Biotechnology in Healthcare, Volume 1 Technologies and Innovations
Biotechnology in Healthcare, Technologies and Innovations, Volume One presents up-to-date knowledge on the emerging field of biotechnology as applied to the healthcare industry. Sections cover 3D printing, tissue engineering, synthetic biology, nano-biotechnology, omics, precision medicine, gene the...
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| Format: | eBook |
| Language: | English |
| Published: |
Chantilly
Elsevier Science & Technology
2022
Academic Press |
| Edition: | 1 |
| Subjects: | |
| ISBN: | 0323898378, 9780323898379 |
| Online Access: | Get full text |
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Table of Contents:
- Acknowledgments -- References -- 2 Three-dimensional printing in healthcare -- 2.1 Introduction -- 2.2 Three-dimensional printing technology (hardware and software) -- 2.3 Materials of three-dimensional printing -- 2.4 Three-dimensional printing in surgical planning and medical education -- 2.5 Three-dimensional printing in oral and maxillofacial surgery -- 2.6 Three-dimensional printing in orthopedics -- 2.7 Three-dimensional printing in neurosurgery -- 2.8 Bioprinting tissue and organ fabrication -- 2.9 Three-dimensional printing in pharmaceutical industry -- 2.10 Future of three-dimensional printing -- 2.10.1 Limitations of three-dimensional printings -- 2.11 Conclusion -- References -- 3 Synthetic biology in healthcare: technologies and applications -- 3.1 Introduction -- 3.1.1 Cell-free systems and applications -- 3.2 Technologies for synthetic DNA, proteins, and organisms -- 3.2.1 Synthetic biology-based Doggybone DNA technology and its uses in vaccines and DNA-based gene therapy products -- 3.2.2 Development of linear dbDNA vaccine construct -- 3.3 Gen9 technology-microfluidic devices and methods for gene synthesis -- 3.3.1 DNA synthesis and scale up (BioFab platform) -- 3.3.2 Technologies for synthetic genomes -- 3.3.3 Synthetic biology to create an artificial membrane-binding protein -- 3.3.4 Pathway rewiring with adapters and scaffolds -- 3.3.5 Synthetic DNA for developing new antibiotics -- 3.3.6 Synthetic DNA for amino-acid replacement -- 3.3.7 Synthetic proteins technologies -- 3.3.8 Technologies to create synthetic organisms -- 3.3.9 Synthetic biology applications in diagnostics -- 3.4 Transcriptional, posttranslational, and hybrid biosensing and applications -- 3.4.1 Transcriptional biosensing -- 3.4.2 Posttranslational biosensing -- 3.4.3 Hybrid biosensing -- 3.5 Applications -- 3.5.1 Paper-based diagnostic
- 6.2.3 Databases for noncoding genes -- 6.2.4 Databases used for annotation of human genetic variants and rearrangements -- 6.2.5 Prediction of gene function -- 6.3 Bioengineering, machine learning for personalized medicine -- 6.3.1 Principle of machine learning -- 6.3.2 Why machine learning? -- 6.3.3 Supervised machine learning -- 6.3.4 Unsupervised machine learning -- 6.3.5 Reinforcement learning -- 6.3.6 Online learning -- 6.3.7 Recommendations in machine learning -- 6.3.8 Testing and verification -- 6.4 Application of bioinformatics machine learning and in-depth data analysis -- 6.4.1 Get the data -- 6.4.2 Explore and prepare data -- 6.4.3 Feature selection with decision trees -- 6.4.4 Feature selection by analysis -- 6.4.5 Analysis with edgeR or DESeq2 -- 6.4.6 Pickup machine learning models -- 6.4.7 Evaluate machine learning model -- 6.4.8 Workflow for processing readings in RNA-seq -- 6.4.8.1 Preprocessing -- 6.4.8.2 Mapping -- 6.4.8.3 Analysis -- 6.5 BIG DATA -- Acknowledgments -- References -- 7 Omics applications in reproductive medicine -- 7.1 Genetic testing and molecular methods of female infertility -- 7.1.1 Molecular methods of transcriptome analysis in female infertility -- 7.1.2 Methods of metabolomics analysis of female infertility -- 7.1.3 Methods of proteomics analysis of female infertility -- 7.1.4 Molecular methods of microbial analysis of female infertility -- 7.1.5 Molecular methods of genomic analysis of female infertility -- 7.2 Genetic testing and molecular methods of male infertility -- 7.2.1 Molecular methods of transcriptome analysis of male infertility -- 7.2.2 Methods of metabolomics analysis of male infertility -- 7.2.3 Molecular methods of proteomic analysis of male infertility -- 7.2.4 Molecular methods of microbial analysis of male infertility -- 7.2.5 Molecular methods of genomic analysis of male infertility
- 4.5 Conclusion and future perspective -- References -- 5 Analysis and applications of sequencing in healthcare -- 5.1 Introduction -- 5.2 Method of de-novo and reference-based DNA sequencing -- 5.3 Generation of DNA reads -- 5.4 Quality assessment of reads -- 5.5 Trimming of DNA reads -- 5.6 Mapping of DNA reads -- 5.7 Assembly -- 5.8 Analysis of DNA sequences for marker-based surveillance of diseases -- 5.9 Phylomedicine of genetic diseases -- 5.10 Method of de-novo and reference-based RNA sequencing -- 5.11 Generation of short RNA reads and quality assessment -- 5.12 Trimming of RNA reads -- 5.13 Mapping of RNA reads -- 5.14 Assembly of RNA reads -- 5.15 Analysis of differential expression of genes in diseases states and in prognosis of disease -- 5.16 Analysis of alternative splicing of genes and gene fusion in disease states -- 5.17 Analysis of long noncoding RNA and its relevance to disease -- 5.18 Gene coexpression analysis and annotation of TF-TFBS and gene regulatory network -- 5.19 Method of DAP-sequencing and genome-wide annotation of cistrome -- 5.20 Analysis of DAP sequences and its application in healthcare -- 5.21 Genome-wide mapping of TF-TFBS and visualization of gene-regulatory network -- References -- 6 Innovative technologies in precision healthcare -- 6.1 Defining precision and personalized medicine -- 6.1.1 Assessing emerging technologies for personalized precision medicines' clinical trials -- 6.1.2 Biosensors in personalized medicine -- 6.1.3 Omics in precision healthcare -- 6.1.4 Engineering precision medicine technology and platforms -- 6.1.4.1 Processing of digital image data -- 6.1.4.2 Processing of sequenced data -- 6.1.4.3 Processing of numerical data -- 6.1.4.4 Development platforms -- 6.2 Databases applications in precision healthcare -- 6.2.1 Microbiome databases -- 6.2.2 Databases for protein-coding genes
- Front Cover -- Biotechnology in Healthcare, Volume 1 -- Copyright Page -- Dedication -- Contents -- List of contributors -- About the editor -- Preface -- 1 Overview of healthcare biotechnology -- 1.1 Introduction -- 1.2 Genomics -- 1.2.1 Genetic screening and testing -- 1.2.2 Diagnosis of genetic disorders -- 1.2.2.1 Polymorphism-based molecular makers -- 1.2.2.2 Polymerase chain reaction-based methods -- 1.2.2.3 Array based methods -- 1.2.2.4 Sequencing and its advances for genomics -- 1.2.2.5 Whole exome sequencing and clinical genome exome sequencing for genomic diagnostic -- 1.2.3 Pharmacogenomics and epigenomics -- 1.2.4 Personalized medicine -- 1.3 Transcriptomics -- 1.3.1 Tools of transcriptomics -- 1.3.1.1 Expressed sequence tag -- 1.3.1.2 Serial analysis of gene expression and cap analysis of gene expression -- 1.3.1.3 Microarrays -- 1.3.1.4 RNA-seq -- 1.3.1.5 Real-time polymerase chain reaction -- 1.3.2 Transcriptomics in disease diagnosis -- 1.3.3 Transcriptome profiling in drug discovery -- 1.4 Proteomics -- 1.4.1 Tools and techniques in the proteomics-based study -- 1.4.1.1 Purification of protein -- 1.4.1.2 Analysis of proteomes -- 1.4.1.3 Characterization of proteins -- 1.4.1.4 Sequence analysis -- 1.4.1.5 Quantification of proteomes -- 1.4.1.6 Structural analysis -- 1.4.1.7 Bioinformatics analysis -- 1.4.2 Biomarker discovery -- 1.4.3 Drug development -- 1.5 Metabolomics -- 1.5.1 Tools and techniques in metabolomics study -- 1.5.1.1 NMR spectroscopy -- 1.5.1.1.1 One-Dimensional-NMR -- 1.5.1.1.2 Two-dimensional-NMR -- 1.5.1.2 Mass spectrometry -- 1.5.2 Metabolomics in treatment of cancer, neurological, and psychiatric disorders -- 1.5.2.1 Metabolomics in cancer studies -- 1.5.2.2 Metabolomics in neurological disorders -- 1.5.2.3 Metabolomics in psychiatric disorders -- 1.5.3 Individualized metabolomics -- 1.6 Conclusion
- 3.5.2 Synthetic biology applications for drug discovery and therapy -- 3.5.3 Drug-target identification (synthetic pathways and systems) -- 3.5.4 Drug discovery -- 3.5.5 Therapeutic treatment (synthetic biology devices) -- 3.5.6 Therapeutic delivery -- 3.6 Synthetic biology for creating living systems to produce small molecules, for instance, aspirin, that characteristicall... -- 3.6.1 CodeEvolver-like protein-engineering synthetic-biology platform to create unique enzymes as therapeutics -- 3.6.2 Chimeric antigen receptor -- 3.6.3 Synthetic genomes and vaccine design (SARS-CoV-2 and other viruses) -- 3.7 Living therapies-engineering microbes and bacteriophage to treat disease -- 3.7.1 Engineered bacteria (such as Salmonella) to deliver vaccines -- 3.7.2 Understanding disease mechanism -- 3.7.3 Synthetic biology-based pathway engineering for pharmaceutical production -- 3.7.4 Constructing biosynthetic pathways -- 3.7.5 Optimizing pathway flux -- 3.7.6 Programming novel functionality and materials -- 3.7.7 Chemical retrosynthesis and its future applications in healthcare -- 3.8 Future challenges and conclusions -- Acknowledgment -- References -- 4 Nanotechnology in healthcare: nanoparticles for diagnostic and therapy -- 4.1 Introduction -- 4.2 Classification and properties of nanoparticles -- 4.2.1 Gold nanoparticles -- 4.2.2 Magnetic nanoparticles -- 4.2.3 Quantum dots -- 4.2.4 Carbon nanostructures -- 4.2.4.1 Carbon nanotubes -- 4.2.4.2 Graphene and graphene oxide -- 4.2.5 Polymeric nanoparticles -- 4.3 Nanoparticle-based biosensors for medical diagnosis -- 4.3.1 Plasmonic biosensors -- 4.3.2 QD-based biosensors -- 4.3.3 Carbon nanostructure-based biosensors -- 4.4 Nanoparticle-based therapy and imaging -- 4.4.1 Targeted drug delivery -- 4.4.2 Bioimaging and photothermal therapy -- 4.4.3 Nanoparticles in the clinic
- 7.3 Genetic testing and molecular methods of embryonic analysis and monitoring during the in vitro fertilization process