Characterization of microRNA expression in bovine adipose tissues: a potential regulatory mechanism of subcutaneous adipose tissue development
Bibliographic record
Abstract
BACKGROUND: MicroRNAs (miRNAs), a family of small non-coding RNA molecules, appear to regulate animal lipid metabolism and preadipocyte conversion to form lipid-assimilating adipocytes (i.e. adipogenesis). However, no miRNA to date has been reported to modulate adipogenesis and lipid deposition in beef cattle. RESULTS: The expression patterns of 89 miRNAs including four bovine specific miRNAs in subcutaneous adipose tissues from three groups of crossbred steers differing in backfat thickness were compared using qRT-PCR analysis. Eighty-six miRNAs were detectable in all samples, with 42 miRNAs differing among crossbreds (P < 0.05) and 15 miRNAs differentially expressed between tissues with high and low backfat thickness (P < 0.05). The expression levels of 18 miRNAs were correlated with backfat thickness (P < 0.05). The miRNA most differentially expressed and the most strongly associated with backfat thickness was miR-378, with a 1.99-fold increase in high backfat thickness tissues (r = 0.72). CONCLUSIONS: MiRNA expression patterns differed significantly in response to host genetic components. Approximately 20% of the miRNAs in this study were identified as being correlated with backfat thickness. This result suggests that miRNAs may play a regulatory role in white adipose tissue development in beef animals.
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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 | 0.000 | 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".