Endurance Training‐mediated Differential Regulation of miRNAs in Skeletal Muscle of Lean and Obese Men
Bibliographic record
Abstract
MicroRNAs (miRNAs) are evolutionarily conserved small non‐coding RNA species involved in post‐transcriptional gene regulation. In skeletal muscle, an acute bout of endurance exercise differentially alters miRNA species that regulate transcriptional networks involved in mitochondrial biogenesis, glucose and fatty acid metabolism, and muscle remodeling. The purpose of this study was to assess the expressional profile of targeted miRNAs following 3‐mo of endurance training in sedentary lean and obese men (N=5/group). The expression of miR‐ 100, 144, 190, 424, 668, and 923 was measured in the vastus lateralis before and after the program. Basal expression of miR‐424 was 160% greater (P<0.02), whereas miR‐144 was reduced by 77% (P=0.06) in the obese compared to the lean group. Endurance training significantly increased the expression of miR‐424 in both groups (P<0.01). Interestingly, the expression of miR‐144 in the obese was normalized to lean controls post endurance training (P=0.06). No changes were observed in miR‐100, 190, 668, and 923. We conclude that investigation of these molecules and their genetic targets may potentially identify new pathways involved in the pathology of complex metabolic diseases, improving our understanding of metabolic disorders and influence future approaches to the treatment of obesity. Research supported by CIHR.
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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".