Regulation of skeletal muscle capillary growth in exercise and disease
Bibliographic record
Abstract
Capillaries, which are the smallest and most abundant type of blood vessel, form the primary site of gas, nutrient, and waste transfer between the vascular and tissue compartments. Skeletal muscle exhibits the capacity to generate new capillaries (angiogenesis) as an adaptation to exercise training, thus ensuring that the heightened metabolic demand of the active muscle is matched by an improved capacity for distribution of gases, nutrients, and waste products. This review summarizes the current understanding of the regulation of skeletal muscle capillary growth. The multi-step process of angiogenesis is coordinated through the integration of a diverse array of signals associated with hypoxic, metabolic, hemodynamic, and mechanical stresses within the active muscle. The contributions of metabolic and mechanical factors to the modulation of key pro- and anti-angiogenic molecules are discussed within the context of responses to a single aerobic exercise bout and short-term and long-term training. Finally, the paradoxical lack of angiogenesis in peripheral artery disease and diabetes and the implications for disease progression and muscle health are discussed. Future studies that emphasize an integrated analysis of the mechanisms that control skeletal muscle capillary growth will enable development of targeted exercise programs that effectively promote angiogenesis in healthy individuals and in patient populations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".