Smooth Muscle/Endothelial K <sub>IR</sub> Channels Modulate Cell‐to‐cell Communication in Cerebral Arteries
Bibliographic record
Abstract
Global blood flow control is enabled by the conduction of charge along arteries. The distance over which electrical phenomena spread is governed by 3 factors, one being the ionic properties of vascular cells. In this study, we determined the role of inward rectifying K + channels (K IR ) in setting membrane conductance and charge spread along the arterial wall. Small middle cerebral arteries (~100 μm diameter) from hamster were probed with a standard conduction protocol. A focal KCl stimulus elicited a constrictor response that conducted robustly, with a decay constant of ~0.4 μm/100μm vessel length. Selective inhibition each K + channel class had no effect on conduction, the exception being Ba 2+ blockade of K IR, which facilitated decay (~1.6 μm/100μm vessel length). Patch clamp electrophysiology and Q‐PCR highlighted the presence of a K IR current in smooth muscle comprised of K IR 2.1/2.2 subunits. The incorporation of this current into an electrical model revealed that it was too small to account for the change in conduction decay; consequently another K IR current must be present. Electrophysiology and Q‐PCR confirmed a second K IR current in the endothelium. Computational modeling subsequently confirmed that inhibiting both currents had a greater effect on conduction decay, the result of a sizable voltage shift increasing feedback from voltage dependent‐ and Ca 2+ activated K + channels. In summary, our observations indicate that K IR channels are present in endothelial and smooth muscle cells, and together these K + conductances can tune electrical communication. Support or Funding Information Supported by CIHR
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".