Acidification alters Antiarrhythmic Drug Blockade of the ether‐a‐go‐go‐related Gene (HERG) Channels
Bibliographic record
Abstract
Acidosis is one of the important deleterious factors during myocardial ischaemia and reperfusion. The ether-a-go-go-related gene, HERG, is a primary target for blockade by many drugs including dofetilide, quinidine and azimilide. While most drugs lose their efficacy against arrhythmias associated with myocardial ischaemia and reperfusion, dofetilide remains effective. The unique ability of dofetilide to terminate ischaemia-induced arrhythmias is not yet fully explained. The aim of the present study is to elucidate the acidification modulation of antiarrhythmic drugs blockade of HERG channels. The human gene HERG encoding K+ channels were expressed in Xenopus oocytes, and Whole-cell macroscopic currents of Xenopus oocytes were recorded with conventional two-electrode techniques. The inhibitory effects of dofetilide (0.25 microM) were significantly enhanced with decreasing pH (from 7.5 to 6.5). The percent block of dofetilide under pH 6.5 at 0 mV was 69+/-6.1% versus 54+/-3.0% under pH 7.5 (n=7, P<0.05). The IC50 values, determined by the Hill equation with the currents recorded at 0 mV, were decreased by approximately half from 192+/-23 nM with pH 7.5 to 93+/-15 nM with pH 6.5 (P<0.01). Acidification weakened the inhibitory effects of quinidine and azimilide on HERG channels. At 0 mV, the percent block of quinidine (10 microM) under pH 6.5 was 24+/-2.8% versus 62.5+/-9.0% under pH 7.5 (n=4, P<0.01), The percent block of azimilide (10 microM) under pH 6.5 was similar to that under pH 7.5 (n=6). Acidification markedly potentiated dofetilide blockade of the HERG channels but weakened the inhibitory effects of quinidine and azimilide.
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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".