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