'Precision and personalized medicine,' a dream that comes true?

Machine learning and network medicine: a novel approach for precision medicine and personalized therapy in cardiomyopathies

The early identification of pathogenic mechanisms is essential to predict the incidence and progression of cardiomyopathies and to plan appropriate preventive interventions. Noninvasive cardiac imaging such as cardiac computed tomography, cardiac magnetic resonance, and nuclear imaging plays an important role in diagnosis and management of cardiomyopathies and provides useful prognostic information. Most molecular factors exert their functions by interacting with other cellular components, thus many diseases reflect perturbations of intracellular networks. Indeed, complex diseases and traits such as cardiomyopathies are caused by perturbations of biological networks. The network medicine approach, by integrating systems biology, aims to identify pathological interacting genes and proteins, revolutionizing the way to know cardiomyopathies and shifting the understanding of their pathogenic phenomena from a reductionist to a holistic approach. In addition, artificial intelligence tools, applied to morphological and functional imaging, could allow imaging scans to be automatically analyzed to extract new parameters and features for cardiomyopathy evaluation. The aim of this review is to discuss the tools of network medicine in cardiomyopathies that could reveal new candidate genes and artificial intelligence imaging-based features with the aim to translate into clinical practice as diagnostic, prognostic, and predictive biomarkers and shed new light on the clinical setting of cardiomyopathies. The integration and elaboration of clinical habits, molecular big data, and imaging into machine learning models could provide better disease phenotyping, outcome prediction, and novel drug targets, thus opening a new scenario for the implementation of precision medicine for cardiomyopathies.

Personalized, Predictive, Participatory, Precision, and Preventive (P5) Medicine in Rotator Cuff Tears

Rotator cuff (RC) disease is a common musculoskeletal disorder of the shoulder entailing pain, with reduced functionality and quality of life. The main objective of this study was to present a perspective of the current scientific evidence about personalized, predictive, participatory, precision, and preventive approaches in the management of RC diseases. The personalized, predictive, participatory, precision and preventive (P5) medicine model is an interdisciplinary and multidisciplinary approach that will provide researchers and clinicians with a comprehensive patrimony of knowledge in the management of RC diseases. The ability to define genetic predispositions in conjunction with the evaluation of lifestyle and environmental factors may boost the tailoring of diagnosis and therapy in patients suffering from RC diseases.

Neuro-arrhythmology: a challenging field of action and research: a review from the Task Force of Neuro-arrhythmology of Italian Association of Arrhythmias and Cardiac Pacing

: There is a growing interest in the study of the mechanisms of heart and brain interactions with the aim to improve the management of high-impact cardiac rhythm disorders, first of all atrial fibrillation. However, there are several topics to which the scientific interests of cardiologists and neurologists converge constituting the basis for enhancing the development of neuro-arrhythmology. This multidisciplinary field should cover a wide spectrum of diseases, even beyond the classical framework corresponding to stroke and atrial fibrillation and include the complex issues of seizures as well as loss of consciousness and syncope. The implications of a more focused interaction between neurologists and cardiologists in the field of neuro-arrhythmology should include in perspective the institution of research networks specifically devoted to investigate 'from bench to bedside' the complex pathophysiological links of the abovementioned diseases, with involvement of scientists in the field of biochemistry, genetics, molecular medicine, physiology, pathology and bioengineering. An investment in the field could have important implications in the perspectives of a more personalized approach to patients and diseases, in the context of 'precision'medicine. Large datasets and electronic medical records, with the approach typical of 'big data' could enhance the possibility of new findings with potentially important clinical implications. Finally, the interaction between neurologists and cardiologists involved in arrythmia management should have some organizational implications, with new models of healthcare delivery based on multidisciplinary assistance, similarly to that applied in the case of syncope units.