Expression patterns of <i>PPARγ2</i>, <i>PGC-1α</i>, and <i>MEF2C</i> and their association with intramuscular fat content and skeletal muscle tenderness of crossbred Simmental bulls
Bibliographic record
Abstract
PPARγ2, PGC-1α, and MEF2C play an important role in skeletal muscle development and fat deposition. This study aimed to determine their mRNA expression levels in longissimus dorsi (Ld), semitendinosus (Se), and soleus (Sol) muscles of crossbred Simmental bulls and estimate their association with intramuscular fat (IMF) content and meat shear force (MSF). We measured the muscle fiber (MF) density and area, IMF content, and MSF of 6-, 12-, and 36-mo-old bulls. We found that the expression patterns differed with age: the PPARγ2 expression in the three muscles of 36-mo-old bulls was greater than that in the muscles of 6- and 12-mo-old bulls (P < 0.05). Furthermore, PGC-1α expression in Sol of 36-mo-old and MEF2C expression in Ld of 12-mo-old bulls were higher than those in the respective muscles of 6- and 12-mo-old bulls, and 6- and 36-mo-old bulls, respectively (P < 0.05). The MF area, IMF content, and MSF increased with age (P < 0.05). The PPARγ2 mRNA expression in Ld, Se, and Sol was positively correlated with MF area and IMF content (P < 0.05) and negatively correlated with MF density (P < 0.05). Thus, PPARγ2 might be a candidate marker, which is positively correlated with IMF content and MF area.
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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".