The efficacy of Ranolazine on E1784K is altered by temperature and calcium
Bibliographic record
Abstract
Abstract E1784K is the most common mixed syndrome SCN5a mutation underpinning both Brugada syndrome type 1 (BrS1) and Long-QT syndrome type 3 (LQT3). The charge reversal mutant enhances the late sodium current (INa) passed by the cardiac voltage-gated sodium channel (NaV1.5), delaying cardiac repolarization. Exercise-induced triggers, like elevated temperature and cytosolic calcium, exacerbate E1784K late INa. In this study, we tested the effects of Ranolazine, the late INa blocker, on voltage-dependent and kinetic properties of E1784K at elevated temperature and cytosolic calcium. We used whole-cell patch clamp to measure INa from wild type and E1784K channels expressed in HEK293 cells. At elevated temperature, Ranolazine attenuated gain-of-function in E1784K by decreasing late INa, hyperpolarizing steady-state fast inactivation, and increasing use-dependent inactivation. Both elevated temperature and cytosolic calcium hampered the capacity of Ranolazine to suppress E1784K late INa. In-silico action potential (AP) simulations were done using a modified O’Hara Rudy (ORd) cardiac model. Simulations showed that Ranolazine failed to shorten AP duration, an effect augmented at febrile temperatures. The drug-channel interaction is clearly affected by external triggers, as reported previously with ischemia. Determining drug efficacy under various physiological states in SCN5a cohorts is crucial for accurate management of arrhythmias.
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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".