Solving the Reach Problem: A Review of Present and Future Approaches for Addressing Ventricular Arrhythmias Arising from Deep Substrate.

Ventricular tachycardia (VT) is a significant cause of morbidity and mortality in patients with ischaemic and non-ischaemic cardiomyopathies. In most patients, the primary strategy of VT catheter ablation is based on the identification of critical components of reentry circuits and modification of abnormal substrate which can initiate reentry. Despite technological advancements in catheter design and improved ability to localise abnormal substrates, putative circuits and site of origins of ventricular arrhythmias (VAs), current technologies remain inadequate and durable success may be elusive when the critical substrate is deep or near to critical structures that are at risk of collateral damage. In this article, we review the available and potential future non-surgical investigational approaches for treatment of VAs and discuss the viability of these modalities.

A scalable, secure, and interoperable platform for deep data-driven health management.

The large amount of biomedical data derived from wearable sensors, electronic health records, and molecular profiling (e.g., genomics data) is rapidly transforming our healthcare systems. The increasing scale and scope of biomedical data not only is generating enormous opportunities for improving health outcomes but also raises new challenges ranging from data acquisition and storage to data analysis and utilization. To meet these challenges, we developed the Personal Health Dashboard (PHD), which utilizes state-of-the-art security and scalability technologies to provide an end-to-end solution for big biomedical data analytics. The PHD platform is an open-source software framework that can be easily configured and deployed to any big data health project to store, organize, and process complex biomedical data sets, support real-time data analysis at both the individual level and the cohort level, and ensure participant privacy at every step. In addition to presenting the system, we illustrate the use of the PHD framework for large-scale applications in emerging multi-omics disease studies, such as collecting and visualization of diverse data types (wearable, clinical, omics) at a personal level, investigation of insulin resistance, and an infrastructure for the detection of presymptomatic COVID-19.