Angio‐adaptation in unloaded skeletal muscle: new insights into an early and muscle type‐specific dynamic process
Bibliographic record
Abstract
With a remarkable plasticity, skeletal muscle adapts to an altered functional demand. Muscle angio-adaptation can either involve the growth or the regression of capillaries as respectively observed in response to endurance training or muscle unloading. Whereas the molecular mechanisms that regulate exercise-induced muscle angiogenesis have been extensively studied, understanding how muscle unloading can in contrast lead to capillary regression has received very little attention. Here we have investigated the consequences of a 9 day time course hindlimb unloading on both capillarization and expression of angio-adaptive molecules in two different rat skeletal muscles. Both soleus and plantaris muscles were atrophied similarly. In contrast, our results have shown different angio-adaptive patterns between these two muscles. Capillary regression occurred only in the soleus, a slow-twitch and oxidative postural muscle. Conversely, the level of capillarization was preserved in the plantaris, a fast-twitch and glycolytic muscle. We have also measured the time course protein expression of key pro- and anti-angiogenic signals (VEGF-A, VEGF-B, VEGF-R2, TSP-1). Our results have revealed that the angio-adaptive response to unloading was muscle-type specific, and that an integrated balance between pro- and anti-angiogenic signals plays a determinant role in regulating this process. In conclusion, we have brought new evidence that measuring the ratio between pro- and anti-angiogenic signals in order to evaluate muscle angio-adaptation was a more accurate approach than analysing the expression of molecular factors taken individually.
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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 | 0.001 |
| 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".