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Argininemia or arginase-1 deficiency constitutes a rare, genetic, metabolic disorder caused by mutations in arginase 1—the last enzyme of the urea cycle—that hydrolyses L-arginine to ornithine and urea. The disease is associated with progressive development of spasticity and other symptoms, including seizures, developmental delay, cognitive impairment, and hepatic pathology. The present review attempts to summarize the current knowledge on the pathophysiology of the disease and highlight novel methods for its evaluation. Different factors, such as the accumulation of arginine, ammonia, and guanidino compounds, act as neurotoxins and may account for the neurological sequelae observed in the disease. New markers, such as arginine/ornithine ratio along with metabolomics, machine learning algorithms, and genetic methods, can be useful in the early diagnosis of argininemia, while mobile phone apps can assist argininemic patients in adhering to the strict diet required. Neurophysiology, multi-modal imaging, and new modelling methods, such as induced pluripotent stem cells, hold promise for providing new insights into the pathophysiology of the disease. There are still many uncertainties regarding the underlying mechanisms of argininemia, but the use of novel modelling methods and new technology can lead to the decipherment of its pathophysiology, improvement of diagnostic accuracy, and better disease management.

Introduction Mining of electronic health record (EHRs) data is increasingly being implemented all over the world but mainly focuses on structured data. The capabilities of artificial intelligence (AI) could reverse the underusage of unstructured EHR data and enhance the quality of medical research and clinical care. This study aims to develop an AI-based model to transform unstructured EHR data into an organised, interpretable dataset and form a national dataset of cardiac patients.Methods and analysis CardioMining is a retrospective, multicentre study based on large, longitudinal data obtained from unstructured EHRs of the largest tertiary hospitals in Greece. Demographics, hospital administrative data, medical history, medications, laboratory examinations, imaging reports, therapeutic interventions, in-hospital management and postdischarge instructions will be collected, coupled with structured prognostic data from the National Institute of Health. The target number of included patients is 100 000. Natural language processing techniques will facilitate data mining from the unstructured EHRs. The accuracy of the automated model will be compared with the manual data extraction by study investigators. Machine learning tools will provide data analytics. CardioMining aims to cultivate the digital transformation of the national cardiovascular system and fill the gap in medical recording and big data analysis using validated AI techniques.Ethics and dissemination This study will be conducted in keeping with the International Conference on Harmonisation Good Clinical Practice guidelines, the Declaration of Helsinki, the Data Protection Code of the European Data Protection Authority and the European General Data Protection Regulation. The Research Ethics Committee of the Aristotle University of Thessaloniki and Scientific and Ethics Council of the AHEPA University Hospital have approved this study. Study findings will be disseminated through peer-reviewed medical journals and international conferences. International collaborations with other cardiovascular registries will be attempted.Trial registration number NCT05176769.

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