Ca2+ signaling in vascular smooth muscle and endothelial cells in blood vessel remodeling: a review
Bibliographic record
Abstract
Abstract Vascular smooth muscle cells (VSMCs) and endothelial cells (ECs) act together to regulate blood pressure and systemic blood flow by appropriately adjusting blood vessel diameter in response to biochemical or biomechanical stimuli. Ion channels that are expressed in these cells regulate membrane potential and cytosolic Ca2+ concentration ([Ca2+]cyt) in response to such stimuli. The subsets of these ion channels involved in Ca2+ signaling often form molecular complexes with intracellular molecules via scaffolding proteins. This allows Ca2+ signaling to be tightly controlled in localized areas within the cell, resulting in a balanced vascular tone. When hypertensive stimuli are applied to blood vessels for extended periods, gene expression in these vascular cells can change dramatically. For example, alteration in ion channel expression often induces electrical remodeling that produces a depolarization of the membrane potential and elevated [Ca2+]cyt. Coupled with endothelial dysfunction blood vessels undergo functional remodeling characterized by enhanced vasoconstriction. In addition, pathological challenges to vascular cells can induce inflammatory gene products that may promote leukocyte infiltration, in part through Ca2+-dependent pathways. Macrophages accumulating in the vascular adventitia promote fibrosis through extracellular matrix turnover, and cause structural remodeling of blood vessels. This functional and structural remodeling often leads to chronic hypertension affecting not only blood vessels, but also multiple organs including the brain, kidneys, and heart, thus increasing the risk of severe cardiovascular events. In this review, we outline recent advances in multidisciplinary research concerning Ca2+ signaling in VSMCs and ECs, with an emphasis on the mechanisms underlying functional and structural vascular remodeling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".