MicroRNAs miR-27a and miR-143 Regulate Porcine Adipocyte Lipid Metabolism
Bibliographic record
Abstract
MicroRNAs (miRNAs) are non-coding small RNAs that play roles in regulating gene expression. Some miRNAs have been classed as epigenetic regulators of metabolism and energy homeostasis. Previous reports indicated that the miRNAs miR-27a and miR-143 were involved in lipid metabolism in human and rodents. To determine the roles of porcine miR-27a and miR-143 in adipocyte lipid metabolism, porcine adipocytes were cultured and allowed to induce differentiation for 10 days. The lipid-filled adipocytes were then transfected with miRNA mimics and inhibitors. We measured how the indicators of adipogenesis and adipolysis in porcine adipocytes were affected by the over-expression and by the inhibition of both miR-27a and miR-143. The results indicated that the over-expression of miR-27a could accelerate adipolysis releasing significantly more glycerol and free fatty acids than the negative control (P < 0.001), while the mimic of miR-143 expression, promoted adipogenesis by accumulating more triglycerides (P < 0.001) in the adipocytes. In addition, we demonstrated that there was good correlation (r > 0.98, P < 0.001) between the indicators of adipolysis in cell lysates and in the culture medium.
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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 | 0.001 |
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".