Molecular Characterization of Large Conductance, Ca <sup>2+</sup> ‐activated, K <sup>+</sup> Channels (BK) in Arteries from Cerebral and Skeletal Muscle Vasculatures
Bibliographic record
Abstract
Previous studies have shown functional differences in BK of smooth muscle cells (VSMC) from cerebral and cremaster muscle vasculatures. Specifically, BK from cremaster showed decreased Ca 2+ sensitivity resulting in equivalent channel opening at more positive membrane potential compared to cerebral VSMC. Studies were performed to determine if variation in molecular features of BK contributes to differences in activity. The existence of splice variants in the intracellular C terminus domain of the α‐subunit was examined by RT‐PCR. Expression of β1, β2, β3 and β4 subunits was also determined. Total RNA was extracted from isolated arterioles and quantified by absorbance (260/280nm). Equal amounts of RNA were reverse‐transcribed into cDNA and quantitative‐PCR was performed. Two α‐subunit variants, Zero (without splice insert) and STREX‐1 (with a 58 amino acid insert at splice site 2), were identified as identical in cremaster and cerebral arteries by sequencing of RT‐PCR products. Q‐PCR showed the β1 subunit to be the predominant accessory subunit expressed in both vessels. A low level of β4 compared to β1 was observed in both preparations. No evidence was obtained for either β2 or β3 mRNA expression. The results suggest the molecular features of BK are likely similar in the two arteries. Differences in functional properties may relate to subunit stoichiometry of the intact channel or post‐translational mechanisms.
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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) | 0.001 | 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".