Capillary communication: the role of capillaries in sensing the tissue environment, coordinating the microvascular, and controlling blood flow
Bibliographic record
Abstract
Historically, capillaries have been viewed as the microvascular site for flux of nutrients to cells and removal of waste products. Capillaries are the most numerous blood vessel segment within the tissue, whose vascular wall consists of only a single layer of endothelial cells and are situated within microns of each cell of the tissue, all of which optimizes capillaries for the exchange of nutrients between the blood compartment and the interstitial space of tissues. There is, however, a growing body of evidence to support that capillaries play an important role in sensing the tissue environment, coordinating microvascular network responses, and controlling blood flow. Much of our growing understanding of capillaries stems from work in skeletal muscle and more recent work in the brain, where capillaries can be stimulated by products released from cells of the tissue during increased activity and are able to communicate with upstream and downstream vascular segments, enabling capillaries to sense the activity levels of the tissue and send signals to the microvascular network to coordinate the blood flow response. This review will focus on the emerging role that capillaries play in communication between cells of the tissue and the vascular network required to direct blood flow to active cells in skeletal muscle and the brain. We will also highlight the emerging central role that disruptions in capillary communication may play in blood flow dysregulation, pathophysiology, and disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".