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Epigenetics in Cardiovascular Disease
Provides a comprehensive overview of cardiovascular disease epigenetics, as well as instructions in research methods and advancing personalized healthcare
Yvan Devaux (Edited by), Emma Louise Robinson (Edited by)
9780128222584
Paperback, published 10 March 2021
498 pages, 80 illustrations (40 in full color)
23.4 x 19 x 3.1 cm, 0.97 kg
Approx.472 pages
SECTION I INTRODUCTORY INFORMATION 1. The ever-growing burden of cardiovascular disease 2. Epigenetics concepts: An overview 3. From classical signaling pathways to the nucleus SECTION II EPIGENETICS MECHANISMS IN CARDIOVASCULAR DISEASE 4. DNA methylation in heart failure 5. Histone modifications in cardiovascular disease initiation and progression 6. RNA modifications in cardiovascular disease—An experimental and computational perspective 7. Regulatory RNAs in cardiovascular disease 8. Regulation of splicing in cardiovascular disease 9. Cardiac transcriptomic remodeling in metabolic syndrome 10. Sex differences in epigenetics mechanisms of cardiovascular disease 11. Epigenetics in cardiac development and human induced pluripotent stem cells SECTION III BIOMARKER VALUE 12. Peripheral blood DNA and RNA biomarkers of cardiovascular disease in clinical practice 13. Epigenetics and physical exercise 14. Long noncoding RNAs and circular RNAs as heart failure biomarkers 15. Artificial intelligence in clinical decision-making for diagnosis of cardiovascular disease using epigenetics mechanisms SECTION IV THERAPEUTIC POTENTIAL 16. Therapeutic strategies for modulating epigenetic mechanisms in cardiovascular disease SECTION V METHODOLOGICAL ISSUES 17. Single-cell RNA sequencing in cardiovascular science 18. Good laboratory and experimental practices for microRNA analysis in cardiovascular research 19. Analytical challenges in microRNA biomarker development: Best practices for analyzing microRNAs in cell-free biofluids 20. Concept of biological reference materials for RNA analysis in cardiovascular disease 21. Unbiased bioinformatics analysis of microRNA transcriptomics datasets and network theoretic target prediction
Subject Areas: DNA & Genome [PSAK1], Genetics [non-medical PSAK]
