Smooth Muscle K <sup>+</sup> Channels and the Modulation of Conduction in Cerebral Arteries
Bibliographic record
Abstract
Blood flow control is dependent on the initiation of electrical signals and their conduction along the arterial wall. The distance electrical phenomena conduct is primarily governed by gap junctions and membrane resistivity. In this study we determined whether changes in ion channel activity alter membrane resistivity, limiting electrical conduction. Middle cerebral arteries from Syrian hamsters were isolated, cannulated and studied under the standard conduction protocol. A focal KCl stimulus elicited a constrictor response that conducted robustly along the arterial wall with a decay constant of 0.484±0.036 μm/100μm vessel length. Manipulating the activity of large‐conductance Ca 2+ ‐activated or voltage‐dependent K + channels did not produce significant changes in conduction decay. Blocking and/or activating ATP‐sensitive K + channels also failed to modify the conducted response. In contrast, Ba 2+ blockade of inwardly‐rectifying K + (K IR ) channels augmented conduction decay (1.452±0.22 μm/100μm length), an effect attributed to the selective loss of negative slope conductance. In addition, experiments performed for the first time on human cerebral arteries displayed a similar increase in conduction decay in response to K IR inhibition. This study demonstrates that selective smooth muscle K + conductances can tune electrical communication by retaining appropriate biophysical properties.
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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) | 0.000 | 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 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".