Membrane Lipid-K <sub>IR</sub> 2.x Channel Interactions Enable Hemodynamic Sensing in Cerebral Arteries
Bibliographic record
Abstract
Objective— Inward rectifying K + (K IR ) channels are present in cerebral arterial smooth muscle and endothelial cells, a tandem arrangement suggestive of a dynamic yet undiscovered role for this channel. This study defined whether distinct pools of cerebral arterial K IR channels were uniquely modulated by membrane lipids and hemodynamic stimuli. Approach and Results— A Ba 2+ -sensitive K IR current was isolated in smooth muscle and endothelial cells of rat cerebral arteries; molecular analyses subsequently confirmed K IR 2.1/K IR 2.2 mRNA and protein expression in both cells. Patch-clamp electrophysiology next demonstrated that each population of K IR channels was sensitive to key membrane lipids and hemodynamic stimuli. In this regard, endothelial K IR was sensitive to phosphatidylinositol 4,5-bisphosphate content, with depletion impairing the ability of laminar shear stress to activate this channel pool. In contrast, smooth muscle K IR was sensitive to membrane cholesterol content, with sequestration blocking the ability of pressure to inhibit channel activity. The idea that membrane lipids help confer shear stress and pressure sensitivity of K IR channels was confirmed in intact arteries using myography. Virtual models integrating structural/electrical observations reconceptualized K IR as a dynamic regulator of membrane potential working in concert with other currents to set basal tone across a range of shear stresses and intravascular pressures. Conclusions— The data show for the first time that specific membrane lipid-K IR interactions enable unique channel populations to sense hemodynamic stimuli and drive vasomotor responses to set basal perfusion in the cerebral circulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".