Electrical Communication in Integrated Networks of Resistance Arteries
Bibliographic record
Abstract
Vascular cells within arterial networks electrically communicate with one another to control tissue blood flow. To ascertain how charge distributes within an arterial network, we extended an existing model of electrical communication so that virtual arteries of variable dimension could be connected to one another. In general, each virtual artery consisted of one layer of endothelium and a single layer of smooth muscle. Each vascular cell was treated as the electrical equivalent of capacitor coupled in parallel with a non‐linear voltage dependent resistor (representing ionic conductance). Gap junctions interconnected neighboring cells and were represented as ohmic resistors. Simulations revealed that hyperpolarization initiated in a small number of endothelial cells spreads with little decay along an arterial wall. As these endothelial‐initiated responses conducted across a branch point, electrical decay was enhanced. The extent of decay varied according to the relative diameter of the daughter and parent arteries. Computational observations coincided with functional observations of cell‐to‐cell communication in the mouse cremaster preparation. Further simulations revealed the ability of electrical responses initiated in two daughter vessels to summate in a parent vessel and for hyperpolarizing stimuli originating in multiple distal vessels to dilate large proximal arteries. Together, these observations begin to highlight how the structural features of an arterial network influence cell‐to‐cell communication. These findings have important functional implications to the hyperemic response in normal and diseased animals. Funded by AHFMR and HSFC.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".