Importance of Anti‐angiogenic Factors in the Regulation of Skeletal Muscle Angiogenesis
Bibliographic record
Abstract
The microcirculation is essential for delivery of oxygen and nutrients to maintain skeletal muscle health and function. The network of microvessels surrounding skeletal myocytes has a remarkable plasticity that ensures a good match between muscle perfusion capacities and myofiber metabolic needs. Depending on physiologic conditions, this vascular plasticity can either involve growth (e.g., exercise-induced angiogenesis) or regression (e.g., physical deconditioning) of capillaries. This angio-adaptative response is thought to be controlled by a balance between pro- and anti-angiogenic factors and their receptors. While changes in the expression or activity for pro-angiogenic factors have been well studied in response to acute and chronic exercise during the past two decades, little attention thus far has been devoted to endogenous negative regulators that are also likely to be important in regulating capillary growth/regression. Indeed, the importance and contribution of anti-angiogenic factors in controlling skeletal muscle angiogenesis remains poorly understood. Here, we highlight the emerging research related to skeletal muscle expression of several negative angiogenic factors and discuss their potential importance in controlling skeletal muscle angio-adaptation, particularly in physiologic response to physical activity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".