Effects of dietary n-6: n-3 polyunsaturated fatty acid ratios on meat quality, carcass characteristics, tissue fatty acid profiles, and expression of lipogenic genes in growing goats
Bibliographic record
Abstract
The present study was conducted to investigate the effects of altering the ratio of n-6 to n-3 fatty acids in the diet on meat quality, fatty acid composition of muscle, and expression of lipogenic genes in the muscle of Boer goats. A total of twenty-one Boer goats (5 months old; 31.66±1.07 kg body weight) were randomly assigned to three dietary treatments with n-6:n-3 fatty acid ratios of 2.27:1 (LR), 5.01:1 (MR) and 10.38:1 (HR), fed at 3.7% of body weight. After 100 days of feeding, all goats were slaughtered and the longissimus dorsi muscle was sampled for analysis of fatty acids and gene expression. The dietary treatments did not affect (P>0.05) the carcass traits, and meat quality of growing goats. The concentrations of cis-9,trans-11 conjugated linoleic acid, trans vaccenic acid, polyunsaturated fatty acids, and unsaturated/saturated fatty acid ratios linearly increased (P<0.01) with decreasing dietary n-6:n-3 fatty acid ratios, especially for LR in the longissimus dorsi muscle of goats. In contrast, the mRNA expression level of the PPARα and PPARγ was down-regulated and stearoyl-CoA desaturase up-regulated in the longissimus dorsi of growing goats with increasing dietary n-6:n-3 fatty acid ratios (P<0.01). In conclusion, the results obtained indicate that the optimal n-6:n-3 fatty acid ratio of 2.27:1 exerted beneficial effects on meat fatty acid profiles, leading towards an enrichment in n-3 polyunsaturated fatty acids and conjugated linoleic acid in goat intramuscular fat.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".