Advances in clinical chemistry. Volume 107 /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Makowski, Gregory S. (Gregory Stephen) (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: [Place of publication not identified] : Academic Press, 2022.
Colección:Advances in clinical chemistry ; volume 107
Temas:
Clinical chemistry.
Chemistry, Clinical
Chimie clinique.
Clinical chemistry
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • Advances in Clinical Chemistry
  • Copyright
  • Contents
  • Contributors
  • Preface
  • Chapter One: Advances in quantum dots as diagnostic tools
  • 1. Introduction
  • 2. Quantum dots: Synthesis, functionalization and characterization
  • 2.1. Synthetic procedures
  • 2.1.1. Top-down processing methods
  • 2.1.2. Bottom-up processing methods
  • 2.2. Solubilization and functionalization
  • 2.2.1. Aqueous stabilization of QDs
  • 2.2.2. Bioconjugation of QDs
  • 2.3. Analytical tools for QD characterization
  • 2.3.1. Morphological information
  • 2.3.2. Crystallographic information
  • 2.3.3. Chemical composition
  • 2.3.4. Surface characterization
  • 2.3.5. Bioconjugation efficiency
  • 3. Quantum dots for point-of-care (POC) diagnosis
  • 3.1. POC devices: Role of QDs
  • 3.2. Optical and electrochemical methods for QD biosensing tag detection
  • 3.3. POC systems based on QD barcode technology
  • 3.4. Paper-based POC diagnostic devices using QDs
  • 3.4.1. Lateral-flow principles: Optical detection
  • 3.4.2. Smartphone integration
  • 3.4.3. Paper-based devices with electrochemical detection
  • 3.4.4. Emerging nanopaper platforms
  • 4. Ultrasensitive biomarker detection based on quantum dots
  • 5. Bioconjugated quantum dots for molecular imaging
  • 5.1. Bioconjugated QDs for fluorescence molecular imaging
  • 5.2. Near-infrared optical imaging
  • 5.3. Multimodal imaging
  • 6. Summary and future perspectives
  • Acknowledgments
  • References
  • Chapter Two: Host polymorphisms and COVID-19 infection
  • 1. Introduction
  • 2. Genetic polymorphisms affecting the angiotensin-converting enzyme 2 expression (Table 1)
  • 2.1. Angiotensin-converting enzyme 2
  • 2.2. Transmembrane serine protease 2
  • 2.3. Androgen receptor
  • 2.4. A disintegrin and metalloproteinase 17
  • 2.5. Angiotensin-converting enzyme 1.
  • 3. Genetic polymorphisms affecting the immune response against COVID-19 (Table 2)
  • C-C chemokine receptor 5
  • 3.2. Complement C3
  • 3.3. Interferon-induced transmembrane protein 3
  • 3.4. Tumor necrosis factor-alpha
  • 3.5. Toll-like receptor 7
  • 3.6. Apolipoprotein E
  • 3.7. Vitamin D binding protein
  • 3.8. Dipeptidyl peptidase
  • 3.9. Glutathione S-transferase theta 1
  • 4. Associated genetic polymorphisms (Table 3)
  • 4.1. ABO blood group
  • 4.2. Human leukocyte antigen system
  • 4.3. Haplogroup R
  • 4.4. Neanderthal DNA
  • 5. Conclusion
  • References
  • Chapter Three: Clinical metabolomics for inborn errors of metabolism
  • 1. Introduction
  • 2. Overview of metabolomics
  • 3. Overview of inborn error of metabolism laboratory diagnosis
  • 4. Analytical methodology
  • 4.1. Sample extraction
  • 4.2. Analytical instrumentation and methods
  • 4.3. The importance of library-based bioinformatics methodology
  • 4.4. Authentic standard library
  • 4.5. Quality control and data alignment
  • 4.6. Data curation
  • 5. Computations for reference population comparisons
  • 6. Bridging to reference populations and other analytical considerations
  • 6.1. Bridging to the reference population
  • 6.2. Process quality control
  • 6.3. Compound performance criteria
  • 6.3.1. Analytical performance
  • 6.3.1.1. Precision studies
  • 6.3.1.2. Platform correlation studies
  • 6.3.1.3. Median ratio studies
  • 6.3.1.4. Compound stability studies
  • 6.3.2. Biological relevance
  • 6.4. Preanalytical sample quality assessment
  • 6.5. Reporting and limitations
  • 7. Clinical utility of metabolomics for the identification of IEMs
  • 7.1. Application in rare disease profiling
  • 7.2. Rare disease characterization and therapeutic monitoring
  • 7.3. Adjunct to exome sequencing
  • 8. Future applications and challenges
  • 9. Conclusions
  • References.
  • Chapter Four: Amino acids in inflammatory bowel diseases: Modern diagnostic tools and methodologies
  • 1. Introduction
  • 2. Inflammatory bowel diseases
  • 2.1. Risk factors of IBD
  • 2.2. Therapy of inflammatory bowel disease
  • 3. Biomarkers of inflammatory bowel disease
  • 4. Amino acids as biomarkers and their role in inflammatory bowel disease
  • 5. Analytical strategies for analysis of amino acids in biological samples
  • 5.1. Sample preparation
  • 5.1.1. Solid-phase extraction and solid-phase microextraction
  • 5.1.2. Liquid-liquid extraction
  • 5.1.3. Protein precipitation
  • 5.1.4. Electrophoretic preconcentration
  • 5.1.5. Derivatization
  • 5.2. Chromatographic techniques
  • 5.2.1. Detection in liquid chromatography
  • 5.2.2. Practical applications of chromatographic methods in bioanalysis of amino acids
  • 5.3. Capillary electrophoresis
  • 5.3.1. Detection in capillary electrophoresis
  • 5.3.2. Practical applications of electromigration methods in bioanalysis of amino acids
  • 6. Current studies of amino acids in IBD
  • 7. Conclusion
  • Acknowledgments
  • Conflict of interest
  • References
  • Chapter Five: Advances in sports genomics
  • 1. Introduction
  • 2. Genome-wide association (GWAS) and whole genome sequencing (WGS) studies in sport
  • 2.1. Genome-wide association studies
  • 2.2. Whole genome sequencing study
  • 3. Gene variants for endurance athlete status
  • 4. Gene variants for power athlete status
  • 5. Gene variants for strength athlete status
  • 6. Gene variants for soft tissue injuries
  • 7. Conclusion
  • References
  • Chapter Six: Biomarkers in muscle invasive bladder cancer
  • 1. Introduction
  • 2. Current treatment of MIBC
  • 2.1. Cystectomy versus radiotherapy
  • 2.2. Radiosensitizing agents
  • 2.3. Neoadjuvant and adjuvant therapy
  • 3. The unmet need for robust biomarkers in MIBC
  • 4. Urinary biomarkers.
  • 5. Gene expression signatures predicting lymph node metastasis
  • 6. Biomarkers of anti-tumor immunity
  • 6.1. PD-1/PD-L1
  • 6.2. Lymphocytopenia
  • 7. Hypoxia biomarkers
  • 8. Biomarkers of DNA damage repair
  • 8.1. MRE11
  • 8.2. ERCC1 and XRCC1
  • 8.3. ATM/RB1/FANCC
  • 8.4. AIMP3
  • 9. Clinical trials of poly ADP ribose polymerase (PARP) inhibitors
  • 10. Other molecular biomarkers
  • 10.1. p53
  • 10.2. EGFR family
  • 11. Molecular taxonomy
  • 12. The fibroblast growth factor receptor (FGFR) pathway
  • 13. Liquid biopsy
  • 14. Micro-RNA
  • 15. Conclusion
  • Funding
  • Acknowledgments
  • References
  • Index.

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