The BrAID study protocol: integration of machine learning and transcriptomics for brugada syndrome recognition

Noncoding RNAs and Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes in Cardiac Arrhythmic Brugada Syndrome

Hundreds of thousands of people die each year as a result of sudden cardiac death, and many are due to heart rhythm disorders. One of the major causes of these arrhythmic events is Brugada syndrome, a cardiac channelopathy that results in abnormal cardiac conduction, severe life-threatening arrhythmias, and, on many occasions, death. This disorder has been associated with mutations and dysfunction of about two dozen genes; however, the majority of the patients do not have a definite cause for the diagnosis of Brugada Syndrome. The protein-coding genes represent only a very small fraction of the mammalian genome, and the majority of the noncoding regions of the genome are actively transcribed. Studies have shown that most of the loci associated with electrophysiological traits are located in noncoding regulatory regions and are expected to affect gene expression dosage and cardiac ion channel function. Noncoding RNAs serve an expanding number of regulatory and other functional roles within the cells, including but not limited to transcriptional, post-transcriptional, and epigenetic regulation. The major noncoding RNAs found in Brugada Syndrome include microRNAs; however, others such as long noncoding RNAs are also identified. They contribute to pathogenesis by interacting with ion channels and/or are detectable as clinical biomarkers. Stem cells have received significant attention in the recent past, and can be differentiated into many different cell types including those in the heart. In addition to contractile and relaxational properties, BrS-relevant electrophysiological phenotypes are also demonstrated in cardiomyocytes differentiated from stem cells induced from adult human cells. In this review, we discuss the current understanding of noncoding regions of the genome and their RNA biology in Brugada Syndrome. We also delve into the role of stem cells, especially human induced pluripotent stem cell-derived cardiac differentiated cells, in the investigation of Brugada syndrome in preclinical and clinical studies.

Toward advanced diagnosis and management of inherited arrhythmia syndromes: Harnessing the capabilities of artificial intelligence and machine learning

The use of advanced computational technologies, such as artificial intelligence (AI), is now exerting a significant influence on various aspects of life, including health care and science. AI has garnered remarkable public notice with the release of deep learning models that can model anything from artwork to academic papers with minimal human intervention. Machine learning, a method that uses algorithms to extract information from raw data and represent it in a model, and deep learning, a method that uses multiple layers to progressively extract higher-level features from the raw input with minimal human intervention, are increasingly leveraged to tackle problems in the health sector, including utilization for clinical decision support in cardiovascular medicine. Inherited arrhythmia syndromes are a clinical domain where multiple unanswered questions remain despite unprecedented progress over the past 2 decades with the introduction of large panel genetic testing and the first steps in precision medicine. In particular, AI tools can help address gaps in clinical diagnosis by identifying individuals with concealed or transient phenotypes; enhance risk stratification by elevating recognition of underlying risk burden beyond widely recognized risk factors; improve prediction of response to therapy, and further prognostication. In this contemporary review, we provide a summary of the AI models developed to solve challenges in inherited arrhythmia syndromes and also outline gaps that can be filled with the development of intelligent AI models.