Candidate genes validation for intramuscular fat content of Nellore Cattle
Bibliographic record
Abstract
Intramuscular fat is an important factor for the sensory quality and value of meat. However, in tropical breeds such as Nellore, the lower marbling capacity represents a challenge for its positioning in premium markets. Marbling score (MS) and the total lipid (TL) determination methods are complementary methodologies for measuring beef intramuscular fat. Longissimus thoracis samples from the 24 most extreme steers (out of 189 steers) for MS (high = 12 and low = 12) and TL (high = 12 and low = 12) traits were collected to (1) validate in Nellore cattle differentially expressed genes for MS and TL traits found in the literature in other populations using real-time PCR, and (2) verify if differentially expressed genes were translated into differentially expressed proteins through advanced mass spectrometry (LC-MS/MS). Significant differences in the expression levels of the genes FABP4, DGAT1, DGAT2, BARX2, STAT5A, and SDC were observed in the group with high intramuscular fat content. These genes play important roles in lipid metabolism, adipogenesis, and muscle development. Proteins encoded by genes ( PRDM1 and COL1A2) regulated by the transcription factor STAT5A were differentially expressed and probably play a key role during intramuscular fat deposition. Our results confirm that the genes FABP4, DGAT1, DGAT2, STAT5A, and BARX2 validated here can potentially be used as biomarkers for intramuscular fat in Nellore 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".