Cardiomyopathy and Precision Medicine: 2nd Edition
A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".
Deadline for manuscript submissions: 25 December 2024 | Viewed by 5260
Special Issue Editor
Interests: heart failure; cardiomyopathy; autoimmunity; microbitoa; biomarkers; genetic testing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The diagnosis of the most common forms of cardiomyopathies, dilated and hypertrophic cardiomyopathies, have long been based on morphology and function by non-invasive diagnostic modalities (e.g., echocardiography). In addition, despite extensive studies on biophysical, cellular, and animal models, the current approaches to the management of patients with cardiomyopathies remain largely unchanged.
Recent advances in genetics, proteomics, metabolomics, and microbiomics have broadened our understanding of the molecular and cellular pathophysiology of cardiovascular diseases including cardiomyopathies, which can potentially lead to more precise risk prediction and the discovery of optimal treatment strategies for individual patients. Also, novel approaches, such as artificial intelligence and machine learning to medical big data from various sources including clinical registries, electronic health records, biomarkers, medical images, and all spectrum of ‘omics’ data (e.g., genomic, proteomic, and metabolomic data), have huge potential for empowering personalized medicine for cardiomyopathy patients.
This Special Issue of the Journal of Personalized Medicine aims to highlight the current knowledge and future perspectives in the potential analytic approaches leading to personalized medicine for cardiomyopathies.
Dr. Yuji Nagatomo
Guest Editor
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Keywords
- cardiomyopathy
- personalized medicine
- genetics
- proteomics
- metabolomics
- microbiomics
- genome-wide association study
- single-nucleotide polymorphism
- artificial intelligence
- machine learning
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