Omics Approaches and Technologies in COVID-19

Front Cover
Omics Approaches and Technologies in COVID-19
Copyright
Dedication
Contents
Contributors
About the editors
Preface
Section A: Omics
	Chapter 1 Omics approaches in COVID-19: An overview
		1.  Introduction
		2.  Genomics, meta-genomics, and pan-genomics approaches
			2.1  Genomic approaches to covid-19
			2.2  Metagenomic approaches
			2.3  Pan-genomics
		3.  Genotype-phenotype correlations in COVID-19
			3.1  COVID-19 susceptibility genes identified by Genome-Wide Association Studies
			3.2  Protein coding SNPs associated with COVID-19
			3.3  Regulatory SNPs associated with COVID-19
			3.4  Implications of these genes in developing COVID-19 disease subphenotypes
			3.5  The path to develop disease prediction, detection, and therapeutic target
		4.  Proteomics in COVID-19
			4.1  Identification of protein level changes in various tissues
			4.2  Identification therapeutic targets
		5.  Host-pathogen protein—Protein interactions and interactomics
			5.1  Interaction of SARS-CoV2 proteins with host proteins
			5.2  Implication of protein interactions in developing therapy
		6.  Currently available COVID-19 management options
		7.  Transcriptomic approaches in COVID-19: From infection to vaccines
			7.1  Identification of target using RNA sequencing from patients’ blood, nasopharyngeal swab, and other tissues
			7.2  Single cell sequencing to identify genetic changes in COVID-19 patients
		8.  miRNAomics in COVID-19
		9.  Epigenetic implementations in COVID-19
			9.1  Whole genome methylation profiling of COVID-19 patients
			9.2  Other epigenetic approaches
		10.  Nutrigenomics and nutrition aspects in COVID-19
			10.1  Role of vitamins and other nutritional factors
			10.2  Involvement of gut as an important part of COVID-19 complications
			10.3  Microbial sequencing to identify gut microbes in COVID-19 patients
		11.  COVID-19 and phenomics
		12.  Metabolomics in COVID-19
			12.1  Metabolomic approaches to COVID-19 detection and prediction
		13.  Applications of genetic engineering in COVID-19
		14.  CRISPR-based assays for rapid detection of SARS-CoV2
		15.  Approaches to understand the emergence and dynamics of COVID-19 and future pandemics
		16.  Artificial intelligence (AI) in COVID-19
		17.  Applications of mathematical modeling and simulation in COVID-19
		18.  In silico disease modeling for COVID-19
		19.  System biology applications in COVID-19
			19.1  Cellular pathway involved for genes that are genetically associated with COVID-19
			19.2  Other genes that are identified using proteomic or interactomic studies
		20.  Computational approaches in COVID-19 drug discovery
		21.  Computational approaches in COVID-19 vaccine development
		22.  Applications of multiomics data in COVID-19
		23.  Publicly available resources in COVID-19 research and their applications
		24.  Emerging technologies for COVID-19 diagnosis, prevention, and management
		25.  Applications of digital and smart technologies to control SARS-CoV2 transmission, rapid diagnosis, and monitoring
		26.  Technologies for prediction of a patient’s health condition and outcomes from COVID-19
		27.  Conclusion and overall implications
		References
	Chapter 2 Genomics, metagenomics, and pan-genomics approaches in COVID-19
		1.  Introduction
			1.1  Genomic diversity
			1.2  Structural proteins
				1.2.1  Spike protein
				1.2.2  Nucleocapsid protein
				1.2.3  Envelope protein
				1.2.4  Membrane protein
			1.3  Accessory proteins
		2.  Metagenomic analysis
			2.1  Metagenomic next generation sequencing analysis
			2.2  Mutational, recombinational, and phylogenetic analysis
		3.  Covid-19 pangenome dynamics
			3.1  Statistics-WG sequencing-databases-CNBC-NCBI
			3.2  Pangenome diversity, genomic variation in the region
		4.  Application and advancement of pan genome-derived data in therapeutics
			4.1  Immunoinformatics
			4.2  Reverse genetic systems
			4.3  Reverse vaccinology and drug targets
			4.4  Tools for therapeutics development against COVID-19
		5.  Implementations of SARS-CoV-2 genomics, metagenomics, and pan-genomics approaches
			5.1  Decoding SARS-CoV-2 infection biology
			5.2  Biomarkers development for early diagnosis and screening of COVID-19 susceptible population
			5.3  Developing preventive strategy and formulation against SARS-CoV-2
				5.3.1  Drug development
			5.4  Developing therapeutics and management for mild and severe COVID-19 cases
				5.4.1  Implementations using genomics approaches
				5.4.2  Implementations using metagenomics approaches
				5.4.3  Implementations using pan-genomic approaches
			5.5  Individualized/personalized and targeted therapy and care to COVID-19 patients
			5.6  Mechanism of multiorgan injuries and their squeal
			5.7  Predicting the long-term health consequences and their treatment options in recovered patients
			5.8  Other implementations
		6.  Conclusion and future perspectives
		References
	Chapter 3 Genotype and phenotype correlations in COVID-19
		1.  Introduction
		2.  Structure and lifecycle of coronavirus
		3.  COVID-19 susceptibility genes identified by GWASs and implications of these genes in developing COVID-19 disease su ...
		4.  The cellular pathway of these SNPs/genes leading to COVID-19 subphenotypes
			4.1  Protein coding SNPs
			4.2  Regulatory SNPs
			4.3  SNPs in microRNAs and Linc RNAs
		5.  Genetic variations associated with susceptibility and severity to COVID-19
			5.1  Virus genome variation
			5.2  Human genetic changes and COVID-19
				5.2.1  ACE2
				5.2.2  TMPRSS2
				5.2.3  HLA polymorphisms
				5.2.4  IFN (interferons) and cytokines
				5.2.5  Furin, cathepsin L (CatL), adaptor-associated kinase 1 (AAK1) and cyclin G-associated kinase (GAK), phosphatidyl ...
			5.3  Inborn errors and congenital conditions and their associations with COVID-19
			5.4  Other factors
				5.4.1  Influence of blood group
				5.4.2  Sex and SARS-CoV-2
				5.4.3  Age
				5.4.4  Geographical influence on genetics
		6.  Epigenetic mechanisms of SARS-CoV-2 infection and associated comorbidities
		7.  Genetic variations and impact on diagnosis and treatment
		8.  Implementations of genotype-phenotype correlations in COVID-19
			8.1  Decoding infection biology
			8.2  COVID-19 and therapeutic treatment
			8.3  Individualized/personalized care to patients
			8.4  Multiorgan failure
			8.5  Short-term and potential long-term effects
		9.  Other implementations
			9.1  Conclusion and future perspectives
		References
	Chapter 4 Proteomic understanding of SARS-CoV-2 infection and COVID-19: Biological, diagnostic, and therapeutic perspectives
		1 .  Introduction
		2 .  Proteome of the SARS-CoV-2 virus
		3 .  Host-pathogen protein-protein interactions in COVID-19
			3.1  Proteomic profiling of SARS-CoV-2-infected host cells
			3.2  Protein-protein interactions
				3.2.1  SARS-CoV-2 interacts with host proteins to facilitate its life cycle
				3.2.2  SARS-CoV-2 interacts with the host replication, transcription, and translation machinery
				3.2.3  SARS-CoV-2 interacts with multiple innate immune pathways
				3.2.4  SARS-CoV-2 controls the host cell metabolism
		4 .  Proteomics in COVID-19 patients
			4.1  Blood proteomes of COVID-19 patients
			4.2  Blood proteomic predictors of COVID-19 severity or outcome
			4.3  Proteomics of childhood with multisystem inflammatory syndrome
			4.4  Proteomics landscape of end organs or autopsy
		5 .  Posttranslational modifications in COVID-19
			5.1  Phosphorylation
			5.2  Glycosylation
			5.3  Ubiquitination
			5.4  Other modifications
		6 .  Proteomics tools and applications for COVID-19
			6.1  Detection of SARS-CoV-2 infection
			6.2  Early detection of COVID-19 disease
			6.3  Target identification tools
		7 .  Implementations of SARS-CoV-2 and COVID-19 proteomics
			7.1  Targeting the spike-ACE2 interaction
			7.2  Targeting other SARS-CoV-2 proteins
			7.3  Targeting the host response
		8 .  Critical analyses of the present achievements and future perspectives
		References
	Chapter 5 Metabolites and metabolomics in COVID-19
		1 .  Introduction
			1.1  From metabolites to metabolomics
		2 .  Metabolites and metabolomics in viral infection
			2.1  Metabolomics and metabolites in Zika virus, dengue virus, and SARS-CoV infections
			2.2  Metabolome and metabolites in SARS-CoV-2
		3 .  Metabolomics in SARS-CoV-2
			3.1  Metabolomics studies with different biofluids and important findings related to COVID-19
			3.2  The relevance of saliva and gut metabolome for COVID-19
			3.3  The unique metabolic signature of SARS-CoV-2 as compared to other viral infections
			3.4  Metabolic signature of asymptotic, mild, and severe COVID-19
		4 .  Implementation of metabolites/metabolomics in COVID-19
			4.1  Biomarker development for early diagnosis and screening of COVID susceptible populations
			4.2  Developing preventive strategies and formulation of prophylactic agents/strategies
				4.2.1  Preexposure prophylaxis
				4.2.2  Postexposure prophylaxis
			4.3  Development of therapeutics and management for mild and severe COVID-19 cases
			4.4  Understanding the mechanism of multiorgan injuries and their sequelae
			4.5  Predicting long-term health consequences and treatment options in recovered patients
		5 .  Conclusions and future perspectives
		References
	Chapter 6 Host-pathogen protein-protein interactions and interactomics in COVID-19
		1.  Introduction
			1.1  Structure and key motifs of SARS-CoV-2 in PPI
				1.1.1  The virus arsenal of proteins
				1.1.2  The route of SARS-CoV-2 into the human cells
				1.1.3  SARS-CoV-2 and human PPI network
					Predicted PPI networks
					Experimental PPI networks
				1.1.4  Lessons from the interactome
		2.  Comparative interactomics: How the SARS-CoV-2 PPI differs from SARS-CoV-1 and other viruses
		3.  PPI-based pathway interactions in COVID-19
		4.  Interactome datasets and tools available to the community on COVID-19
		5.  Implementations of SARS-CoV-2 and human PPI/translational interactomics
			5.1  Understanding the comorbid conditions and COVID-19 interactions
			5.2  Identifying drug targets
		6.  The search for druggable targets through ACE2 protein-protein interaction networks (PPINs)
		7.  Bioinformatics-based HPI and their implementations
		8.  Comparative coronavirus interactomics and host targets
		9.  Drug repurposing
		10.  Understanding the mechanism of multiorgan injuries and their sequels
		11.  Critical analyses of the present achievements
		12.  Conclusion and future perspectives
		References
	Chapter 7 Currently available COVID-19 management options
		1.  Introduction
		2.  General treatment strategies
		3.  Specific treatments
		4.  Antiviral therapies
		5.  Anti-SARS-CoV-2 neutralizing antibody products
		6.  Immunomodulatory agents
		7.  Ventilation management and oxygenation in COVID-19
		8.  Management of critical cases
			8.1  Hemodynamic support
			8.2  Ventilatory support
			8.3  Therapeutic support
		9.  Supplementation
		10.  Vitamin D
		11.  Vitamin C
		12.  Zinc
		13.  Magnesium
		14.  Vitamin B12
		15.  Alpha-lipoic acid
		16.  Management of postinfection complications
		17.  Conclusions and future perspectives
		References
	Chapter 8 Transcriptomic approaches in COVID-19: From infection to vaccines
		1 .  Introduction
		2 .  The structural basis of SARS-CoV-2 transcriptome
		3 .  Transcriptional host responses to SARS-CoV-2 infection
		4 .  Implementations of transcriptomics in COVID-19
			4.1  Decoding SARS-CoV-2 infection biology
			4.2  Biomarker development for early diagnosis and screening of COVID-19 susceptible population
			4.3  Developing therapeutics and management for mild and severe COVID-19 cases
			4.4  Individualized/personalized and targeted therapy and care to COVID-19 patients
			4.5  Understanding the mechanism of multiorgan injuries and their squeal
			4.6  Predicting the long-term health consequences and their treatment options in recovered patients
			4.7  Vaccines
		5 .  Single-cell transcriptomics in COVID-19
		6 .  Conclusions and perspectives
		References
	Chapter 9 miRNAomics in COVID-19
		1 .  Introduction
		2 .  miRNA expression profile in COVID-19
		3 .  Interactions of SARS-CoV-2 miRNA/small RNAs and host miRNA
		4 .  SARS-CoV-2 encoded miRNAs/small RNAs in SARS-CoV-2 infection and COVID-19 pathology
		5 .  Host miRNA/small noncoding RNAs and COVID-19 pathology/severity/host response
		6 .  miRNA perspective of comorbid conditions and long-haul COVID
		7 .  Implementations of miRNAs in
		8 .  List of miRNA therapeutics in clinical trials
		9 .  Conclusion and future perspectives
		References
	Chapter 10 Epigenetic features, methods, and implementations associated with COVID-19
		1 .  Introduction
		2 .  Epigenetic landscape alteration and epigenetic mechanisms in respiratory viral infections
		3 .  Cutting-edge epigenetics and epigenomics technology applied in COVID-19
		4 .  Epigenetic landscape and mechanism in SARS-CoV-2 entry and infection
			4.1  Host epigenetics and SARS-CoV-2 infection
			4.2  Association of X inactivation with COVID-19
				4.2.1  DNA methylation overview
				4.2.2  DNA methylation and ACE2
				4.2.3  DNA methylation associated with immune regulators and SARS-CoV-2
				4.2.4  Other epigenetic associations of ACE2
			4.3  Epigenetic events of the immune system associated with COVID-19
			4.4  Histone citrullination and COVID-19
			4.5  Histone citrullination, stem cell pluripotency, and COVID-19
		5 .  Interactions between human epigenetic factors and SARS-CoV-2 proteins
		6 .  Epigenetic biomarkers for COVID-19 risk and severity
		7 .  Epitranscriptome profiling, technology, outcomes, and implementations in COVID-19
			7.1  Epitranscriptomics—Profiling and technology
			7.2 .  Epitranscriptomics—Outcomes and implementation in COVID-19
		8 .  Epitherapy and epidrug repurposing in COVID-19 clinical trials
		9 .  Implementations of SARS-CoV-2 epigenetics/epigenomics
			9.1  Decoding SARS-CoV-2 infection biology
			9.2  Biomarker development for early diagnosis and screening of the COVID-susceptible population
			9.3  Developing preventive strategies and formulation of prophylaxis agents/strategies
			9.4  Developing therapeutics and management for mild and severe COVID-19 cases
			9.5  Individualized/personalized and targeted therapy and care to COVID-19 patients
			9.6  Understanding the mechanism of multiorgan injuries and their squeal
			9.7  Predicting the long-term health consequences and their treatment options in recovered patients
			9.8  Other implementations
		10 .  Conclusion and future perspectives
		References
	Chapter 11 Nutrigenetics and nutrition aspects in COVID-19
		1 .  Introduction of nutrigenetics
			1.1  Nutriepigenetics
			1.2  Nutrigenetics and dietary signals
			1.3  Nutrigenetics and viral infections
		2 .  Gene-diet interaction and precision nutrition in COVID-19
		3 .  Various diet components and their cellular and molecular effects on COVID-19
			3.1  Macronutrients
				3.1.1  Proteins
				3.1.2  Fats
				3.1.3  Carbohydrates
			3.2  Micronutrients
				3.2.1  Vitamins
					Vitamin D
					Vitamin C
					Vitamin E
				3.2.2  Minerals
					Zinc
					Selenium
					Copper
			3.3  Prophylaxis and COVID-19
		4 .  Recent updates of nutrigenomic studies in COVID-19
			4.1  Interaction between nutrition, COVID-19, and microbiota
			4.2  Personalized diet for reducing COVID-19 effects
			4.3  Nutraceuticals and COVID-19
				4.3.1  Elderberries
				4.3.2  Hesperidin
				4.3.3  Lactoferrin
			4.4  Noncaloric dietary components
				4.4.1  Prebiotics
				4.4.2  Antioxidants
				4.4.3  Flavonoids
		5 .  Link of COVID-19 to low mortality of Asians compared to Americans and Europeans
		6 .  Conclusions and future perspectives
		References
	Chapter 12 COVID-19 phenomics
		1 .  Introduction to phenomics in the context of viral disease
		2 .  Phenomic approaches to COVID-19
			2.1  Phenome-wide association studies
				2.1.1  Sources of large-scale phenotypic data for PheWAS
					Electronic health records
				2.1.2  Statistical methods
					Testing independent associations
					Modeling
				2.1.3  Bioinformatic tools
				2.1.4  Applications of PheWAS to the study of COVID-19
					SARS-CoV-2 exposure PheWAS
					COVID-19 severity PheWAS
					COVID-19-associated genetic variant PheWAS
			2.2  Intensive phenomic studies
		3 .  Applications of phenomics to COVID-19
			3.1  Decoding SARS-CoV-2 infection biology
				3.1.1  Cytokine response
				3.1.2  Innate immune cells
				3.1.3  Adaptive immune cells
				3.1.4  Systemic effects
			3.2  Biomarker development for early diagnosis and screening of the COVID-susceptible population
				3.2.1  Cellular markers
				3.2.2  Cytokines as biomarkers
				3.2.3  Blood test parameters
					Circulating markers of inflammation and tissue damage
					Hepatic markers
					Renal markers
					Coagulation parameters
			3.3  Evaluating therapeutics and management strategies for mild and severe COVID-19 cases
				3.3.1  Therapeutic strategies evaluated in phenomic studies
				3.3.2  Therapeutic strategies and clinical interventions suggested by phenomic results
					Antibody therapy
					Clinical intervention
					Metabolic intervention
					Pharmacological intervention
			3.4  Prognostics to support individualized and targeted care for COVID-19 patients
				3.4.1  Individual risk factors
				3.4.2  Multivariate predictive models
					4C mortality score
					COVID@Spain
					COVID-GRAM
			3.5  Understanding the extent and mechanisms of multiorgan injuries
				3.5.1  Pulmonary impact
				3.5.2  Cardiac impact
				3.5.3  Renal impact
				3.5.4  Hepatic impact
				3.5.5  Pancreatic impact
				3.5.6  Neurological impact
			3.6  Understanding and predicting the long-term health consequences of COVID-19
				3.6.1  PASC associated with energy level and physical fitness
				3.6.2  Respiratory PASC
				3.6.3  Neuropsychiatric PASC
				3.6.4  Metabolic PASC
				3.6.5  Renal PASC
				3.6.6  Cardiovascular PASC
				3.6.7  Indirect effects
				3.6.8  Predicting long-term effects
				3.6.9  Outlook
		4 .  Concluding remarks
		References
	Chapter 13 Applications of genetic engineering in COVID-19
		1.  Introduction
		2.  SARS-CoV-2 protein production during the lifecycle emphasizes important posttranslation modifications
			2.1  Genome expression and evolution
			2.2  Host-membrane interaction is mediated by the spike protein
		3.  Application of subunit spike protein production
			3.1  Large-scale production of recombinant spike protein
				3.1.1  Trimeric spike protein
				3.1.2  Monomeric protein
				3.1.3  Spike protein subdomains
			3.2  Recombinant S protein for immunoassays
				3.2.1  Serological tests
				3.2.2  Neutralizing tests
				3.2.3  ELISA and similar tests
			3.3  Spike protein subunit vaccine
			3.4  Protein expression in the mRNA vaccine
				3.4.1  5′-Capping of mRNA
				3.4.2  Nucleoside modification
				3.4.3  Codon optimization
		4.  Production of recombinant virus and virus-like particles
			4.1  Production of virus-like particles
			4.2  Viral pseudoparticles
			4.3  Adenovirus vaccines
		5.  Genetically engineered models
			5.1  Cell lines
				5.1.1  Virus growth cell lines for isolating and propagating the virus
				5.1.2  Virus infectivity assays to study antiviral activity
				5.1.3  Neutralization assay for SARS-CoV-2
				5.1.4  Cell-based assay for drugs
			5.2  Mouse model
		6.  Synthetic biology of SARS-CoV-2
		7.  Concluding remarks
		Conflict of interest
		References
	Chapter 14 CRISPR-based assays for rapid detection of SARS-CoV-2
		1 .  Introduction
		2 .  SHERLOCK—CRISPR-Cas13a enzyme-based COVID-19 detection assay
		3 .  DETECTR—CRISPR-Cas12a enzyme-based detection assay
		4 .  AIOD-CRISPR—All-in-one dual CRISPR-Cas12a assay
		5 .  CRISPRENHANCE—Enhanced analysis of nucleic acids with crRNA extensions assay
		6 .  CASdetec—CRISPR-Cas12b-mediated DNA detection assay
		7 .  FELUDA—CRISPR-Cas9 enzyme-based detection assay
		8 .  Conclusion
		References
Section B: Artificial intelligence and bioinformatics
	Chapter 15 Emergence and dynamics of COVID-19 and future pandemics
		1 .  Differentiating the disease and infection
		2 .  The weight of preconceived ideas
		3 .  The example of COVID-19
		4 .  The “medical approach”
		5 .  The “environmental approach”
		6 .  The “laboratory leak narratives”
		7 .  The “Circulation” model and the evolution of viruses in the human population
		8 .  The genetic accident
		9 .  The societal accident
		10 .  An intermediate summary
		11 .  What can be performed to prevent the occurrence of further pandemics?
		12 .  Conclusion: The solution is in the societal management
		References
	Chapter 16 Artificial intelligence in COVID-19
		1 .  Introduction
		2 .  Background
			2.1  SARS-CoV-2 virology
			2.2  COVID-19 testing methods
			2.3  Chest X-rays and computed tomography background
			2.4  Metrics
		3 .  AI implementations for COVID-19
			3.1  SARS-CoV-2 therapeutics
			3.2  COVID-19 diagnostics
				3.2.1  Introduction
				3.2.2  Transfer learning
				3.2.3  Image generation
				3.2.4  Anomaly detection
				3.2.5  Segmentation
				3.2.6  Result interpretability
				3.2.7  Nonimage approaches
			3.3  COVID-19 mass testing
		4 .  Conclusion
		References
	Chapter 17 Applications of mathematical modeling and simulation in COVID-19
		1 .  The basic principles and applications of mathematical modeling and simulation in pandemics
		2 .  Data sets and various mathematical models applied to COVID-19
		3 .  Implementations of modeling and simulation
			3.1  To predict future COVID-19 outbreaks
			3.2  The effects of new variants of SARS-CoV-2 in the modeling of the spread of COVID-19
			3.3  To assess the effects on the at-risk population
			3.4  To forecast cases, severity, secondary infections, and deaths
			3.5  Predicting long-term health consequences
			3.6  Resource allocation for supportive staff and clinical beds
			3.7  To understand transmission dynamics and to cut the transmission chain
			3.8  Impacts of travel restrictions, social distancing, and early detection
			3.9  Efficacy of the prophylaxis agents in preventing the SARS-CoV-2 infection
			3.10  To understand optimum interventions
			3.11  To understand the impact of currently available vaccines
			3.12  To understand economic impacts in various sectors
		4 .  Limitations and potential challenges of modeling and simulation in COVID-19
		5 .  Conclusions and future perspectives
		References
	Chapter 18 In silico disease modeling for COVID-19
		1 .  Introduction: COVID-19 models
		2 .  In silico modeling of infectious disease: Types of models and biological implications
		3 .  In silico modeling of SARS-COV-2 dynamics within the host
		4 .  Implementations of in silico modeling of COVID-19
			4.1  Decoding SARS-CoV-2 infection biology
			4.2  Biomarker development for early diagnosis and screening of the COVID-susceptible population
			4.3  Developing a preventive strategy and the formulation of prophylaxis agents/strategies
			4.4  Developing therapeutics and management for mild and severe COVID-19 cases
			4.5  Understanding the mechanism of multiorgan injuries and their sequelae
		5 .  Conclusions
		References
	Chapter 19 Systems biology in COVID-19
		1 .  Introduction
		2 .  Strategies, tools, DBs, and other publicly available resources for COVID-19 systems biology and phenomics
			2.1  Literature databases and data portals
			2.2  Repositories of curated COVID-19 and SARS-CoV-2 data, tools, and workflows
			2.3  COVID-19 simulators and data-driven phenomics resources: Real-time epidemiology
		3 .  Implementations of systems biology in COVID-19 in basic and translational research
			3.1  Decoding SARS-CoV-2 infection biology, multisystem involvement, and its sequelae
			3.2  Biomarker development for early diagnosis and screening of COVID-19 susceptible populations
		4 .  Systems pharmacology approaches in identifying the targetome, therapeutics, and prophylactic agents for COVID-19
			4.1  Systems vaccinology approaches in COVID-19 prevention and prophylaxis
			4.2  Developing therapeutics and management for mild and severe COVID-19 cases
			4.3  Predicting long-term health consequences and their treatment options in recovered patients
		5 .  Other implementations: Big data, phenomics, and radiomics
		6 .  Critical analyses of the present achievements
		7 .  Conclusion and future perspectives
		References
	Chapter 20 Computational approaches for drug discovery against COVID-19
		1 .  Introduction
		2 .  Computational approaches to identify drug targets in SARS-CoV-2 and humans
			2.1  Signature matching
			2.2  Genomic association analysis
			2.3  Pathway or network mapping
			2.4  PPI-based targets
			2.5  Other omics (transcriptome, epigenome, and so on)-based targets
			2.6  Key drug targets in SARS-CoV-2 and humans
		3 .  In silico approaches to identify candidate drugs
			3.1  Rigid docking
			3.2  Pharmacophore approach
			3.3  Virtual screening
			3.4  Molecular dynamics and simulation
			3.5  Machine learning and artificial intelligence
			3.6  Big data-based approach for COVID-19
		4 .  Potential anti-COVID drugs based on computational approaches
			4.1  Based on high-throughput screening of compounds
			4.2  Based on high-throughput screening of phytochemicals (medicinal plants)
			4.3  Based on peptide designing
			4.4  Drug repurposing approach of FDA-approved drugs using computational approaches
			4.5  Based on computer-aided drug combinations
			4.6  Based on machine learning and artificial intelligence
		5 .  Identification of prophylaxis and COVID-19 management agents (such as NO etc.) using in silico approaches
			5.1  In silico design of SARS-CoV-2-specific therapeutic antibodies
		6 .  In silico disease (COVID-19) modeling for testing the efficacy of candidate therapeutics
		7 .  Critical analyses of the present achievements
		8 .  Conclusion and future perspectives
		References
	Chapter 21 Computational approaches in COVID-19 vaccine development
		1 .  Why is the vaccine the best way to fight COVID-19?
		2 .  Steps in the development of conventional vaccines and their difficulties and disadvantages
		3 .  How the computational approaches speed up the vaccine design and development process
		4 .  The concept of computational immunology and the available resources
		5 .  Computational immune proteomics approach in COVID vaccine design and outcomes
		6 .  Reverse vaccinology-based approach in COVID vaccine design and outcomes
		7 .  Identified epitope and multiepitope-based peptide vaccines against COVID-19
		8 .  Computer-aided mRNA vaccine design against COVID-19
		9 .  Computer-aided DNA vaccine design against COVID-19
		10 .  Artificial intelligence and systems biology approaches in COVID-19 vaccine development
		11 .  Computational approaches for rapid design, development, and testing the efficacy of COVID-19 vaccines
		12 .  Computational approaches applied in various vaccine development platforms for COVID-19
		13 .  The currently available vaccines and the computational approaches behind these vaccines
			13.1  Computational approaches for designing an mRNA-based SARS-CoV-2 vaccine
		14 .  Computational approaches to validate the efficacy of the developed vaccines against COVID-19
		15 .  Tools and applications used in tracking the COVID-19 vaccination regime
		16 .  Publicly available resources for COVID-19 vaccine discovery
		17 .  Critical analyses of the present achievements
		18 .  Conclusion and future perspectives
		References
	Chapter 22 Applications of multiomics data in COVID-19
		1 .  Omics and multiomics approaches: Data, data integration, and analysis
		2 .  Multiomics approaches to decode SARS-CoV-2 infection biology
		3 .  Multiomics approaches to understand how nCoV hijacks the host cell machinery
		4 .  Multiomics approaches to understand the comorbid conditions and COVID-19 interactions
			4.1  Diabetes and COVID-19
			4.2  Hypertension and COVID-19
			4.3  Cancers and COVID-19
			4.4  Cardiovascular diseases and COVID-19
		5 .  Multiomics approaches for personalized and targeted therapy and care to COVID-19 patients
		6 .  Early diagnosis and multiomics-based screening of biomarkers for the COVID-susceptible population
			6.1  Genomics and transcriptomics-based biomarkers
			6.2  Metabolite-based biomarker
			6.3  Proteomics-based biomarker
		7 .  Developing a preventive strategy and multiomics approach toward the formulation of prophylaxis agents/strategies
		8 .  Developing therapeutics and management for mild and severe COVID-19 cases
		9 .  Multiomics approaches toward multiple organ injury and response because of COVID-19
		10 .  Multiomics-based prediction of long-term health consequences and their treatment options in recovered patients
		11 .  Conclusion
		References
	Chapter 23 Publicly available resources in COVID-19 research and their applications
		1 .  Introduction
		2 .  Literature resources
		3 .  Epidemiology resources
		4 .  SARS-CoV-2-specific genomic databases and tools for analysis
		5 .  SARS-CoV-2-specific proteomic databases and tools for analysis
		6 .  SARS-CoV-2-specific epigenetic databases and tools for analysis
		7 .  SARS-CoV-2-specific transcriptomic databases and tools for analysis
		8 .  In vivo and clinical trial databases for COVID-19 drugs
		9 .  Bioinformatics tools and databases for SARS-CoV-2 drug designing and vaccine developments
		10 .  Toxicogenomic databases and tools for SARS-CoV-2 research
		11 .  Resources and tools for clinicians
		12 .  Mobile apps for tracking the pandemic
		13 .  Conclusion and future perspectives
		References
Section C: Smart and emerging technologies
	Chapter 24 Emerging technologies for COVID-19, diagnosis, prevention, and management
		1.  Introduction
			1.1  SARS-CoV-2 and COVID-19 epidemiology
			1.2  Clinical signs and symptoms
		2.  Emerging technologies for diagnosing SARS-CoV-2
			2.1  Novel molecular technologies to diagnose COVID-19
				2.1.1  Nanopore sequencing
				2.1.2  LamPORE
				2.1.3  CRISPR-Cas technology
			2.2  Rapid and point-of-care diagnostic technologies for COVID-19
				2.2.1  Nanobiosensor‐based diagnostic assays
				2.2.2  Electric field-driven microfluidic CRISPR assay
				2.2.3  Lateral flow assay
			2.3  Artificial intelligence for early and prompt diagnosis of COVID-19
			2.4  Rapid data sharing for diagnostics
		3.  Emerging technologies for studying the COVID-19 epidemiology
			3.1  AI and mathematical modeling for epidemiology and prediction of a future pandemic
		4.  Technologies for prevention and control the SARS-CoV-2 transmission
		5.  Emerging technologies for therapeutic and vaccine development
			5.1  AI and in silico investigations in drug and vaccine development for SARS-CoV-2
			5.2  Nanomedicine
			5.3  Novel vaccine development technologies
			5.4  Monoclonal antibodies
			5.5  Plasma therapy
			5.6  Telemedicine
		6.  Conclusion and future perspectives
		References
	Chapter 25 Applications of digital and smart technologies to control SARS-CoV-2 transmission, rapid diagnosis, and monitoring
		1.  Introduction
			1.1  The state of art in smart/digital tech
			1.2  AI, IoT, nanomaterials, big data, electronics
			1.3  SARS-CoV-2 transmission and importance of rapid diagnosis and monitoring
		2.  Digital and smart technologies used in the COVID pandemic
			2.1  Mechanical and automation systems
				2.1.1  Autonomous vehicles
					Drones
						Disinfectant drone
						Delivery drone
					Robots
						Telerobots
						Collaborative robots
						Social robots
						Autonomous
			2.2  Digital technologies
				2.2.1  Virtual reality
				2.2.2  Cloud computing
				2.2.3  Artificial intelligence
				2.2.4  Online social networks
			2.3  Smart technologies
				2.3.1  Smart wearables
				2.3.2  Smart thermometers
				2.3.3  Smart helmets
				2.3.4  Smart clothes
				2.3.5  Smart cities
		3.  Implementation of digital and smart technologies in COVID-19
			3.1  Community screening
			3.2  Identification of the infected
			3.3  New strains
			3.4  Monitoring quarantine and self-isolation
				3.4.1  Remote-based monitoring
					Image-based monitoring
					Symptom-based monitoring
					Telemedicine
				3.4.2  Mobile-based applications
			3.5  Monitoring contact tracing
			3.6  Smart lockdowns and release
			3.7  Clinical management
				3.7.1  Rapid diagnosis
					Machine learning-based methods
						Image processing (X-ray, computed tomography, computed ultrasonography)
						Symptom-based data
					Telemedicine
					Developed apps and software platforms
					New materials (nanotechnology)
					Biosensors
					Robots
						Telerobots
						Collaborative robots
						Social robots
						Autonomous robots
				3.7.2  Prognosis
				3.7.3  Vaccine management
		4.  Technologies for Integration of various sectors (clinical, environmental, public and private, hospitals, health ca ...
		5.  Emerging smart and digital tech for research purposes
			5.1  For surveillance and planning
			5.2  For delivery treatment and vaccine
		6.  Comparison of smart and digital techs implemented for pandemic management between first and third world economic co ...
		7.  Advantages, disadvantages, and risks of the currently used smart and digital technologies in the COVID pandemic
		8.  Ethical perspective and steps to ensure data safety
			8.1  Critical analyses of the present achievements
		9.  Conclusion and future perspectives
		References
	Chapter 26 Application of big data analytics in the COVID-19 pandemic: Selected problems
		1 .  Introduction
		2 .  Challenges in COVID-19 and big data
			2.1  Data collection and access
			2.2  Data use
			2.3  Data sharing
			2.4  Information correctness
			2.5  Data protection
			2.6  Patient cooperation
		3 .  Recommendations
		4 .  Conclusion
		Acknowledgments
		References
Index
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