Electrochemical Biosensors for Whole Blood Analysis

Foreword
Contents
1 Introduction
	1.1 Blood Analysis as a Powerful Database
	1.2 Electrochemical Biosensors for Endogenous Substances Detection in Whole Blood
	1.3 Electrochemical Biosensors for Exogenous Substances Detection in Whole Blood
	1.4 Conclusion and Outlook
	References
2 Electrochemical Biosensors and the Signaling
	2.1 Introduction
	2.2 Electrochemical Methods
		2.2.1 Voltammetry/Amperometry
		2.2.2 Electrochemical Impedance Spectroscopy (EIS)
		2.2.3 Photoelectrochemical and Electrochemluminescent (ECL) Biosensors
	2.3 Biorecognition Elements for Electrochemical Biosensors
		2.3.1 Aptamers
		2.3.2 DNAzymes
		2.3.3 Peptides
		2.3.4 Peptide Nucleic Acids (PNAs)
		2.3.5 Proteins
		2.3.6 Antibodies
		2.3.7 Enzymes
		2.3.8 Lectins
		2.3.9 Epitopes
		2.3.10 Molecularly Imprinted Polymers (MIPs)
	2.4 Conclusion and Outlook
	References
3 Electrochemical Biosensors for Ions Detection
	3.1 Introduction
	3.2 Cu2+ Detection
	3.3 Hg2+ Detection
	3.4 Pb2+ Detection
	3.5 Simultaneous Detection of Cl−, Na+ and K+
	3.6 Zn2+ Detection
	3.7 Simultaneous Detection of Cu2+, Pb2+, Hg2+, and Cd2+
	3.8 As3+ Detection
	3.9 Cr6+ Detection
	3.10 Ca2+ Detection
	3.11 Inorganic Phosphate Detection
	3.12 Nitrite and Nitrate Detection
	3.13 Blood Urea Nitrogen Detection
	3.14 Conclusion
	References
4 Electrochemical Biosensors for Neurotransmitters Detection
	4.1 Introduction
	4.2 Electrochemical Biosensors for Mono-NTS
		4.2.1 Epinephrine (EP)
		4.2.2 Dopamine (DA)
		4.2.3 Norepinephrine (NEP)
		4.2.4 Γ-Aminobutyric Acid (GABA)
		4.2.5 Serotonin (ST)
	4.3 Simultaneous Detection of Multi-NTs
	4.4 Conclusion and Outlook
	References
5 Electrochemical Biosensors for Metabolites Detection
	5.1 Introduction
		5.1.1 Biosensors for Blood Metabolites Detection
		5.1.2 Glucose Detection
		5.1.3 Urea Detection
		5.1.4 Uric Acid Detection
		5.1.5 Bilirubin Detection
		5.1.6 Cholesterol Detection
		5.1.7 Lactic Acid Detection
		5.1.8 Creatinine Detection
		5.1.9 Galactose Detection
		5.1.10 Triglyceride Detection
		5.1.11 Hydrogen Peroxide Detention
	5.2 Conclusion
	References
6 Electrochemical Biosensors for Drugs Detection
	6.1 Introduction
	6.2 Nanomaterials-Based Electrochemical Biosensors
		6.2.1 Zero-Dimensional Nanoparticles-Based Electrochemical Biosensors
		6.2.2 One-Dimensional Nanotubes-Based Electrochemical Biosensors
		6.2.3 Two-Dimensional Nanosheets-Based Electrochemical Biosensors
		6.2.4 Three-Dimensional Nanomaterials-Based Electrochemical Biosensors
	6.3 Enzymatic Electrochemical Biosensors
	6.4 Conclusion and Outlook
	References
7 Electrochemical Biosensors for Amino Acids Detection
	7.1 Introduction
		7.1.1 Amino Acids
		7.1.2 Significance of Amino Acid Detection
		7.1.3 Common Methods for Amino Acid Detection
	7.2 Application of Electrochemical Biosensors for Amino Acid Detection
		7.2.1 Electrochemical Biosensors for Essential Amino Acid Detection
		7.2.2 Electrochemical Biosensors for Non-essential Amino Acid Detection
	7.3 Conclusions
	References
8 Electrochemical Biosensors for Nucleic Acids Detection
	8.1 Introduction
	8.2 Design Principles of Electrochemical Biosensors for Nucleic Acid Detection
	8.3 Biosensors with DNA Nanostructures Probes
		8.3.1 1D DNA Probes
		8.3.2 PNA Probes
		8.3.3 2D DNA Probes
		8.3.4 3D DNA Probes
	8.4 Conclusion and Outlook
	References
9 Electrochemical Biosensors for Hepatic and Cardiac Biomarkers Detection
	9.1 Introduction
		9.1.1 Biomarkers of Myocardial Necrosis/Myocardial Damage
		9.1.2 Predictive Biomarkers of Cardiovascular Events
		9.1.3 Biomarkers of Hepatitis
		9.1.4 Cardiac Troponins
		9.1.5 CK
		9.1.6 CRP
		9.1.7 Hepatitis Biomarkers
		9.1.8 Conclusion and Outlook
	References
10 Electrochemical Biosensors for Inflammatory Biomarkers Detection
	10.1 Brief Introduction of Inflammatory Biomarkers
		10.1.1 Inflammatory Biomarkers
		10.1.2 Inflammatory Biomarkers in Whole Blood
	10.2 Principles for Inflammatory Biomarkers Detection by Electrochemical Biosensors
		10.2.1 Transducer Elements-Electrode
		10.2.2 Biorecognition Elements
		10.2.3 Electrochemical Methods
	10.3 Electrochemical Biosensors for Inflammatory Biomarkers Detection in Whole Blood
		10.3.1 Potentiometry
		10.3.2 Voltammetry
		10.3.3 Electrochemical Impedance Spectroscopy (EIS)
		10.3.4 Conductometry
	10.4 Bioanalytical and Clinical Applications
		10.4.1 Analysis and Application in the Diagnosis of Bacterial or Viral Infections
		10.4.2 Analysis and Application in Malignant Tumors
		10.4.3 Analysis and Application in Cardiovascular Disease
		10.4.4 Analysis and Application in Autoimmune Diseases
		10.4.5 Analysis and Application in the Area of Organ Transplantation
		10.4.6 Analysis and Application in Obstetrics and Gynecology
	10.5 Conclusions and Perspectives
	References
11 Electrochemical Biosensors for Tumor Biomarkers Detection
	11.1 Introduction
	11.2 Tumor Biomarkers and Cancers
	11.3 Electrochemical Biosensors for the Detection of Tumor Markers
		11.3.1 Electrochemical Biosensor Principles
		11.3.2 Electrochemical Biosensors for Tumor Biomarker Detection
	11.4 Conclusion and Outlook
	References
12 Electrochemical Biosensors for Virus Detection
	12.1 Introduction
	12.2 Bio-recognition Probes
		12.2.1 Nucleic Acids
		12.2.2 Antibodies
		12.2.3 Peptides
	12.3 Electrode Interface Nanomaterials
		12.3.1 Gold-Based Nanomaterials
		12.3.2 Carbon-Based Nanomaterials
		12.3.3 Magnetic Nanoparticles
	12.4 Conclusion
	References
13 Electrochemical Biosensors for Cells and Cell Exosomes Detection
	13.1 Introduction
	13.2 Bio-recognition Probes
		13.2.1 Nucleic Acids
		13.2.2 Antibodies
		13.2.3 Peptides
	13.3 Electrode Interface Nanomaterials
		13.3.1 Metal Nanomaterials
		13.3.2 Carbon Nanomaterials
		13.3.3 Magnetic Nanoparticles
		13.3.4 Metal–Organic Framework Nanomaterials
	13.4 Conclusion
	References
14 Electrochemical Biosensor with Machine Learning
	14.1 Introduction
	14.2 Backgrounds of Basic Machine Learning Techniques
		14.2.1 Supervised Learning
		14.2.2 Unsupervised Learning
		14.2.3 Semi-supervised Learning
		14.2.4 Reinforcement Learning
		14.2.5 Active Learning
	14.3 Machine-Learning-Driven Electrochemical Biosensor Data Analysis
	14.4 Conclusion
	References