Correlation between three glycometabolic‐related hormones and muscle glycolysis, as well as meat quality, in three pig breeds
Bibliographic record
Abstract
BACKGROUND: The present study aimed to evaluate the correlations among muscle concentrations of three glycometabolic-related hormones (insulin, epinephrine and glucagon), muscle glycolysis and meat quality in representative muscles of either glycolytic or oxidative types. Moreover, the relative glycometabolic-related gene expression was measured. One Western crossbreed DLY (Duroc × (Landrace × Yorkshire)), one crossbreed with half-Chinese native-pig origin DL (Duroc × LiangShan) and one pure Chinese native pig TP (Tibetan pig) were used in the present study. RESULTS: Among the three breeds, DLY had the greatest glucagon and epinephrine (P < 0.01). Compared with DLY, TP and DL had lower lactic acid concentrations, showing lower glycolytic potentials (GP), greater ultimate pH values (P < 0.01) and lower relative expression levels of glycometabolic-related genes (GYS1, PRKAG3 and PKM2). Compared with the glycolytic muscle (musculus longissimus dorsi), oxidative muscle PM (musculus psoas major) had lower glucagon and epinephrine contents, lower GP and better meat quality. The concentration of glycometabolic-related hormones in the muscle had significant correlations with muscle glycolysis, meat pH and lightness. CONCLUSION: The results obtained in the present study imply that glucagon and epinephrine levels could be used to indicate early glycolytic metabolism during postmortem. These findings may be helpful in identifying pork with undesirable quality traits. 2016 Society of Chemical Industry.
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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 | 0.001 | 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) | 0.000 | 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".