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Biofilm-mediated diseases : causes and controls / / Rina Rani Ray, Moupriya Nag, Dibyajit Lahiri, editors
| Biofilm-mediated diseases : causes and controls / / Rina Rani Ray, Moupriya Nag, Dibyajit Lahiri, editors |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Gateway East, Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (XI, 284 p. 42 illus., 35 illus. in color.) |
| Disciplina | 616.01 |
| Soggetto topico |
Medical microbiology
Biofilms Microbiologia mèdica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-16-0745-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 Introduction to Bacteria and Acute Infections -- 2 Quorum sensing -- 3 Bacteria and Biofilms as Natural Inhabitants of our Body -- 4 Biofilms and Acute and Chronic Infections -- 5 Bacterial and Biofilms in Chronic Infections and Nosocomial Diseases -- 6 Immune Response to Biofilm -- 7 Biofilm in Medical Appliances -- 8 Biofilm and Antimicrobial Resistance -- 9 Inhibition of Biofilm Formation -- 10 Novel and Future Treatment Strategies for Biofilm Associated Infections -- 11 Biofilm- the Unknown Armour in the Arsenal of Bacteria: A Case Study. |
| Record Nr. | UNINA-9910483061303321 |
| Gateway East, Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Systems Biology Approaches
| Systems Biology Approaches |
| Autore | Joshi Sanket |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore : , : Springer, , 2024 |
| Descrizione fisica | 1 online resource (587 pages) |
| Disciplina | 616.900113 |
| Altri autori (Persone) |
RayRina Rani
NagMoupriya LahiriDibyajit |
| ISBN | 981-9994-62-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Editors and Contributors -- About the Editors -- Contributors -- 1: Introduction to Systems Biology -- 1.1 Introduction -- 1.2 Systems Biology: Current Status -- 1.3 Systems Biology for Network Modelling -- 1.3.1 Disease Modelling and Model Integration -- 1.3.2 Disease Dynamics Analysis: Network Perturbations -- 1.3.3 Physiological and Immunological Models -- 1.3.4 Networks as Drug Target -- 1.4 Application of System Biology in Pathophysiology -- 1.4.1 Molecular Diagnostics -- 1.4.2 Genetic Techniques -- 1.4.3 Deletion/Duplication Analysis -- 1.4.4 Targeted Variant Analysis -- 1.4.5 Sequence Analysis: Nucleic Acid Amplification of Target Sequence -- 1.4.6 DNA Methylation: Luminometric Methylation Assay (LUMA) and Other Techniques -- 1.4.7 RNA Single Cell Sequencing and Array-Based Testing -- 1.4.8 Non-coding RNAs: Biomarker of Disease -- 1.4.9 DNA: Whole Exome and Whole Genome Sequencing -- 1.4.10 Detection of Nucleotide Expansion Disorders -- 1.4.11 Pathway-Based Biomarker Analysis -- 1.4.12 Gene Interaction Maps -- 1.4.13 Disease Genes Identification -- 1.5 Conclusions -- References -- 2: Systems Biology and Human Diseases -- 2.1 Introduction -- 2.2 Human Microbiome and Health -- 2.3 Systems Biology: A Tool for Disease Analysis -- 2.4 Origin of Diseases -- 2.5 Network Flow of System Biology -- 2.6 Networks and Interactions -- 2.7 Analysis of Disease Modules Using Network -- 2.8 Colorectal Cancer -- 2.8.1 Gene Expression-Methodology -- 2.8.2 Multi-Approach Omics Analysis for Colorectal Cancer -- 2.8.2.1 Retrieving Omics Data -- 2.8.2.2 Analysis of Differentially Expressed Genes -- 2.8.2.3 Protein-Protein Interaction Network -- 2.8.2.4 Identification of Potent Pathways: Key for Early Treatment -- 2.9 Alzheimer's Disease.
2.10 Systems Biology-Predictor of Disease Risk and Provider of Personalized Health Care -- 2.11 Network Modelling System Analysis -- 2.12 Predicting Therapeutic Measures Through System Biology -- 2.13 Challenges in Application of Systems Biology -- 2.14 Future of Systems Biology -- 2.15 Summary and Conclusion -- References -- 3: Medical Applications of Systems Biology -- 3.1 Introduction -- 3.2 Principles of Systems Biology Research Methods -- 3.3 Computational Methods and Biological Systems in Systems Biology -- 3.4 Systems Biology Applied to Human Illness -- 3.5 System Medicine Implications in the Development of Novel Medicines -- 3.6 Analysis and Future Prospects -- References -- 4: Systems Biology for Metabolic Disorder and Disease -- 4.1 Introduction -- 4.2 The Metabolic Syndrome and System Biology -- 4.3 Metabolic Syndrome and Liver Disease -- 4.3.1 Lack of Fatty Liver Can Cause Metabolic Syndrome -- 4.3.2 Hepatic Disease's Clinical Presentation in the Metabolic Syndrome -- 4.4 The Function of Adipose Tissue in the Pathogenesis and Treatment of Metabolic Syndrome -- 4.4.1 Adipocytes -- 4.4.2 Adipokinase Secreted by Adipose Tissue -- 4.4.3 Leptin -- 4.4.4 Interlukin-6 (IL-6) -- 4.5 Metabolic Syndrome and Skeletal Muscle -- 4.5.1 Skeletal Muscle Insulin Resistance in the Metabolic Syndrome -- 4.5.2 Metabolic Syndrome-Related Change in Insulin Signalling in Muscle -- 4.5.3 Metabolic Syndrome and Glucose Metabolism in Muscle -- 4.5.3.1 Glucose Transport -- 4.5.3.2 Glucose Phosphorylation -- 4.5.3.3 Glycogen Synthesis -- 4.5.3.4 Oxidation of Glucose -- 4.5.4 Metabolism of Muscle Lipids in Metabolic Syndrome -- 4.5.4.1 Transport of Fatty Acids -- 4.5.4.2 Oxidation of Fatty Acids -- 4.5.4.3 Fatty Acid Storage -- 4.6 Lipid Metabolism in Metabolic Syndrome -- 4.6.1 Lipoprotein Metabolism. 4.6.2 Studies in Living Systems Provide Insight into the Effects of Type 2 Diabetes and the Metabolic Syndrome on Lipid Metabolism -- 4.7 Gut Microbiota in Metabolic Syndrome -- 4.7.1 Adipose Tissue Microbial Energy Extraction and Storage -- 4.7.2 Host Metabolism Modulated by Microbes -- 4.7.3 Gut Microbiota and the Obesity -- 4.7.4 Gut Microbiota as a Modulator of Glucose Metabolism -- 4.7.5 Metabolomics for the Identification of the Microbial Metabolites in Disease -- 4.8 Future Directions -- References -- 5: Systems Biology Consortium for Infectious Diseases -- 5.1 Introduction -- 5.1.1 Infectious Diseases "A Global Burden" -- 5.1.2 Host-Pathogen Relationship -- 5.2 Understanding HPI -- 5.3 What Is Systems Biology? -- 5.3.1 Systems Biology of Host-Pathogen Interactions -- 5.3.2 Elements of Systems Biology -- 5.3.2.1 Biomarker Discovery -- 5.3.2.2 Network Inference -- 5.3.2.3 Identification of Disease Modules -- 5.3.3 Contributions of Systems Biology in the Field of Systemic Medicine & -- Personalized Medicine -- 5.3.3.1 Systemic Medicine -- 5.3.3.2 Personalized Medicine -- 5.3.4 Translational Systems Biology -- 5.4 Conclusion -- References -- 6: Case Study-Based Approaches of Systems Biology in Addressing Infectious Diseases -- 6.1 Introduction -- 6.2 Overview of Infectious Diseases -- 6.2.1 Definition and Classification -- 6.2.1.1 Bacterial Infections -- 6.2.1.2 Viral Infections -- 6.2.1.3 Fungal Infections -- 6.2.1.4 Parasitic Infections -- 6.2.2 Global Impact and Challenges of Infectious Diseases -- 6.2.2.1 Public Health Impact -- 6.2.2.2 Economic Burden -- 6.2.2.3 Emergence of Drug Resistance -- 6.2.2.4 Complex Interactions and Heterogeneity -- 6.3 Systems Biology and its Application in Infectious Diseases -- 6.3.1 Principles and Concepts of Systems Biology -- 6.3.1.1 Holistic Approach. 6.3.1.2 Integration of Multi-Omics Data -- 6.3.1.3 Quantitative and Dynamic Analysis -- 6.3.1.4 Feedback and Feedforward Loops -- 6.3.1.5 Emergent Properties and Systems Robustness -- 6.3.1.6 Network Analysis and Topological Properties -- 6.3.1.7 Iterative Experimental and Computational Cycles -- 6.3.1.8 Data Integration and Model Integration -- 6.3.2 Experimental Techniques in Systems Biology -- 6.3.2.1 High-Throughput Omics Technologies -- 6.3.2.2 Molecular Interaction Analysis -- 6.3.2.3 Imaging and Single-Cell Analysis -- 6.3.2.4 Perturbation Experiments -- 6.3.3 Computational Approaches in Systems Biology -- 6.3.3.1 Network Analysis -- 6.3.3.2 Data Integration and Harmonization -- 6.3.3.3 Mathematical Modeling and Simulation -- 6.3.3.4 Machine Learning and Data Mining -- 6.3.4 Applications of Systems Biology in Infectious Diseases -- 6.3.4.1 Host-Pathogen Interactions -- 6.3.4.2 Disease Progression and Biomarker Discovery -- 6.3.4.3 Drug Discovery and Repurposing -- 6.3.4.4 Vaccine Development -- 6.3.4.5 Predictive Modeling and Outbreak Control -- 6.4 Rationale for a Systems Biology Consortium -- 6.4.1 Addressing Complexity and Heterogeneity -- 6.4.2 Integration of Multi-Omics Data -- 6.4.3 Identification of Key Biomarkers and Drug Targets -- 6.4.4 Predictive Modeling and Simulation -- 6.4.5 Accelerating Drug Development and Repurposing -- 6.5 Objectives and Scope of a Systems Biology Consortium -- 6.5.1 Facilitating Collaboration and Data Sharing -- 6.5.2 Standardization of Experimental and Computational Methods -- 6.5.3 Establishing Integrated Databases and Resources -- 6.5.4 Education and Training in Systems Biology -- 6.5.5 Collaboration with Industry and Policymakers -- 6.5.6 Addressing Ethical, Legal, and Social Implications -- 6.6 Structure and Governance of a Systems Biology Consortium. 6.6.1 Leadership and Management -- 6.6.2 Membership and Collaborations -- 6.6.3 Funding and Sustainability -- 6.6.4 Governance and Decision-Making -- 6.6.5 Data Sharing and Intellectual Property -- 6.7 Case Studies -- 6.7.1 Systems Biology Approaches for Viral Infections -- 6.7.2 Systems Biology Approaches for Bacterial Infections -- 6.7.3 Systems Biology Approaches for Parasitic Infections -- 6.8 Challenges and Future Perspectives -- 6.8.1 Data Integration and Harmonization -- 6.8.2 Ethics and Privacy Concerns -- 6.8.3 Regulatory and Policy Implications -- 6.8.4 Technological Advances and Emerging Opportunities -- 6.9 Conclusion -- 6.9.1 Summary of the Systems Biology Consortium Concept -- 6.9.2 Potential Impact and Benefits -- 6.9.3 Call to Action: Collaborative Efforts for Global Health -- References -- 7: Systems Biology and Hospital-Associated Infections -- 7.1 Introduction -- 7.2 Hospital-Associated Infections (HAIs) -- 7.3 Systems Biology and Its Application in the Health Sector -- 7.3.1 Detection and Rapid Identification of HAIs -- 7.3.2 Detection of Contamination Hotspots in the Healthcare Setting -- 7.3.2.1 Use of the ANN for HAI Risk Prediction Among Lung Cancer Patients -- 7.3.2.2 ANN for Predicting the Risk of HAIs in ICUs Using a Didactic Preliminary Model -- 7.3.2.3 HAI Detection by Use of Gradient Tree Boosting and Support Vector Machines -- 7.4 HAI Transmission -- 7.4.1 HAI Modeling of Transmission Dynamics of COVID-19 -- 7.4.2 Mathematical Modeling Improvement to Check the Transmission of HAIs -- 7.5 HAI Management by Systems Biology -- 7.5.1 HAI Management by the System of the ANN and Artificial Intelligence (AI) -- 7.5.2 HAI Management by the System of Mathematical Modeling -- 7.5.3 HAI Management by the System of Machine Learning (ML) -- 7.5.4 HAI Management by the System of Data Analysis. 7.5.5 HAI Management by the System of Internet of Things (IoT). |
| Record Nr. | UNINA-9910861086003321 |
Joshi Sanket
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| Singapore : , : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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