Effects of dietary crude protein levels and cysteamine supplementation on meat quality and related indices of finishing pigs
Bibliographic record
Abstract
The aim of this study was to investigate the effects of dietary crude protein levels and cysteamine (CS) supplementation on meat quality and related indices in longissimus dorsi muscle of finishing pigs. One hundred and twenty barrows were randomly allocated to a 2 × 2 factorial arrangement with five replicates of six pigs each. The primary variations were crude protein levels (14% or 10%) and CS supplemental levels (0 or 140 mg kg−1). After 41 d, 10 pigs per treatment were slaughtered. The results showed that low-protein level diets (LPDs) decreased Warner–Bratzler shear force (P < 0.01) and increased the content of intramuscular fat (P < 0.01). The mRNA expressions of lipogenic genes were up-regulated (P < 0.01), and the mRNA expressions of lipolytic genes were down-regulated (P < 0.01) in pigs fed LPD. LPDs increased the mRNA expressions of μ-calpain, and decreased the mRNA expression of calpastatin (P < 0.01). In addition, CS supplementation increased the mRNA expression of μ-calpain (P < 0.01). In conclusion, LPD improved the meat quality probably through regulating the lipogenesis, lipolysis, and the proteolysis process in muscle. The CS supplementation did not affect the meat quality of finishing pigs. Moreover, no significant interaction between dietary protein levels and CS supplementation for the meat quality of finishing pigs was observed.
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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 | 0.000 | 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".