Circ<i>RIMKLB</i> promotes myoblast proliferation and inhibits differentiation by sponging <i>miR-29c</i> to release <i>KCNJ12</i>
Bibliographic record
Abstract
Muscle development is a complex process that was regulated by many factors, among which non-coding RNAs (ncRNAs) play a vital role in regulating multiple life activities of muscle cells. Circular RNA (circRNA), a type of non-coding RNA with closed-loop structure, has been reported to affect multiple life processes. However, the roles of circRNAs on muscle development have not been fully elucidated. The present study aimed to determine whether and how circ<i>RIMKLB</i> affects muscle development. Our study revealed that circ<i>RIMKLB</i> promoted myoblast proliferation and inhibited differentiation. Besides, <i>miR-29</i>c was proved as a downstream target of circ<i>RIMKLB</i> using dual-luciferase reporter assay and RNA-binding protein immunoprecipitation (RIP) assay. Also, potassium inwardly rectifying channel subfamily J member 12 (<i>KCNJ12</i>) was identified as a novel target of <i>miR-29c</i> via dual-luciferase reporter assay, quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR), and western blot. Circ<i>RIMKLB</i> and <i>KCNJ12</i> were both proved to regulate cell cycle on muscle regeneration after injury <i>in vivo</i>. In conclusion, we demonstrated that circ<i>RIMKLB</i> sponged <i>miR-29c</i>, releasing <i>KCNJ12</i> to regulate myoblast proliferation and differentiation and regulating cell cycle during muscle regeneration after injury <i>in vivo</i>.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".