Comparative studies of meat quality traits and the proteome profile between low pH and high pH muscles in longissimus dorsi of Berkshire
Bibliographic record
Abstract
This study was conducted to assess the molecular mechanism of meat quality between low- and high-pH muscles using two-dimensional gel electrophoresis (2DE) with matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI–TOF/MS) on longissimus dorsi muscles from Berkshire species, assigned to high- (5.92 ± 0.02) and low-pH groups (5.55 ± 0.03). The high-pH group had a lower lightness, yellowness, drip loss, shear force, and a higher National Pork Producers Council color than the low-pH group. The meat quality changes were related to the altered protein expression between the two groups. Fourteen protein spots were identified by MALDI–TOF/MS and among them, nine proteins involved in meat quality attributes significantly increased: the alpha-crystallin B chain, dual specificity phosphatase 1, vimentin X1 and X2, ATP synthase subunit d, mitochondrial (ATP5H), peroxiredoxin 6 (PRDX6), ubiquitin carboxyl-terminal hydrolase 14 (UCTH14), and cytochrome c. Moreover, the proteins’ translation efficiency was analyzed by their mRNA expression via quantitative polymerase chain reaction. An increase in the mRNA levels of ATP5H, PRDX6, and UCTH14 is consistent with protein expressions. These results may provide valuable information to decipher the molecular mechanism behind meat quality of low- and high-pH muscles.
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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) | 0.001 | 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".