Regulation of calcium-activated chloride channels in smooth muscle cells: a complex picture is emerging
Bibliographic record
Abstract
Calcium-activated chloride channels (ClCa) are ligand-gated anion channels as they have been shown to be activated by a rise in intracellular Ca2+ concentration in various cell types including cardiac, skeletal and vascular smooth muscle cells, endothelial and epithelial cells, as well as neurons. Because ClCa channels are normally closed at resting, free intracellular Ca2+ concentration (approximately 100 nmol/L) in most cell types, they have generally been considered excitatory in nature, providing a triggering mechanism during signal transduction for membrane excitability, osmotic balance, transepithelial chloride movements, or fluid secretion. Unfortunately, the genes responsible for encoding this class of ion channels is still unknown. This review centers primarily on recent findings on the properties of these channels in smooth muscle cells. The first section discusses the functional significance and biophysical and pharmacological properties of ClCa channels in smooth muscle cells, and ends with a description of 2 candidate gene families (i.e., CLCA and Bestrophin) that are postulated to encode for these channels in various cell types. The second section provides a summary of recent findings demonstrating the regulation of native ClCa channels in vascular smooth muscle cells by calmodulin-dependent protein kinase II and calcineurin and how their fine tuning by these enzymes may influence vascular tone.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".