The Impact of Arterial Network Structure on Electrical Communication
Bibliographic record
Abstract
The basis of electrical communication and its role in regulating vascular tone has have been successfully build on an examination of charge movement in isolated vessel segments. How electrical communication behaves in complex arterial networks remains elusive. The goal of this investigation was to examine the nature of electrical communication in arterial networks and to ascertain how charge distributes within these structures in normal and pathophysiological states. Our analysis began with simulations performed on an existing computational model expanded to form arterial networks ranging from 1–31 vessels. Initial simulations revealed that focal endothelial stimulation generated electrical responses that conducted to a variable degree along an unbranched vessel or across a single branch point depending on the designed structure. These predictions were consistent with observations from the mesenteric arteries and supported the idea that vessel length and branch points influence the conducted response by increasing electrical load. Further expansion of the virtual network to 31 branches revealed that electrical stimuli could ascend into proximal arteries if a sufficient number of distal arteries were simultaneously activated. Additional network analysis centered on the gap junctional conductance changes that occur during sepsis, hypertension and atherosclerosis, highlighted that each disease state uniquely affects electrical communication and consequently the nature of blood flow control. By synergistically employing computational modeling with experimentation, this study becomes the first to address how the physical structure of arterial network and its impact on electrical load influences electrical information in normal and diseased vessels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".