Precision Medicine in Stroke

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