内容

目次

List of Contributors xi Preface xiii 1 Introduction 1Luigi Marrelli References 6 2 Artificial Kidney: The New Challenge 9Pasquale Berloco, Simone Novelli, and Renzo Pretagostini 2.1 Introduction 9 2.2 Kidney Transplantation Statistics 11 2.3 Transplantation Costs 12 2.4 Post–Transplant Costs 12 2.5 Renal Replacement Devices 13 2.6 Implantable Artificial Kidney: Prototype Developments 16 2.7 Kidney Tissue Engineering 17 2.8 Next Steps 20 2.9 Conclusion 21 List of Acronyms 22 References 23 3 Current Status and New Challenges of the Artificial Liver 27Hiroshi Mizumoto, Nana Shirakigawa, and Hiroyuki Ijima 3.1 Introduction 27 3.2 Non–Biological Artificial Liver 28 3.2.1 Classification and Clinical Study 29 3.2.2 PE and HDF 29 3.2.2.1 High–Volume Therapeutic PE 29 3.2.2.2 High–Flow Dialysate Continuous HDF 29 3.2.2.3 PE with Online HDF 30 3.2.3 Blood Purification with Albumin Dialysis 30 3.2.3.1 Single–Pass Albumin Dialysis 30 3.2.3.2 Molecular Adsorbent Recirculating System 31 3.2.3.3 Fractionated Plasma Separation and Adsorption (Prometheus™) 32 3.2.3.4 Hepa Wash 32 3.2.4 Selective Plasma Filtration Therapy 32 3.2.4.1 Biologic–Detoxifilter/Plasma Filter 32 3.2.4.2 Selective Plasma–Exchange Therapy 32 3.2.4.3 Plasma Filtration with Dialysis 33 3.2.5 Clinical Observations of Various Combinations 33 3.3 Bioartificial Liver 35 3.3.1 Bioartificial Liver Support System 35 3.3.2 Cell Source for BAL 37 3.4 New Stream for Artificial Liver 40 3.4.1 Tissue Engineering for Liver Construction 40 3.4.2 Whole Organ Engineering for the Transplantable Artificial Liver 41 3.5 Conclusion and Future Trends 43 List of Acronyms 44 References 45 4 A Chemical Engineering Perspective on Blood Oxygenators 55Luisa Di Paola 4.1 Introduction 55 4.2 A Historical Note 57 4.3 Chemical Engineering Principles in Blood Oxygenators 60 4.4 Chemical Engineering Process Analogues of ECMO Systems 65 4.5 New Challenges 67 4.6 Conclusion 69 List of Symbols 69 References 69 5 Model Predictive Control for the Artificial Pancreas 75M. Capocelli, L. De Santis, A. Maurizi, P. Pozzilli, and Vincenzo Piemonte 5.1 Introduction 75 5.2 Phenomenological Models 78 5.2.1 Background and Two–Compartmental Models 78 5.2.2 Three–Compartment Models 79 5.3 Black–Block Approach 85 5.4 Conclusions 90 Nomenclature 91 References 92 6 Multiscale Synthetic Biology: From Molecules to Ecosystems 97Luisa Di Paola and Alessandro Giuliani 6.1 Introduction: An Historical–Epistemological Perspective 97 6.2 Applications 99 6.2.1 Protein Synthetic Biology 99 6.2.2 Tissue Engineering and Artificial Organs 108 6.2.3 Biotechnology and Ecology Applications 109 6.3 Conclusions 111 List of Symbols 112 References 112 7 Chemical Reaction Engineering Methodologies for Biomedical Imaging Analysis 119Masahiro Kawahara 7.1 Introduction 119 7.2 Magnetic Resonance Imaging (MRI) 119 7.2.1 1H–MRI 120 7.2.2 19F–MRI 121 7.2.3 MRI using Magnetization Transfer 122 7.3 Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT) 123 7.3.1 PET 123 7.3.2 SPECT 125 7.4 Fluorescence Imaging 126 7.4.1 Fluorescent Proteins 126 7.4.2 Small Organic Fluorophores 128 7.5 Conclusion 131 List of Abbreviations 131 References 132 8 Noninvasive and Label–Free Characterization of Cells for Tissue Engineering Purposes 145Shunsuke Tomita 8.1 Introduction 145 8.2 Multivariate Analyses 146 8.2.1 Principal Component Analysis (PCA) 147 8.2.2 Linear Discriminant Analysis (LDA) 148 8.2.3 Hierarchical Clustering Analysis (HCA) 148 8.2.4 Other Multivariate Analyses 149 8.3 Vibrational Spectroscopic Features 149 8.3.1 Cell Characterization Based on Whole–Cell Analysis by Raman Spectroscopy 151 8.3.2 Cell Characterization Based on Subcellular Analysis by Raman Spectroscopy 153 8.3.3 Raman–Based Cell Characterization Toward Biomedical Applications 157 8.4 Morphological Features 160 8.4.1 Cell Characterization Based on Unstained Microscopic Images of Single Cells 160 8.4.2 Cell Characterization Based on Unstained Microscopic Images of Cell Populations 162 8.5 Secreted Molecule Features 165 8.5.1 Cell Characterization Based on Response Signatures 165 8.6 Conclusion and Outlook 167 List of Acronyms 168 References 168 9 TMS–EEG: Methods and Challenges in the Analysis of Brain Connectivity 175Elisa Kallioniemi, Mervi Könönen, and Sara Määttä 9.1 Introduction 175 9.1.1 Transcranial Magnetic Stimulation 175 9.1.2 Electroencephalography 176 9.1.3 Combined TMS and Electroencephalography 178 9.1.4 Data Acquisition 178 9.1.5 Artifacts and Their Prevention 180 9.2 Signal Processing Methods 181 9.2.1 Preprocessing 181 9.2.2 Connectivity Analysis Methods in TMS–EEG 182 9.2.3 Time Domain Methods 183 9.2.4 Frequency Domain Methods 183 9.3 TMS–EEG Applications in Studies of Connectivity 184 9.3.1 General Aspects 184 9.3.2 TMS–Evoked Potentials (TEPs) 185 9.3.3 TMS–Induced Oscillations 186 9.3.4 Clinical Perspectives 187 9.3.4.1 Alzheimer’s Disease 187 9.3.4.2 Schizophrenia 188 9.3.4.3 Disorders of Consciousness 189 9.4 Conclusions and Future Trends 189 List of Acronyms 190 References 190 10 Thermal Treatments of Tumors: Principles and Methods 199P. Saccomandi, E. Schena, M. Diana, J. Marescaux, and G. Costamagna 10.1 Introduction 199 10.2 Effects of Temperature on Living Tissue 199 10.2.1 Hyperthermal Tissue Destruction 200 10.2.2 Cold Temperature for Tissue Destruction 202 10.3 Physical Principles of Thermal Treatments 203 10.3.1 Hyperthermal Treatments 203 10.3.1.1 High–Intensity Focused Ultrasound Ablation 203 10.3.1.2 Radiofrequency Ablation (RFA) 204 10.3.1.3 Microwave Ablation (MWA) 205 10.3.1.4 Laser Ablation (LA) 206 10.3.2 Cryoablation 207 10.4 Mathematical Modeling of Thermal Therapies 209 10.5 Temperature Monitoring During Thermal Treatments 211 10.5.1 Invasive (Contact) Thermometric Techniques 212 10.5.2 Non–Invasive (Contactless) Thermometric Techniques 215 10.6 Conclusions 218 List of Acronyms 219 List of Symbols 219 References 220 Index 229