CaV1.2/CaV3.x channels mediate divergent vasomotor responses in human cerebral arteries
Bibliographic record
Abstract
The regulation of arterial tone is critical in the spatial and temporal control of cerebral blood flow. Voltage-gated Ca(2+) (CaV) channels are key regulators of excitation-contraction coupling in arterial smooth muscle, and thereby of arterial tone. Although L- and T-type CaV channels have been identified in rodent smooth muscle, little is known about the expression and function of specific CaV subtypes in human arteries. Here, we determined which CaV subtypes are present in human cerebral arteries and defined their roles in determining arterial tone. Quantitative polymerase chain reaction and Western blot analysis, respectively, identified mRNA and protein for L- and T-type channels in smooth muscle of cerebral arteries harvested from patients undergoing resection surgery. Analogous to rodents, CaV1.2 (L-type) and CaV3.2 (T-type) α1 subunits were expressed in human cerebral arterial smooth muscle; intriguingly, the CaV3.1 (T-type) subtype present in rodents was replaced with a different T-type isoform, CaV3.3, in humans. Using established pharmacological and electrophysiological tools, we separated and characterized the unique profiles of Ca(2+) channel subtypes. Pressurized vessel myography identified a key role for CaV1.2 and CaV3.3 channels in mediating cerebral arterial constriction, with the former and latter predominating at higher and lower intraluminal pressures, respectively. In contrast, CaV3.2 antagonized arterial tone through downstream regulation of the large-conductance Ca(2+)-activated K(+) channel. Computational analysis indicated that each Ca(2+) channel subtype will uniquely contribute to the dynamic regulation of cerebral blood flow. In conclusion, this study documents the expression of three distinct Ca(2+) channel subtypes in human cerebral arteries and further shows how they act together to orchestrate arterial 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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".