Performance, diameter of muscle fibers, and gene expression of mechanistic target of rapamycin in pectoralis major muscle of broilers supplemented with leucine and valine
Bibliographic record
Abstract
Two experiments were performed to study the interaction between the standardized ileal digestible (SID) leucine (Leu) and valine (Val) levels on the mRNA expression of genes involved in the mechanistic target of rapamycin (mTOR) pathway (experiment I) and determine the requirement of these amino acids in low-protein diets, and their effects on performance, serum parameters and muscle fiber diameters of broilers (experiment II) from day 1 to day 21 post hatch. Broiler chickens were distributed in a completely randomized design in a 3 × 3 and 5 × 5 factorial arrangement for a total of 9 and 25 treatments in experiments I and II, respectively. There was no (P > 0.05) interaction between the SID Leu and Val levels on mRNA expression of mTOR, S6 kinase 1 (S6K1), 4E-binding protein-1 (4EBP1), eukaryotic elongation factor 2 (eEF2), and insulin-like growth factor-1 (IGF-1) genes in pectoralis major muscle. Leucine supplementation increased (P < 0.05) mRNA expression of mTOR and S6K1 genes in muscle tissue, whereas Val supplementation did not affect (P > 0.05) mRNA expression of the genes investigated. Interaction was observed (P < 0.05) between dietary Leu and Val levels on feed intake and gain:feed. Leucine supplementation may stimulate mRNA expression of mTOR and S6K1 genes in pectoralis major muscle of broilers from day 1 to day 21 post hatch. The SID Leu and Val levels required for the optimization of feed intake, weight gain, and gain:feed in low-crude protein diets for broiler chickens from day 1 to 21 post hatch were estimated at 1.29% and 0.96%, 1.28% and 0.92%, and 1.27% and 0.91%, respectively; however, these requirements may be greater to maximize muscle fiber growth.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".