Current perspective on differential communication in small resistance arteriesThis article is part of a Special Issue on Information Transfer in the Microcirculation.
Bibliographic record
Abstract
Blood flow is controlled by an integrated network of resistance arteries that are coupled in series and parallel with one another. To dramatically alter tissue perfusion as required during periods of high metabolic demand, arterial networks must dilate in a coordinated manner. Gap junctions facilitate arterial coordination by enabling electrical stimuli to conduct among endothelial and (or) smooth muscle cells. The goal of this review was to provide an introduction to the field of vascular communication, the process of intercellular conduction, and the manner in which key properties influence charge flow. After a brief historical introduction, we establish the idea that electrical stimuli conduct differentially among neighbouring endothelial and smooth muscle cells. Highlighting recent studies that have synergistically combined computational and experimental approaches, this perspective explores how specific structural, electrical, and gap junctional properties enable electrical phenomenon to conduct differentially. To close, the concept of differential communication is functionally integrated into a mechanistic understanding of blood flow control.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".