Arginase-1 (ARG1) deficiency is a rare autosomal recessive disorder that affects the liver-based urea cycle, leading to impaired ureagenesis. This genetic disorder is caused by 40+ mutations found fairly uniformly spread throughout the ARG1 gene, resulting in partial or complete loss of enzyme function, which catalyzes the hydrolysis of arginine to ornithine and urea. ARG1-deficient patients exhibit hyperargininemia with spastic paraparesis, progressive neurological and intellectual impairment, persistent growth retardation, and infrequent episodes of hyperammonemia, a clinical pattern that differs strikingly from other urea cycle disorders. This review briefly highlights the current understanding of the etiology and pathophysiology of ARG1 deficiency derived from clinical case reports and therapeutic strategies stretching over several decades and reports on several exciting new developments regarding the pathophysiology of the disorder using ARG1 global and inducible knockout mouse models. Gene transfer studies in these mice are revealing potential therapeutic options that can be exploited in the future. However, caution is advised in extrapolating results since the lethal disease phenotype in mice is much more severe than in humans indicating that the mouse models may not precisely recapitulate human disease etiology. Finally, some of the functions and implications of ARG1 in non-urea cycle activities are considered. Lingering questions and future areas to be addressed relating to the clinical manifestations of ARG1 deficiency in liver and brain are also presented. Hopefully, this review will spark invigorated research efforts that lead to treatments with better clinical outcomes.

Despite substantial advances in the study, treatment, and prevention of cardiovascular disease, numerous challenges relating to optimally screening, diagnosing, and managing patients remain. Simultaneous improvements in computing power, data storage, and data analytics have led to the development of new techniques to address these challenges. One powerful tool to this end is machine learning (ML), which aims to algorithmically identify and represent structure within data. Machine learning’s ability to efficiently analyze large and highly complex data sets make it a desirable investigative approach in modern biomedical research. Despite this potential and enormous public and private sector investment, few prospective studies have demonstrated improved clinical outcomes from this technology. This is particularly true in cardiology, despite its emphasis on objective, data-driven results. This threatens to stifle ML’s growth and use in mainstream medicine. We outline the current state of ML in cardiology and outline methods through which impactful and sustainable ML research can occur. Following these steps can ensure ML reaches its potential as a transformative technology in medicine.

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