Exercise‐induced angiogenesis: When Murine Double Minute‐2 and Vascular Endothelial Growth factor run together for more capillaries
Bibliographic record
Abstract
Running exercise has been described as a physiological stimulus for skeletal muscle angiogenesis. Recent data, mostly from tumor cells studies, have evidenced that the Murine Double Minute‐2 (Mdm2) oncoprotein, the main negative regulator of p53, exerts itself some angiogenic effects, mainly by stimulating endothelial cells migration. Objectives We investigated whether exercise regulated Mdm2 and whether Mdm2 could thus represent a new actor of exercise‐induced muscle angiogenesis. Methods We developed an innovative model of ex vivo muscle explant culture allowing the time‐course study of muscle angiogenesis. Plantaris muscles from sedentary or active (70‐90 min exercise on a running treadmill, 12% slope, 25 m/min) Sprague‐Dawley rats were used for protein measurements and our ex vivo angiogenesis assay. Results We showed that 1) Mdm2 protein was strongly activated by phosphorylation on Ser166 in response to exercise, 2) exercise stimulated endothelial cells migration in our ex vivo muscle explant model, 3) such exercise‐induced angiogenic effect is dependant on Mdm2 activation, 4) Mdm2 activation is mediated by the Vascular Endothelial Growth Factor (VEGF) ‐ Extracellular signal‐regulated kinase 1/2 (ERK1/2) pathway. Conclusion Mdm2 is a new key actor of exercise‐induced angiogenesis in rat skeletal muscle. This work was supported by the Natural Sciences and Engineering Research Council of Canada.
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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) | 0.001 | 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".