Cerebral Vascular K <sub>IR</sub> 2.x Channels are Distinctly Regulated by Membrane Lipids and Hemodynamic Forces.
Bibliographic record
Abstract
This study examined membrane lipid (phosphatidylinositol‐bis‐phosphate (PIP 2 ) and cholesterol) regulation of cerebral arterial K IR and whether these signaling molecules enable distinct channel pools to uniquely sense hemodynamic forces. Endothelial and smooth muscle cells were freshly isolated from rat cerebral arteries; patch‐clamp electrophysiology, Q‐PCR and immunohistochemistry delineated K IR channel activity and expression. Electrophysiology revealed a Ba 2+ ‐sensitive K IR current in smooth muscle and endothelial cells, while Q‐PCR and immunohistochemistry confirmed K IR 2.x mRNA and protein expression respectively. Each cellular pool of K IR channels was sensitive to particular membrane lipids and hemodynamic forces. Endothelial K IR responded dynamically to PIP 2 manipulations, and laminar flow activated this channel pool in a PIP 2 dependent manner. In contrast, smooth muscle K IR reacted to cholesterol perturbations, and pressure stimuli (e.g. hyposmotic challenge or negative pressure application) modulated this channel pool in a cholesterol dependent manner. The flow and pressure sensitivity of K IR channels was confirmed in intact cerebral arteries using vessel myography. In summary, while both vascular cell types express K IR 2.x channels, each pool is distinctly regulated by membrane lipids and hemodynamic stimuli. This emerging picture of K IR regulation advances our mechanistic understanding of how hemodynamic forces interact to control arterial tone development. Support or Funding Information This research was supported by an operating grant from the Canadian Institute of Health Research. DG Welsh is Rorabeck Chair of Molecular Neuroscience and Vascular Biology at the University of Western Ontario. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.001 |
| 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".