Potentiation of BK Channels by α5β1 Integrin Activation in Arteriolar Smooth Muscle
Bibliographic record
Abstract
RGD peptides produce sustained vasodilation of rat skeletal muscle arterioles. We hypothesized that RGD ligands work through α5β1 integrin to modulate the activity of large conductance, Ca 2+ ‐activated K + (BK) channels in arteriolar smooth muscle. K + currents were recorded in single arteriolar myocytes using whole‐cell and single‐channel patch clamp methods. Activation of α5β1 integrin by an insoluble antibody resulted in ∼30–50% increase in the amplitude of IBTX‐sensitive, whole‐cell K + current. The endogenous α5β1 integrin ligand, fibronectin (FN; 10 μg/ml, 120 kD fragment), potentiated IBTX‐sensitive K + current by ∼80%, an effect that was blocked by the c‐Src inhibitor, PP2 (0.1–1 μM). In cell‐attached or excised, inside‐out patches, the N•Po of a ∼240 pS K + channel was significantly increased after FN application to the external surface of the patch through the recording pipette. FN also caused a leftward shift in the N•Po‐voltage relationship at constant [Ca 2+ ], which was blocked by PP2. The results suggest that α5β1 integrin activation potentiates BK channel activity in vascular smooth muscle via a membrane‐delimited pathway through both Ca 2+ ‐ and c‐Src‐dependent mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".