Expression of peroxisome proliferator-activated receptor (PPARγ) mRNA in adipose and muscle tissue of Holstein and Charolais cattle
Bibliographic record
Abstract
Peroxisome proliferator-activated receptor γ(PPARγ) regulates adipogenesis and lipid metabolism-related gene transcripts. The role, however, of PPARγ in different adipose depots and muscle in Holstein and Charolais cattle is still unclear. We used 20 animals (10 from each breed) for semi-quantitative reverse transcription polymerase chain reaction (RT-PCR) to measure PPARγ mRNA levels in subcutaneous (SC), perirenal (PR), omental (OM), and intramuscular (IM) adipose depots as well as longissimus muscle (MU). IM fat was dissected from muscle tissue in MU. Holstein were characterized by their higher OM (P < 0.01) and PR (P < 0.05) fat weights while the Charolais had a higher body weight (P < 0.001) and a larger longissimus muscle area (P < 0.001). The IM fat content and marbling scores tended to be higher in Holstein. No significant differences in PPARγ mRNA expression were observed between these two breeds for any tissue. In both breeds, MU PPARγ had the lowest expressed mRNA level (P < 0.05). In the IM fat depot, expression was higher (P < 0.05) than MU, but lower than the SC, PR, and OM fat depot PPARγ mRNA levels. Only OM PPARγ mRNA levels were higher (P < 0.05) than SC and PR in Charolais. To characterise the role of PPARγ mRNA in bovine adipogenesis, correlations were performed among PPARγ mRNA, carcass characteristics, and adipogenesis-related genes. Key words: Adipose tissue, muscle tissue, PPARγ, cattle
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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 | 0.001 | 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".