Febrile temperature decreases the cell‐surface expression of the human ether‐a‐go‐go‐related gene (hERG) channel by facilitating channel degradation (949.3)
Bibliographic record
Abstract
The human‐ether‐a‐go‐go‐related gene (hERG) encoded I Kr and KvLQT1+minK encoded I Ks are primary K + currents responsible for cardiac repolarization in many species including humans. Dysfunction of either I Kr or I Ks can lead to long QT syndrome (LQTS), which predisposes the affected individuals to arrhythmias, syncope, or sudden death. Recent studies have reported that fever may serve as a trigger for QT interval prolongation and arrhythmias in patients. In the present study, we investigated the effect of febrile temperature on hERG and KvLQT1+minK channels using Western blot, patch clamp, and immunocytochemistry. Our data show that culturing cells in febrile temperature (40°C) reduced the expression and current amplitude of hERG channels stably expressed in HEK cells as well as I Kr in neonatal rat cardiomyocytes. However, this manipulation did not reduce KvLQT1+minK current. Our data further show that accelerated protein degradation is responsible for the reduced plasma abundance of hERG channels. We have previously shown that hypokalemia accelerates hERG degradation and reduces hERG expression at the plasma membrane. Our data revealed that a reduction in K + greatly exaggerated, and K + insensitive mutation S624T completely abolished febrile temperature ‐induced hERG degradation. We conclude that febrile temperature may facilitate the development of LQTS via accelerating hERG degradation. Supported by the Canadian Institutes of Health Research (CIHR). Grant Funding Source : Supported by Canadian Institutes of Health Research
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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".