Ca <sub>V</sub> 3.2 Channels and the Induction of Negative Feedback in Cerebral Arterial Smooth Muscle
Bibliographic record
Abstract
Ca V 1.2 (L‐type) along Ca V 3.1/Ca V 3.2 (T‐type) are the principal subtypes of voltage‐gated Ca 2+ channels (VGCC) expressed in cerebral arterial smooth muscle. While studies have long discerned the functional role of Ca V 1.2, the physiological significance of Ca V 3.x expression is uncertain. Recent immunohistochemical analysis noted that Ca V 3.2 localizes in close proximity to ryanodine receptors (RyR) on the sarcoplasmic reticulum. From these observations, we hypothesized that Ca V 3.2 triggers Ca 2+ ‐induced Ca 2+ release (RyR), activating large‐conductance Ca 2+ ‐activated K + channels (BK Ca ) to attenuate arterial constriction. Structural analysis involving immuno‐labeling approaches, electron microscopy, and 3D‐tomomography revealed a microdomain structure in cerebral arteries comprised of Ca V 3.2 and RyR. Using mathematical techniques, a microdomain model was subsequently developed and it revealed that Ca v 3.2 was capable of activating RyR and induce repetitive CICR‐like events. In keeping with these theoretical observations, perforated patch clamp electrophysiology revealed that Ni 2+ (50 μM, Ca v 3.2 inhibitor) attenuated the frequency and amplitude of BK Ca ‐mediated spontaneous transient outward currents (STOCs). Pressurized cerebral arteries were also shown to depolarized and constricted to micromolar Ni 2+ . The magnitude of these functional responses was comparable to paxilline, a BK Ca channel inhibitor. In summary, findings indicate for the first time that Ca V 3.2 channels are capable of driving a CICR‐like process that moderates arterial constriction through a feedback response involving BK Ca channels.
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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) | 0.000 | 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".