Mepivacaine attenuates vasodilation induced by ATP-sensitive potassium channels in rat aorta
Bibliographic record
Abstract
The goal of this in vitro study was to investigate the effect of mepivacaine on vasodilation induced by the ATP-sensitive potassium (KATP) channel opener levcromakalim in isolated endothelium-denuded rat aortas. The effects of mepivacaine and the KATP channel inhibitor glibenclamide, alone or in combination, on levcromakalim-induced vasodilation were assessed in the isolated aortas. The effects of mepivacaine or combined treatment with a protein kinase C (PKC) inhibitor, GF109203X, and mepivacaine on this vasodilation were also investigated. Levcromakalim concentration–response curves were generated for isolated aortas precontracted with phenylephrine or a PKC activator, phorbol 12,13-dibutyrate (PDBu). Further, the effects of mepivacaine and glibenclamide on levcromakalim-induced hyperpolarization were assessed in rat aortic vascular smooth muscle cells. Mepivacaine attenuated levcromakalim-induced vasodilation, whereas it had no effect on this vasodilation in isolated aortas pretreated with glibenclamide. Combined treatment with GF109203X and mepivacaine enhanced levcromakalim-induced vasodilation compared with pretreatment with mepivacaine alone. This vasodilation was attenuated in aortas precontracted with PDBu compared with those precontracted with phenylephrine. Mepivacaine and glibenclamide, alone or in combination, attenuated levcromakalim-induced membrane hyperpolarization. Taken together, these results suggest that mepivacaine attenuates vasodilation induced by KATP channels, which appears to be partly mediated by PKC.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".