Intercellular Conduction Optimizes Arterial Network Function and Conserves Blood Flow Homeostasis During Cerebrovascular Challenges
Bibliographic record
Abstract
OBJECTIVE: Cerebral arterial networks match blood flow delivery with neural activity. Neurovascular response begins with a stimulus and a focal change in vessel diameter, which by themselves is inconsequential to blood flow magnitude, until they spread and alter the contractile status of neighboring arterial segments. We sought to define the mechanisms underlying integrated vascular behavior and considered the role of intercellular electrical signaling in this phenomenon. Approach and Results: Electron microscopic and histochemical analysis revealed the structural coupling of cerebrovascular cells and the expression of gap junctional subunits at the cell interfaces, enabling intercellular signaling among vascular cells. Indeed, robust vasomotor conduction was detected in human and mice cerebral arteries after focal vessel stimulation: a response attributed to endothelial gap junctional communication, as its genetic alteration attenuated this behavior. Conducted responses were observed to ascend from the penetrating arterioles, influencing the contractile status of cortical surface vessels, in a simulated model of cerebral arterial network. Ascending responses recognized in vivo after whisker stimulation were significantly attenuated in mice with altered endothelial gap junctional signaling confirming that gap junctional communication drives integrated vessel responses. The diminishment in vascular communication also impaired the critical ability of the cerebral vasculature to maintain blood flow homeostasis and hence tissue viability after stroke. CONCLUSIONS: Our findings highlight the integral role of intercellular electrical signaling in transcribing focal stimuli into coordinated changes in cerebrovascular contractile activity and expose, a hitherto unknown mechanism for flow regulation after stroke.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".