Cardiac arrhythmias, which represent a major worldwide public health problem, are significantly associated with increased risks of cardiovascular complications and sudden death, accounting for 15–20 % of all deaths. Cardiac arrhythmia, defined as any abnormal heart rate or rhythm, encompasses sinus node dysfunction, atrial fibrillation (AF), atrioventricular block, ventricular tachycardia and ventricular fibrillation (VF). Unfortunately, from 1906 (AG Mayer ‘discovered’ “reentrant arrhythmia”) to now, we are yet to fully understand arrhythmia mechanisms and learn how to diagnose and treat arrhythmias effectively.
The main reasons for the poor performance of current clinical treatment for cardiac arrhythmias are due to
• Incomplete knowledge of potential risk factors;
• Nonexistence of effective research approaches for identifying substrates;
• Lack of accurate computational platform to bridge the gap between basic research and clinical manifestations;
• Proarrhythmic side effects of the drug therapy and ineffectiveness of catheter ablation in some patients with persistent atrial fibrillation or with concomitant cardiac disease; and
• Need ‘digital twin’ (i.e., mechanistic and statistical model) in aiding diagnosis, guiding treatments and evaluating prognosis.
• New insights and innovative approaches from bench to bedside in cardiac arrhythmias will broaden our knowledge on mechanisms driving this arrhythmia. In addition, they may help to design novel diagnostic tools and treatment strategies.
This research topic aims to collect a series of original studies, review and meta-analysis research articles that would present the most recent advances towards a better understanding, diagnosis and treatment of cardiac arrhythmias, including
• New insights into the cellular, molecular, genetic, and epigenetic mechanisms involved in cardiac arrhythmias;
• New research approaches: a novel experimental model systems; high-density mapping systems; (High-throughput) genetic screening systems and application of (epi)genomics, transcriptomics, metabolomics, kinomics;
• Novel computational approaches: modelling and simulations, novel signal/image processing approaches, machine learning, population-based statistical approach, computer software/languages (such as CellML, developed by the Auckland Bioengineering Institute at the University of Auckland);
• Antiarrhythmic drug therapy: new therapeutic frameworks for antiarrhythmic drug development, drug screening methods for antiarrhythmic agents, antiarrhythmic compounds in medicinal plants (e.g., related Chinese medicines and their active ingredients).
Cardiac arrhythmias, which represent a major worldwide public health problem, are significantly associated with increased risks of cardiovascular complications and sudden death, accounting for 15–20 % of all deaths. Cardiac arrhythmia, defined as any abnormal heart rate or rhythm, encompasses sinus node dysfunction, atrial fibrillation (AF), atrioventricular block, ventricular tachycardia and ventricular fibrillation (VF). Unfortunately, from 1906 (AG Mayer ‘discovered’ “reentrant arrhythmia”) to now, we are yet to fully understand arrhythmia mechanisms and learn how to diagnose and treat arrhythmias effectively.
The main reasons for the poor performance of current clinical treatment for cardiac arrhythmias are due to
• Incomplete knowledge of potential risk factors;
• Nonexistence of effective research approaches for identifying substrates;
• Lack of accurate computational platform to bridge the gap between basic research and clinical manifestations;
• Proarrhythmic side effects of the drug therapy and ineffectiveness of catheter ablation in some patients with persistent atrial fibrillation or with concomitant cardiac disease; and
• Need ‘digital twin’ (i.e., mechanistic and statistical model) in aiding diagnosis, guiding treatments and evaluating prognosis.
• New insights and innovative approaches from bench to bedside in cardiac arrhythmias will broaden our knowledge on mechanisms driving this arrhythmia. In addition, they may help to design novel diagnostic tools and treatment strategies.
This research topic aims to collect a series of original studies, review and meta-analysis research articles that would present the most recent advances towards a better understanding, diagnosis and treatment of cardiac arrhythmias, including
• New insights into the cellular, molecular, genetic, and epigenetic mechanisms involved in cardiac arrhythmias;
• New research approaches: a novel experimental model systems; high-density mapping systems; (High-throughput) genetic screening systems and application of (epi)genomics, transcriptomics, metabolomics, kinomics;
• Novel computational approaches: modelling and simulations, novel signal/image processing approaches, machine learning, population-based statistical approach, computer software/languages (such as CellML, developed by the Auckland Bioengineering Institute at the University of Auckland);
• Antiarrhythmic drug therapy: new therapeutic frameworks for antiarrhythmic drug development, drug screening methods for antiarrhythmic agents, antiarrhythmic compounds in medicinal plants (e.g., related Chinese medicines and their active ingredients).
