Capillary endothelial cells as coordinators of skeletal muscle blood flow during active hyperemia
Bibliographic record
Abstract
In this invited review, we explore the burgeoning possibility of capillary endothelial cells as coordinators of skeletal muscle blood flow in response to muscle contraction. The idea that the capillary is an active vascular unit in skeletal muscle microcirculation starkly diverges from the traditional dogma that seats arterioles as the central controllers of blood flow during exercise. This review aims to incite discussion as we revisit and rethink the role of capillary endothelial cells in skeletal muscle. We discuss the potential for a mismatch in the architectural relationships between the arteriolar microvasculature and contracting motor units that would negate consistent communication between them. We review the data from the past two decades demonstrating that capillaries are ideally located architecturally to communicate with skeletal muscle fibers and are mechanistically capable of signaling upstream arterioles that control their own perfusion. We show that the orchestration of a coordinated vascular response necessary to support active skeletal muscle fibers cannot be achieved by the arterioles, but rather it is the capillaries that drive the blood flow response to muscle contraction. Thus, capillaries need to be seriously considered as critical in the coordination of skeletal muscle blood flow during active hyperemia.
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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 | 0.001 |
| 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".