Angiomotin isoforms are differentially affected by exercise training in skeletal muscles from lean and obese rats
Bibliographic record
Abstract
Angiomotin (Amot) was recently identified on the surface of endothelial cells, regulating their migration and thus angiogenesis. p80 Amot isoform is mostly expressed in the dynamic phase of angiogenesis when activated endothelial cells migrate, whereas p130 is more characteristic of stabilized vessels. Such observations were mainly obtained from studies on pathological angiogenesis and the physiological regulation of Amot in healthy tissues remains largely unknown. Objectives To study 1) whether Amot was physiologically expressed in skeletal muscle, 2) how physical activity, a pro‐angiogenic physiological stimulus in skeletal muscle, could regulate Amot expression, 3) if such regulation was altered in the context of obesity. Methods p80 and p130 expressions were measured in soleus and plantaris from sedentary and trained, lean or obese rats. Results Exercise training shifted Amot isoforms expression pattern toward an "angiogenic phenotype". However, such regulation was different between lean and obese rats. In lean animals, exercise training increased p80 expression, whereas in obese rats it reduced p130. Conclusion p80/p130 ratio could reflect the tissue angiogenic potential. Exercise shifted this Amot isoforms ratio toward an angiogenic phenotype by different mechanisms between lean and obese rats. 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 | 0.001 | 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".