in cows fed the control diet (36.0 g/kg). This was mainly due to a decrease in the synthesis of short-chain fatty acids. Consumption of protected fish oil decreased the abundance of lipogenic enzymes mRNA in the mammary gland. Acetyl-CoA carboxylase, fatty acid synthase, and stearoyl-CoA desaturase mRNAs for cows given 3% protected fish oil averaged only 30%, 25% and 25% of control values, respectively. Dietary addition of unprotected fish oil slightly decreased mRNA abundance of these enzymes but markedly reduced the amount of lipoprotein lipase mRNA. Milk fat content was significantly correlated with gene expression of acetyl-CoA carboxylase, fatty acid synthase, and stearoyl-CoA desaturase but not lipoprotein lipase. These results suggest that fish oil reduces milk fat percentage by inhibiting gene expression of mammary lipogenic enzymes.
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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) | 0.001 | 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 | 0.001 | 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".