Molecular Basis of Atrial Fibrillation Pathophysiology and Therapy
Bibliographic record
Abstract
Atrial fibrillation (AF) is a highly prevalent arrhythmia, with substantial associated morbidity and mortality. There have been significant management advances over the past 2 decades, but the burden of the disease continues to increase and there is certainly plenty of room for improvement in treatment options. A potential key to therapeutic innovation is a better understanding of underlying fundamental mechanisms. This article reviews recent advances in understanding the molecular basis for AF, with a particular emphasis on relating these new insights to opportunities for clinical translation. We first review the evidence relating basic electrophysiological mechanisms to the characteristics of clinical AF. We then discuss the molecular control of factors leading to some of the principal determinants, including abnormalities in impulse conduction (such as tissue fibrosis and other extra-cardiomyocyte alterations, connexin dysregulation and Na + -channel dysfunction), electrical refractoriness, and impulse generation. We then consider the molecular drivers of AF progression, including a range of Ca 2+ -dependent intracellular processes, microRNA changes, and inflammatory signaling. The concept of key interactome-related nodal points is then evaluated, dealing with systems like those associated with CaMKII (Ca 2+ /calmodulin-dependent protein kinase-II), NLRP3 (NACHT, LRR, and PYD domains-containing protein-3), and transcription-factors like TBX5 and PitX2c. We conclude with a critical discussion of therapeutic implications, knowledge gaps and future directions, dealing with such aspects as drug repurposing, biologicals, multispecific drugs, the targeting of cardiomyocyte inflammatory signaling and potential considerations in intervening at the level of interactomes and gene-regulation. The area of molecular intervention for AF management presents exciting new opportunities, along with substantial challenges.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".