Structural and functional changes in the microvasculature of disused skeletal muscle
Bibliographic record
Abstract
Skeletal muscle and its microvasculature can exhibit remarkable plasticity in response to decreased functional demand (i.e., muscle disuse). Since the microvascular adaptation to disuse does seem to not depend solely on reduced demand, this review examines the various factors that may be responsible for the observed regression of microvascular structure and function during disuse. There are several animal models of muscle disuse; their common feature is that they are associated with a variety of confounding effects that make the interpretation of the "pure" disuse effect challenging. As well, in clinical studies, the effect of disuse can be difficult to separate from that of various pathologies. Regardless of methodological difficulties, degeneration of the capillary wall, capillary loss, arteriolar remodelling, reduced resting state blood flow, and reduced arteriolar responsiveness to acute vasodilative and vasoconstrictive stimuli have all been observed in disused muscles. The level, and presence/absence of these changes may depend on many factors including the duration of disuse, degree of muscle atrophy, residual muscle activity, microvascular blood flow, release of vasoactive agents from the degenerating muscle, muscle type, and the particular pathology associated with the muscle withering in humans. It is the present challenge to discover the presence/absence of key agents (possibly originating at the interface between the blood stream and the vascular wall, within the extracellular matrix, or the muscle fibres themselves) that alter the intra- and/or inter-cellular signalling to explain the mechanism of adaptation of the microvasculature to skeletal muscle disuse.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".