Identification of circulating miRNA involved in meat yield of Korean cattle
Bibliographic record
Abstract
Cattle plays an important role in providing essential nutrients through meat production. Thus, we focused on epigenetic factors associated with meat yield. To investigate circulating miRNAs that are involved with meat yield and connect biofluids and longissimus dorsi (LD) muscle in Korean cattle, we performed analyses of the carcass characteristics, miRNA array, qPCR, and bioinformatics. Carcass characteristics relative to the yield grade (YG) showed that the yield index and rib eye area were the highest, whereas the backfat thickness was the lowest for YG A (equal to high YG) cattle among the three YGs. miRNA array sorted the circulating miRNAs that connect biofluids and LD muscle. miRNA qPCR showed that miR-15a (r = 0.84), miR-26b (r = 0.91), and miR-29c (r = 0.92) had positive relationships with biofluids and LD muscle. In YG A cattle, miR-26b was considered to be a circulating miRNA connecting biofluids and LD muscle because the target genes of miR-26b were more involved with myogenesis. Then, miR-26b-targeted genes, DIAPH3 and YOD1, were downregulated in YG A cattle. Our results suggest that miR-15a, miR-26b, and miR-29c are upregulated in biofluids and LD muscle, whereas DIAPH3 and YOD1 are downregulated in the LD muscle of finishing cattle steers.
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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 | 0.001 | 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".