Manipulation of smooth muscle BK <sub>Ca</sub> using subunit directed siRNA
Bibliographic record
Abstract
Cremaster and cerebral vascular smooth muscle cells (VSMC) exhibit heterogeneity in large conductance Ca 2+ ‐activated K + channels (BK Ca ) partly due to differences in β1:α subunit ratio. To gain insight into BK Ca methods were developed for subunit‐specific knockdown of the channel. Using transient transfection approaches and small interfering RNAs (siRNA) either the α or β1 subunit was targeted in isolated arterioles. Control studies used fluorescently labeled siRNA or unrelated siRNA. After 2–3 days culture fluorescence images and whole cell K + currents were examined in dispersed VSMC. From functional data α‐subunit expression was reduced by ~60% in both vessels. Thus, at +70 mV, IBTX‐sensitive K + current density was significantly reduced after α‐subunit siRNA compared to control. Similarly, STOC frequency (at +20 mV) decreased following siRNA treatment while BK Ca opening by NS1619 or estrogen (E2) was decreased. Cells treated with β1‐subunit siRNA showed impaired responses to E2 with a greater effect in cerebral VSMCs compared to those of cremaster. Thus transient transfection and siRNA can be used to effectively decrease endogenous BK Ca activity in intact small arteries. Further, cerebral VSMCs treated with β1‐subunit siRNA exhibit a functional phenotype similar to untreated cremaster VSMCs, supporting the idea that differences in β1:α subunit ratio underlie observed heterogeneity in BK Ca activity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".