Precision Medicine in Stroke

Description

This book provides a comprehensive coverage of the state of the art in precision medicine in stroke. It starts by explaining and giving general information about precision medicine. Current applications in different strokes types (ischemic, haemorrhagic) are presented from diagnosis to treatment. In addition, ongoing research in the field (early stroke diagnosis and estimation of prognosis) is extensively discussed. The final part provides an in-depth discussion of how different interdisciplinary areas like artificial intelligence, molecular biology and genetics are contributing to this area. Precision Medicine in Stroke provides a practical approach to each chapter, reinforcing clinical applications and presenting clinical cases. This book is intended for all clinicians that interact with stroke patients (neurologists, internal medicine doctors, general practitioners, neurosurgeons), students and basic researchers. Preface Contents Part I: Precision Medicine 1: Introduction References 2: Precision Medicine: Enabling Healthcare Progress in the Twenty-First Century 2.1 Introduction 2.2 New Trends in Genetic Diagnosis 2.3 The Advent of RNA Therapeutics, Gene Therapy, and Genome Editing 2.4 Precision Oncology 2.5 A New Frontier in Precision Medicine: The Microbiome 2.6 Conclusions and Outlook References 3: Do We Need Precision Medicine in Stroke? 3.1 Acute Ischemic Stroke Treatment 3.2 Diagnostic Strategies 3.3 Stroke Prevention 3.4 Conclusions References Part II: Current Applications of Precision Medicine in Ischemic Stroke 4: Monogenic Stroke Diseases 4.1 Introduction 4.2 Monogenic CSVD Diagnosis Challenges 4.3 CADASIL and NOTCH3 Gene Mutations 4.4 CARASIL- and HTRA1-Associated CSVD 4.4.1 CARASIL 4.4.2 Autosomal Dominant CSVD Associated with HTRA1 Heterozygous Mutations 4.5 COL41/COL4A2-Associated CSVD 4.5.1 CSVD Associated with Glycine and Stop Codon Mutations 4.5.2 PADMAL 4.6 CARASAL 4.7 RVCL 4.8 Perspectives References 5: Pharmacodynamics and Pharmacokinetics of Stroke Therapy 5.1 Introduction 5.2 General Principles of Stroke Therapy 5.3 Pharmacological Acute Stroke Therapy 5.3.1 Fibrinolytic Drugs 5.4 Secondary Preventive Therapy 5.4.1 Antiplatelet Drugs 5.4.1.1 Aspirin 5.4.1.2 Clopidogrel 5.4.2 Anticoagulant Drugs 5.4.2.1 Vitamin K Antagonists 5.4.2.2 Direct Oral Anticoagulants Direct Thrombin Inhibitor Dabigatran Direct Activated Factor X Inhibitors 5.5 Conclusion and Perspectives for the Future References 6: Current Applications of Precision Medicine in Stroke: Acute Stroke Imaging 6.1 Introduction 6.2 Imaging and Diagnosis 6.2.1 Etiology 6.2.2 Pathophysiology 6.2.3 The Core 6.2.3.1 In CT 6.2.3.2 In MR 6.2.4 The Clot 6.2.5 The Vessel Occlusion 6.2.5.1 CTA 6.2.5.2 MRA 6.2.6 The Collaterals 6.2.6.1 Venous Collaterals 6.2.7 Is There Tissue to Save? 6.2.7.1 Perfusion Evaluation 6.2.7.2 Perfusion Can Be Done in CT or in MR 6.2.8 Protocols 6.3 Imaging and Treatment 6.3.1 New Era Began 6.3.1.1 Criteria Selection for Thrombectomy 6.4 Still in Debate … 6.4.1 Previous mRS 6.4.2 Low ASPECTS 6.4.3 Low NIHSS 6.5 Tandem Occlusions 6.6 Intracranial Stenosis 6.7 Thrombolysis Before Thrombectomy or Not? 6.8 Stroke in COVID-19 Era 6.9 Conclusions References Part III: Current Applications of Precision Medicine in Haemorrhagic Stroke 7: Intracerebral Haemorrhage 7.1 Introduction 7.2 Aetiology 7.2.1 Macrovascular Lesions 7.2.2 Genetic Causes of ICH 7.2.3 Sporadic Cerebral Small Vessel Disease 7.3 Recurrence Risk and Secondary Prevention 7.3.1 Macrovascular Lesions 7.3.2 Cerebral Small Vessel Disease 7.3.3 Antithrombotic-Associated ICH 7.4 Prognostication 7.4.1 Clinical Prediction Models for Functional Outcome 7.4.2 Imaging Biomarkers: The Haematoma 7.4.3 Imaging Biomarkers: Cerebral Small Vessel Disease and ‘Brain Frailty’ 7.4.4 Non-imaging Biomarkers 7.5 Acute Treatment and Future Directions 7.5.1 Haematoma Expansion 7.5.2 Perihaematomal Oedema 7.5.3 The Role of Surgery 7.5.4 Precision Clinical Trials? 7.6 Conclusion References Part IV: Future Application 8: Blood Biomarkers in the Diagnosis of Acute Stroke 8.1 Introduction 8.2 Overview of Emerging Biomarkers for Risk Stratification, Diagnosis, and Etiological Classification in Acute Stroke 8.3 Blood Biomarkers in the Diagnosis of Acute Stroke: A Clinical Perspective 8.3.1 Background 8.3.2 Biomarkers for the Early Differentiation Between Acute Cerebrovascular Events and Mimicking Conditions 8.3.2.1 N-Methyl-d-Aspartate (NMDA) Receptor 8.3.2.2 Lipoprotein-Associated Phospholipase A2 (Lp-PLA2) 8.3.2.3 Heart-Type Fatty Acid-Binding Protein (HFABP) 8.3.2.4 Parkinson Disease Protein 7 (PARK 7) 8.3.3 Biomarkers for the Differentiation Between Ischemic and Hemorrhagic Stroke 8.3.3.1 Glial Fibrillary Acidic Protein (GFAP) 8.3.3.2 S100 Calcium-Binding Protein β (S100β) 8.3.4 Biomarkers for the Prediction of Clinical Severity and Complications in Acute Stroke 8.3.4.1 Matrix Metalloproteinase 9 (MMP-9) 8.3.4.2 Myelin Basic Protein (MBP) 8.3.5 Biomarkers for the Etiological Classification of Ischemic Cerebrovascular Events 8.3.5.1 Natriuretic Peptides (ANP/BNP) 8.3.5.2 D-Dimer 8.3.5.3 Interleukin-6 (IL-6) 8.3.5.4 Serum Neurofilament Light Chain (SNfL) 8.3.5.5 Fibrillin-1 8.3.6 Limitations of Biomarkers 8.3.7 Outlook: The Future of Biomarkers 8.4 Conclusion References 9: Future Application: Prognosis Determination 9.1 Introduction 9.2 Phenotyping of Cerebrovascular Diseases 9.3 Clinical Data 9.4 Molecular Biomarkers 9.4.1 Genomics 9.4.2 Transcriptomics 9.4.3 Proteomics 9.4.3.1 Ischemic Stroke 9.4.3.2 Intracerebral Hemorrhage 9.4.4 Metabolomics 9.4.5 Other Molecular Biomarkers 9.4.6 Cellular Markers 9.4.7 Integromics 9.4.8 System Biology 9.4.9 Conclusions 9.5 Markers Related to Clot Histopathological Composition 9.6 Neuroimaging Markers 9.6.1 Neuroimaging in Ischemic Stroke 9.6.1.1 Metabolic Imaging 9.6.1.2 Intra-arterial Thrombus/Clot Imaging 9.6.1.3 Collaterome 9.6.2 Neuroimaging in Intracerebral Hemorrhage 9.6.3 Imaging of Functional Connectivity in Ischemic and Hemorrhagic Stroke 9.6.4 Radiomics 9.6.5 Conclusion on Neuroimaging 9.7 Theranostic 9.8 Big Data and Data Integration 9.9 Artificial Intelligence and Machine Learning 9.10 Conclusion References Part V: Interdisciplinary Approach 10: Artificial Intelligence Applications in Stroke 10.1 Introduction 10.2 Using Machine Learning to Infer Predictive Models 10.2.1 Symbolic Methods 10.2.2 Statistical Methods 10.2.3 Similarity-Based Methods 10.2.4 Connectionist Methods 10.3 Deep Learning for Image Processing 10.4 Applications of Machine Learning in Stroke References 11: Registry-Based Stroke Research 11.1 Introduction 11.2 Registry-Based Studies 11.3 Types of Studies Generated from a Registry 11.4 Data Processing 11.5 Strengths and Limitations of Registry-Based Studies 11.5.1 Strengths 11.5.2 Limitations 11.6 Internal vs. External Validity 11.7 Bias 11.7.1 Categories of Bias 11.7.1.1 Selection Bias 11.7.1.2 Information Bias 11.8 Confounding 11.9 Local, National, and International Stroke Registries 11.10 The SITS Registry 11.11 Impact of SITS Data on Stroke Treatment 11.12 Benefits of Participation in the SITS Registry 11.13 Limitations of the SITS Registry 11.14 Management of a Large Data Set Derived from a Registry for Analysis 11.15 Who Can Get Access to SITS Database? 11.16 Novel Challenges in Registry-Based Research 11.17 Conclusions References 12: From Bedside to Bench: Methods in Precision Medicine 12.1 Introduction 12.2 Organs, Tissues, Cells 12.3 Proteins 12.3.1 Protein Purification 12.3.2 Protein Separation 12.3.3 Protein Identification and Quantification 12.3.4 Protein Sequence and Structure 12.4 Nucleic Acids 12.4.1 Nucleic Acid Extraction 12.4.2 Nucleic Acid Visualization and Quantification 12.4.3 Nucleic Acid Sequencing 12.5 Conclusion References 13: Approach for Genetic Studies 13.1 Introduction 13.2 Family Studies 13.3 Association Studies 13.4 Mandelman Randomising Functional Analysis 13.5 Genome-Wide Association Studies 13.6 Next-Generation Sequences and Gene Expiration 13.7 Pharmacogenetics in the Future 13.8 Genetics of Cerebral Venous Thrombosis References Part VI: Conclusion 14: Precision Medicine Versus Personalized Medicine 14.1 What Information Is Used to Consider Treatment and Care for an Individual Patient? 14.2 What Is the Patient’s Medical Problem? 14.3 The Patient and Their Environment 14.4 Treatment and the Role of Therapeutic Trials in Choosing Treatment for an Individual Patient References Index