Emerging role of G protein-coupled receptors in microvascular myogenic tone
Bibliographic record
Abstract
Blood flow autoregulation results from the ability of resistance arteries to reduce or increase their diameters in response to changes in intravascular pressure. The mechanism by which arteries maintain a constant blood flow to organs over a range of pressures relies on this myogenic response, which defines the intrinsic property of the smooth muscle to contract in response to stretch. The resistance to flow created by myogenic tone (MT) prevents tissue damage and allows the maintenance of a constant perfusion, despite fluctuations in arterial pressure. Interventions targeting MT may provide a more rational therapeutic approach in vascular disorders, such as hypertension, vasospasm, chronic heart failure, or diabetes. Despite its early description by Bayliss in 1902, the cellular and molecular mechanisms underlying MT remain poorly understood. We now appreciate that MT requires a complex mechanotransduction converting a physical stimulus (pressure) into a biological response (change in vessel diameter). Although smooth muscle cell depolarization and a rise in intracellular calcium concentration are recognized as cornerstones of the myogenic response, the role of wall strain-induced formation of vasoactive mediators is less well established. The vascular system expresses a large variety of Class 1 G protein-coupled receptors (GPCR) activated by an eclectic range of chemical entities, including peptides, lipids, nucleotides, and amines. These messengers can function in blood vessels as vasoconstrictors. This review focuses on locally generated GPCR agonists and their proposed contributions to MT. Their interplay with pivotal G(q-11) and G(12-13) protein signalling is also discussed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".