An assessment of K<sub>IR</sub> channel function in human cerebral arteries
Bibliographic record
Abstract
In the rodent cerebral circulation, inward rectifying K+ (KIR) channels set resting tone and the distance over which electrical phenomena spread along the arterial wall. The present study sought to translate these observations into human cerebral arteries obtained from resected brain tissue. Computational modeling and a conduction assay first defined the impact of KIR channels on electrical communication; patch-clamp electrophysiology, quantitative PCR, and immunohistochemistry then characterized KIR2.x channel expression/activity. In keeping with rodent observations, computer modeling highlighted that KIR blockade should constrict cerebral arteries and attenuate electrical communication if functionally expressed. Surprisingly, Ba2+ (a KIR channel inhibitor) had no effect on human cerebral arterial tone or intercellular conduction. In alignment with these observations, immunohistochemistry and patch-clamp electrophysiology revealed minimal KIR channel expression/activity in both smooth muscle and endothelial cells. This absence may be reflective of chronic stress as dysphormic neurons, leukocyte infiltrate, and glial fibrillary acidic protein expression was notable in the epileptic cortex. In closing, KIR2.x channel expression is limited in human cerebral arteries from patients with epilepsy and thus has little impact on resting tone or the spread of vasomotor responses. NEW & NOTEWORTHY KIR2.x channels are expressed in rodent cerebral arterial smooth muscle and endothelial cells. As they are critical to setting membrane potential and the distance signals conduct, we sought to translate this work into humans. Surprisingly, KIR2.x channel activity/expression was limited in human cerebral arteries, a paucity tied to chronic brain stress in the epileptic cortex. Without substantive expression, KIR2.x channels were unable to govern arterial tone or conduction.
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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 | 0.001 | 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".