The effect of miR-6523a on growth hormone secretion in pituitary cells of Yanbian yellow cattle
Bibliographic record
Abstract
Yanbian yellow cattle breeding is limited by its slow growth. We previously found that the miRNA miR-6523a is differentially expressed between Yanbian yellow cattle and Han Yan cattle, which differ in growth characteristics. In this study, we evaluated the effects of miR-6523a on growth hormone (GH) secretion in pituitary cells of Yanbian yellow cattle. Bioinformatics analyses using TargetScan and RNAhybrid, as well as dual luciferase reporter assays, showed that miR-6523a targets the 3′ untranslated region of somatostatin receptor 5 (SSTR5). We further found that the mRNA and protein expression levels of GH in pituitary cells were significantly higher in cells treated with miR-6523a mimic than in the control group (P = 0.0082 and P = 0.0069). The GH mRNA and protein expression levels were lower in cells treated with miR-6523a inhibitor than in the control group, but the difference was not significant (P = 0.064 and P = 0.089). SSTR5 mRNA and protein levels were inhibited by miR-6523a mimic compared with the control group (P = 0.0024 and P = 0.0028) and were elevated slightly by miR-6523a inhibitor (P = 0.093 and P = 0.091). These results prove that miR-6523a regulates GH secretion in pituitary cells by SSTR5. More broadly, these findings provide a basis for studies of the roles of miRNAs in animal growth and development.
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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 | 0.001 |
| 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".