Binge Alcohol Exposure Triggers Atrial Fibrillation Through T-Type Ca<sup>2+</sup> Channel Upregulation via Protein Kinase C (PKC) / Glycogen Synthesis Kinase 3β (GSK3β) / Nuclear Factor of Activated T-Cells (NFAT) Signaling ― An Experimental Account of Holiday Heart Syndrome ―
Bibliographic record
Abstract
BACKGROUND: The association between binge alcohol ingestion and atrial fibrillation (AF), often termed "holiday heart syndrome", has long been recognized. However, the underlying cellular and molecular mechanisms are unknown. METHODS AND RESULTS: 3.2) and currents in cardiomyocytes, accompanied by upregulation of the transcription factors, Csx/Nkx2.5 and nuclear factor of activated T-cells (NFAT), in the nucleus, and of phospho-glycogen synthesis kinase 3β (GSK3β) in the cytosol. Inhibition of protein kinase C (PKC) during the 7- to 8-h period following ethanol exposure attenuated susceptibility to AF, whereas acute exposure did not. GSK3β inhibition itself upregulated TCC expression and increased AF susceptibility. CONCLUSIONS: The present study results suggest a crucial role for TCC upregulation in the AF substrate following binge alcohol-drinking, resulting from ethanol-induced PKC-activation that hyperphosphorylates GSK3β to cause enhanced calcineurin-NFAT-Csx/Nkx2.5 signaling. These observations elucidate for the first time the potential mechanisms underlying the clinically well-recognized, but mechanistically enigmatic, "holiday heart syndrome".
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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) | 0.002 | 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".