Cover
Title Page
List of Contributors
Preface
1 Introduction
1. References
2 Artificial Kidney: The New Challenge
1. 2.1 Introduction
2. 2.2 Kidney Transplantation Statistics
3. 2.3 Transplantation Costs
4. 2.4 Post‐Transplant Costs
5. 2.5 Renal Replacement Devices
6. 2.6 Implantable Artificial Kidney: Prototype Developments
7. 2.7 Kidney Tissue Engineering
8. 2.8 Next Steps
9. 2.9 Conclusion
10. List of Acronyms
11. References
3 Current Status and New Challenges of the Artificial Liver
1. 3.1 Introduction
2. 3.2 Non‐Biological Artificial Liver
3. 3.3 Bioartificial Liver
4. 3.4 New Stream for Artificial Liver
5. 3.5 Conclusion and Future Trends
6. List of Acronyms
7. References
4 A Chemical Engineering Perspective on Blood Oxygenators
1. 4.1 Introduction
2. 4.2 A Historical Note
3. 4.3 Chemical Engineering Principles in Blood Oxygenators
4. 4.4 Chemical Engineering Process Analogues of ECMO Systems
5. 4.5 New Challenges
6. 4.6 Conclusion
7. List of Symbols
8. References
5 Model Predictive Control for the Artificial Pancreas
1. 5.1 Introduction
2. 5.2 Phenomenological Models
3. 5.3 Black‐Block Approach
4. 5.4 Conclusions
5. Nomenclature
6. References
6 Multiscale Synthetic Biology
1. 6.1 Introduction: An Historical‐Epistemological Perspective
2. 6.2 Applications
3. 6.3 Conclusions
4. List of Symbols
5. References
7 Chemical Reaction Engineering Methodologies for Biomedical Imaging Analysis
1. 7.1 Introduction
2. 7.2 Magnetic Resonance Imaging (MRI)
3. 7.3 Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT)
4. 7.4 Fluorescence Imaging
5. 7.5 Conclusion
6. List of Abbreviations
7. References
8 Noninvasive and Label‐Free Characterization of Cells for Tissue Engineering Purposes
1. 8.1 Introduction
2. 8.2 Multivariate Analyses
3. 8.3 Vibrational Spectroscopic Features
4. 8.4 Morphological Features
5. 8.5 Secreted Molecule Features
6. 8.6 Conclusion and Outlook
7. List of Acronyms
8. References
9 TMS‐EEG
1. 9.1 Introduction
2. 9.2 Signal Processing Methods
3. 9.3 TMS‐EEG Applications in Studies of Connectivity
4. 9.4 Conclusions and Future Trends
5. List of Acronyms
6. References
10 Thermal Treatments of Tumors
1. 10.1 Introduction
2. 10.2 Effects of Temperature on Living Tissue
3. 10.3 Physical Principles of Thermal Treatments
4. 10.4 Mathematical Modeling of Thermal Therapies
5. 10.5 Temperature Monitoring During Thermal Treatments
6. 10.6 Conclusions
7. List of Acronyms
8. List of Symbols
9. References
Index
End User License Agreement

List of Tables

Chapter 02
1. Table 2.1 Relative kidney transplant costs in the US in 2015.
Chapter 03
1. Table 3.1 Overview of non‐biological artificial liver systems.
Chapter 04
1. Table 4.1 Main chemical reactions occurring in blood during gas exchange; chemical equibrium constants are evaluated at room temperature.
2. Table 4.2 Mass transfer empirical correlations in hollow‐fiber oxygenators in the range.
Chapter 05
1. Table 5.1 Definition and explanation of the inter‐patient adjustable parameters obtained by Piemonte et al. [10].
Chapter 08
1. Table 8.1 Representative examples for the characterization of cultured cells using Raman spectroscopy.
2. Table 8.2 Representative definitions of morphology features.

List of Illustrations

Chapter 02
1. Figure 2.1 First experimental dialysis (1913).
2. Figure 2.2 Schematic of an extracorporeal circuit of a bioartificial kidney used to treat patients with acute renal failure. The first cartridge is a hemofiltration cartridge in series with a renal tubule cell device (RAD). The ultrafiltrate is delivered to the luminal compartment of the RAD which contains the cells and the post‐filtered blood is pumped into the extracapillary space of the RAD. The processed luminal ultrafiltrate from the RAD is discarded to waste and the processed blood is returned to the patient.
3. Figure 2.3 Concept illustration of implantable bioartificial kidney.
Chapter 03
1. Figure 3.1 System images of non‐biological artificial livers using plasma exchange and hemodiafiltration. (a) Plasma exchange, (b) plasma exchange and high‐flow hemodiafiltration, and (c) plasma exchange with online hemodiafiltration.
2. Figure 3.2 System images of non‐biological artificial livers using albumin dialysis. (a) Single‐pass albumin dialysis, (b) molecular adsorbent recirculating system, (c) fractionated plasma separation and adsorption, and (d) Hepa Wash.
3. Figure 3.3 System images of non‐biological artificial livers using selective plasma filtration. (a) Biologic‐detoxifilter/plasma filter, (b) selective plasma‐exchange therapy, and (c) plasma filtration with dialysis.
4. Figure 3.4 Concepts of tissue engineering and whole‐organ engineering.
5. Figure 3.5 Histological analysis using hematoxylin and eosin staining (a, b) and molded resin of vascular structure (c, d). native liver (a, c) and decellularized liver (b, d). Scale bars indicate 100 µm (a, b) and 5 mm (c, d), respectively.
Chapter 04
1. Figure 4.1 Number of ECMO active centers and runs worldwide [1].
2. Figure 4.2 Number of papers regarding ECMO during years
3. Figure 4.3 A schematic picture of the extra‐corporeal circuit developed by Gibbon: the key elements are the film oxygenator (on flat metal sheets J) and the peristaltic pumps D, E, and P.
4. Figure 4.4 A modern ECMO circuit.
5. Figure 4.5 Oxygen dissociation curves at different carbon dioxide partial pressures.
6. Figure 4.6 Gas‐liquid contactors: (a) wetted wall column; (b) bubble column; and (c) gas‐liquid hollow‐fiber gas‐liquid contactor.
Chapter 05
1. Figure 5.1 Descriptive scheme of the closed chain system for glucose‐insulin control in an artificial pancreas.
2. Figure 5.2 Basic concept of MPC during a chess game consisting of predicting and regulating glucose level.
3. Figure 5.3 Block diagram of the model presented and tested by Piemonte et al. [10].
4. Figure 5.4 Insulin‐dependent glucose utilization with ISF glucose level when Ii(t) = 6 μU/ml (left); change of insulin‐dependent glucose utilization with ISF insulin level when Gi(t) = 90 mg/dl (right).
5. Figure 5.5 Effect of plasma glucose level on insulin‐independent glucose utilization.
6. Figure 5.6 Change of hepatic glucose production with plasma insulin level.
7. Figure 5.7 Insulin secretion rate with plasma glucose level.
8. Figure 5.8 Glucose level profiles with the simulation and the insulin and glucose intake for one patient reporting in the validation study by Piemonte et al. [10].
9. Figure 5.9 Conceptual scheme of an ideal glucose predictor able to exploit most of the inputs influencing glucose concentration.
10. Figure 5.10 Control algorithm AP including ANN models.
Chapter 06
1. Figure 6.1 A synthetic biology sketch.
2. Figure 6.2 A sketch of kinesin functioning.
3. Figure 6.3 A 3D representation of a dynein bound to microtubule.
4. Figure 6.4 Myosin structure: the long tail (S2 + LMM) is a coiled coil, head S1 comprises the motor domain (MD) and the light chain domain (LCD) in turning including the essential light chain (ELC) and the regulatory LC (RLC).
5. Figure 6.5 Ribbon structure of kinesin.
6. Figure 6.6 A racing motorbike blueprint.
7. Figure 6.7 Protein contact networks. Panel (a) the ribbon representation of the T‐state of human hemoglobin; Panel (b) the corresponding Protein Contact Network.
8. Figure 6.8 Construction of an organized scaffold for tissue engineering.
Chapter 08
1. Figure 8.1 Data outputs analyzed by representative multivariate analyses. (a) Signatures of three different samples (1–3). In the context of, for example, spectroscopy, each variable may correspond to the intensity at each wavenumber. (b) PCA score plot. (c) LDA score plot. (d) HCA dendrogram. The datasets were reproduced with permission from the UCI machine learning repository [5].
2. Figure 8.2 Examples of (a) IR and (b) Raman spectra with a tentative peak assignment of biochemically relevant compounds and moieties.
3. Figure 8.3 Identification of cardiac differentiation of ESCs by Raman spectroscopy. (a) Mean Raman spectra for hESC, hESC‐CM, and FLV‐CM cells. Black lines indicate the average spectra, whereas gray lines indicate the standard deviation from the average spectra. (b) LDA plot for hESC, hESC‐CM, and FLV‐CM cells. Shaded ellipsoids superimposed on the data points serve as visual guidelines.
4. Figure 8.4 Identification of the HSC differentiation hierarchy. (a) Diagram of hematopoiesis [36]. Populations analyzed in this study are highlighted in gray. (b) Average Raman spectra of hematopoietic cells. Gray lines indicate the standard deviation from the average spectra. (c) Identification plots for the PLS‐DA models generated using Raman spectra of hematopoietic cells. Cells located above the classification threshold (upper dashed line) were identified as (a) LT‐HSCs, (b) ST‐HSCs, (c) granulocytes, and (d) B cells.
5. Figure 8.5 Raman spectroscopic analysis of different infection states of Staphylococcus aureus in endothelial cells. (a) Schematics of the infection process and corresponding average Raman spectra with standard deviations at different points in time. (b) PCA‐LDA score plot. A data set was well separated by the first linear discriminant scores.
6. Figure 8.6 Raman spectroscopic analysis of the two most abundant leukocytes. (a) Average Raman spectra with standard deviation. (b) Dendrogram. (c) PCA score plot.
7. Figure 8.7 Quantitative classification of completely and incompletely reprogrammed hiPSC colonies. (a) Experimental overview. (b) Binary classification of colony images against completely reprogrammed hiPSCs (1H cells). Values represent mean values with standard deviations from 10 independent tests. N.S. = not significant.
8. Figure 8.8 Sensor array system for the label‐free and noninvasive identification of cell types and lineages. (a) Decrease in the catalytic activity of anionic enzymes through reversible complexation with PEGylated polyamines, and the subsequent partial recovery of activity through competitive interaction with secreted molecules. (b) Culture supernatants collected from cells were mixed with cross‐reactive complexes to generate activity patterns reflecting the secretomic signatures for given cell types or lineages. (c) Human cancer cell lines, and (d) differentiated (osteoblast and adipocyte) and undifferentiated hMSCs were analyzed by LDA, and the corresponding LDA plots are shown. Ellipses represent confidence intervals (±1 S.D.) for the individual target cells.
Chapter 09
1. Figure 9.1 The methodology of navigated transcranial magnetic stimulation (an example from one commercial vendor) combined with electroencephalography (TMS‐EEG). (a) In order to perform accurate navigation, the subject wears a head tracker with spherical reflective parts. These reflective parts are monitored by an infrared camera. With the help of the head tracker, the subject is co‐registered with the subject‐specific magnetic resonance image (MRI). (b) After co‐registration, the MRI can be used to localize the stimulation target. (c) The movement of the stimulation coil is also followed by reflective parts. The navigation system records the location, tilt and induced electric field direction of a given TMS pulse and therefore, pulses can be repeated. (d) An example of a TMS‐EEG measurement setup. If the applied stimulation intensity is normalized according to the resting motor threshold (rMT), TMS pulses are first focused on the motor cortex and evoked motor responses are measured from the target muscle using surface electromyography (EMG) electrodes to calculate the rMT.
2. Figure 9.2 EEG signal from a representative subject. (a) An electroencephalography (EEG) electrode montage. Raw EEG signal measured from frontal (Fpz), central (Cz), and posterior (Oz) electrodes is shown. (b) A typical TMS evoked potential (TEP) induced by motor cortex stimulation. The TEP is measured from the Cz electrode. The topographical maps present the spread of neural activity at each TEP component (P30 ms, N45 ms, P60 ms, N100 ms, P180 ms, and N280 ms). (c) Event related spectral perturbation (ERSP) and inter‐trial coherence (ITC) of motor cortex TEPs. In B and C, the TMS pulse was given at a time 0 ms.
Chapter 10
1. Figure 10.1 Effects of thermal ablation on tissue: (a) Macroscopic effect of diode laser‐induced thermal ablation of healthy ex vivo animal liver; (b) Specimen from a histopathological examination (zoom × 10) 24 h after Nd:YAG laser ablation on in vivo animal pancreas. Although the laser settings differed between the two treatments, it is possible to distinguish similar effects: (I) the central zone of irreversible damage (in both cases, cells vaporized as a result of the raised temperature), (II) a zone of coagulated necrosis, and (III) the surrounding uninjured parenchyma. Also, a thin (1–2 mm) and watershed zone of early inflammatory response surrounding coagulated tissue is detectable [11].
2. Figure 10.2 Different kinds of hyperthermal treatment whose energy source is characterized by different frequencies (or wavelengths).
3. Figure 10.3 Minimally invasive thermal treatment for tumor removal: laser ablation (LA), microwave ablation (MWA), radiofrequency ablation (RFA), high‐intensity focused ultrasound (HIFU), and cryoablation [53].
4. Figure 10.4 Finite Element Modeling (FEM) simulations of temperature distribution induced by (a) MWA and (b) LA within liver tissue.
5. Figure 10.5 Temperature monitoring during thermal treatments. (a) Invasive thermometry, based on the use of sensors (Fiber Bragg Gratings and thermocouples), which require contact with the target intended for thermal treatment; trends of temperature increase measured by FBG sensors placed inside the ex vivo organ undergoing LAs. Non‐invasive thermometry based on: (b) MR imaging: MR scans are acquired at different times during the ablation procedure, and the temperature increase in each pixel of the image is obtained by comparing the successive scans and the change of signal intensity in each pixel; (c) CT‐scans, where the change of tissue temperature is related to the change of CT‐number of the image (when the temperature increases, the CT‐number decreases, hence the pixel values turn from gray to dark gray), as clearly visible from the images obtained before and at the end of the ablation; (d) US imaging, where the increase of tissue temperature can be monitored by means of the classical B‐mode, resulting into an increase of echogenicity, or, more efficiently, by means of elastography, where the variation of tissue stiffness can be used as an indirect parameter to estimate tissue temperature.

This edition first published 2018
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Library of Congress Cataloging‐in‐Publication Data

Names: Piemonte, Vincenzo, editor. | Basile, Angelo, editor. | Ito, Taichi, editor. | Marrelli, Luigi, editor.
Title: Biomedical engineering challenges : a chemical engineering insight / edited by Vincenzo Piemonte, University Campus Biomedico of Rome, Italy, Angelo Basile, Italian National Research Council, Rende, Italy, Taichi Ito, University of Tokyo, Japan, Luigi Marrelli, University Campus Biomedico of Rome, Italy.
Description: First edition. | Hoboken, NJ : Wiley, 2018. | Includes bibliographical references and index. |
Identifiers: LCCN 2017054493 (print) | LCCN 2017056798 (ebook) | ISBN 9781119296027 (pdf) | ISBN 9781119296010 (epub) | ISBN 9781119296041 (hardback)
Subjects: LCSH: Biomedical engineering. | Biochemical engineering. | BISAC: SCIENCE / Biotechnology.
Classification: LCC R856 (ebook) | LCC R856 .B566 2018 (print) | DDC 610.28–dc23
LC record available at https://lccn.loc.gov/2017054493

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Cover image: © Pobytov/iStockphoto

List of Contributors

Angelo Basile
Italian National Research Council, Rende, Italy

Pasquale Berloco
Università La Sapienza Roma, Dipartimento di Chirurgia Generale e Trapianti d’Organo, Rome, Italy

M. Capocelli
Unit of Process Engineering, Department of Engineering, Università Campus Bio‐Medico di Roma, Italy

G. Costamagna
Institute of Image‐Guided Surgery, IHU‐Strasbourg, France

M. Diana
Institute of Image‐Guided Surgery, IHU‐Strasbourg, France

Alessandro Giuliani
Environment and Health Department, Istituto Superiore di Sanità, Rome, Italy

Hiroyuki Ijima
Department of Chemical Engineering, Faculty of Engineering, Kyushu University, Fukuoka, Japan

Taichi Ito
Department of Bioengineering, School of Medicine and Engineering, University of Tokyo, Japan

Elisa Kallioniemi
Diagnostic Imaging Center, Kuopio University Hospital, Finland

Masahiro Kawahara
Department of Chemistry and Biotechnology, Graduate School of Engineering, The University of Tokyo, Japan

Mervi Könönen
Diagnostic Imaging Center, Kuopio University Hospital, Finland

Sara Määttä
Diagnostic Imaging Center, Kuopio University Hospital, Finland

J. Marescaux
Institute of Image‐Guided Surgery, IHU‐Strasbourg, France

Luigi Marrelli
Department of Engineering, University Campus Biomedico of Rome, Italy

A. Maurizi
Unit of Endocrinology and Diabetes, Department of Medicine, Università Campus Bio‐Medico di Roma, Italy

Hiroshi Mizumoto
Department of Chemical Engineering, Faculty of Engineering, Kyushu University, Fukuoka, Japan

Simone Novelli
Università La Sapienza Roma, Dipartimento di Ingegneria Meccanica e Aerospaziale, Rome, Italy

Luisa Di Paola
Unit of Chemical‐Physics Fundamentals in Chemical Engineering, Department of Engineering, Università Campus Bio‐Medico di Roma, Italy

Vincenzo Piemonte
Department of Engineering, University Campus Biomedico of Rome, Italy

P. Pozzilli
Unit of Endocrinology and Diabetes, Department of Medicine, Università Campus Bio‐Medico di Roma, Italy

Renzo Pretagostini
Università La Sapienza Roma, Dipartimento di Chirurgia Generale e Trapianti d’Organo, Rome, Italy

P. Saccomandi
Unit of Measurements and Biomedical Instrumentation, Department of Engineering, Università Campus Bio‐Medico di Roma, Italy

L. De Santis
Unit of Process Engineering, Department of Engineering, Università Campus Bio‐Medico di Roma, Italy

E. Schena
Unit of Measurements and Biomedical Instrumentation, Department of Engineering, Università Campus Bio‐Medico di Roma, Italy

Nana Shirakigawa
Department of Chemical Engineering, Faculty of Engineering, Kyushu University, Fukuoka, Japan

Shunsuke Tomita
Biomedical Research Institute, National Institute of Advanced Industrial Science and Technology, and DAILAB, Tsukuba, Ibaraki, Japan

Preface

Biomedical engineering has come a long way since Leonardo da Vinci (1452–1519) drew his revolutionary pictures of the skeleton and its musculature and studied the mechanics of bird flight. The modern era has seen the application of engineering in almost every branch of medicine, so much so that the practice of medicine is now completely dependent on the work and support of engineers.

Artificial organ technologies such as extracorporeal life support, dialysis and insulin control systems keep huge numbers of patients alive who await organ transplantation. A person with a new prosthetic arm can ‘feel’ things and thereby grip eggs without breaking them. Imaging techniques aim to see inside the intact human body. An important use of three‐dimensional body imaging is incorporating biomechanical models of muscles and bones, enabling simulations of planned surgical procedures. Less invasive techniques and devices will lead to more comfortable recovery for patients, as well as faster rehabilitation and shorter stays in hospital, all of which also lead to a reduction in health care costs. These are just some examples of the amazing progress that has been made in biomedical engineering over the last few decades.

This book reports on some applications in the field of biomedical engineering ranging from artificial organs and tissue engineering to biomedical imaging.

In detail, after a short introductory chapter devoted to introducing the book and providing an overview of the topics covered, Chapter 2 (Berloco, Novelli and Pretagostini) deals with the artificial kidney. Nowadays, the only real dialysis substitute is kidney transplantation but the while scientific community is looking for a substitute, because of the lack of organs available and the complications that a transplant can cause. The understanding of the cellular and molecular bases of organ function will be translated into new therapeutic approaches, especially in the acute and chronic renal failure. The bases of these new therapies are cell therapy and tissue engineering with a final goal of introducing these cells into the patient either via extracorporeal circuits or as implantable constructs.

In Chapter 3 (Mizumoto, Shirakigawa and Ijima) a detailed survey of artificial liver is given. The development of a functional artificial liver has been desired to save patients’ lives with serious liver failure. For the past 60 years, the removal rate of toxins in blood by artificial systems was improved. As a result, the effectiveness of recovery has been significantly improved by the non‐biological artificial liver. On the other hand, the bioartificial liver (BAL), which is expected to compensate for important functions of the liver, has been developed as a practical treatment system by ingenious improvement of the devices. In the future, it is expected that high efficiency treatment will be developed by the combination of these systems.

Chapter 4 (Di Paola), dealing with blood oxygenators, traces out the parallel development of oxygenators and gas‐liquid contactors, demonstrating in this emblematic case how chemical engineering principles contribute to biomedical engineering innovation.

Chapter 5 (Capocelli, De Santis, Maurizi, Pozzilli and Piemonte) provides an historical review of the predictive control models for artificial pancreas, individuating the drawbacks and the advantages of the most widespread class of algorithms. Moreover, the chapter describes the physical‐mathematical fundamentals and the most important results of a three‐compartment model recently proposed by the chapter’s authors.

Chapter 6 (Di Paola and Giuliani) reports a perspective of actual discoveries and innovations in the field under the light of systems biology. Trying to translate knowledge from engineering to build up biological machines, there is a missing trick due to the strong, systemic nature of organisms, which challenges any interaction between machines and life. The systemic approach could drive a real revolution and provide devices that are compliant with life, but engineering itself could learn many lessons for instance from protein nanomachines about regulation, autoadaptive systems and modularity of smart systems.

Chapter 7 (Kawahara) focuses on the aspects of chemical reaction engineering in the molecular imaging techniques (MRI, PET/SPECT and fluorescence imaging) and summarizes their advances, capability and significance in the biomedical field.

Chapter 8 (Tomita) provides a brief overview of multivariate analyses as key statistical techniques that are commonly used in signature‐based cell profiling. Subsequently, three approaches for noninvasive and label‐free characterization of cultured cells are presented using selected examples.

Chapter 9 (Kallioniemi, Könönen and Määttä) introduces some of the central issues regarding theory, methods and applications of TMS‐EEGin functional connectivity studies. Connectivity studies aim to evaluate and characterize the neural interactions between brain regions. The chapter begins with a presentation of the basic principles related to TMS‐EEG methodology followed by an introduction to the most common data analysis methods in connectivity studies. Finally, the chapter discusses the applications related to TMS‐EEG in studies of connectivity and presents some of the clinical perspectives in the field.

Finally, Chapter 10 (Saccomandi, Schena, Diana, Marescaux and Costamagna) discusses the application of thermal therapies to the treatment of tumors: the principles of working of the main thermal approaches are presented, together with the main techniques for the real‐time monitoring of the outcome.

The Editors wish to take this opportunity to thank all the Authors for their hard work. Special thanks also to the editorial team at Wiley for their continued helpful support.

Prof. Vincenzo Piemonte
University Campus Biomedico of Rome
Italy

Prof. Angelo Basile
Italian National Research Council Rende
Italy

Prof. Taichi Ito
University of Tokyo
Japan

Prof. Luigi Marrelli
University Campus Biomedico of Rome
Italy

1
Introduction

Luigi Marrelli

Department of Engineering, University Campus Biomedico of Rome, Italy

Biomedical Engineering (BE) is a complex applied science that has applications in the fields of medicine concerning diagnosis, therapy and rehabilitation. As in all sectors of technology characterized by complexity, the capability of solving problems and developing new devices for therapy and rehabilitation or to devise innovative techniques for diagnosis and treatment of pathologies requires synergy of various forms of expertise. This need is especially felt in BE where the subject of research is the human body with its complex operations: molecular mechanisms, chemical and biochemical intracellular reactions, control systems, functions of human organs and so on.

Despite the complexity of the biological system that doctors and biomedical engineers must relate to, the elements of which the human body is composed and the functions they perform have a close affinity with the operations carried out in a chemical plant [1] where some raw materials undergo a series of transformations (reactions, separation operations, mass and heat exchanges, etc.) in order to obtain useful products and energy. In the last century, this analogy has led to striking graphic representations of the human body and of its functions as an industrial plant. It is worth mentioning the picture [2] named “Der Mensch als Industriepalast,” a creation by Fritz Kahn, a German doctor, science writer and pioneer of information graphics.

Indeed, the human body is composed of a solid structure of support and a casing that encloses a series of organs with functions of mass exchange, synthesis and transformation, and are connected to each other by a network of ducts passed through by fluids. A pumping system equipped with valves has the task of ensuring blood circulation in the vascular circuit. The digestive system, through complex chemical reactions, transforms ingested raw materials into useful substances and energy needed for the operation of the whole system. The muscular system can be regarded as a set of actuators responsible for moving several parts of the body whereas the peripheral nervous system supplies sensory stimuli coming from the environment to the central nervous system that supervises the control and processing of various functions in a similar way to what happens in the system of sensors, monitoring and automatic control in an industrial plant.

Beyond this evocative picture, however, the analogy suggests the idea that many sophisticated techniques of chemical engineering (CE) could be usefully applied to face the technical challenges of BE. Since the beginnings of its history, CE has dealt with unit operations for the separation of mixtures, humidification of gases, chemical reactors, mass, heat and momentum transport, properties of materials and so on, on the basis of scientific fundamentals that are phase equilibria thermodynamics, chemical and biochemical kinetics, transport phenomena, automatic control and mathematical tools needed to better understand many complex phenomena and to represent the behavior of equipment through theoretical or semi‐empirical models useful for the simulation and the optimization of the process. Therefore, CE can provide skills to the solution of problems that BE has to face and, vice versa by a cross‐fertilization process, can receive from BE valuable input for the development of innovative methods and processes deduced from the behavior of biological systems.

The fields where CE can provide fundamental contributions are numerous and range from the macroscale of artificial and bio‐artificial organs to the nanoscale of chemical‐physical properties of materials of cell micro‐reactors. Furthermore, also in the industrial biotechnology and pharmaceutical fields, CE points out its potentiality in the large‐scale production of drugs and in sophisticated methods of targeted drug delivery.

Organs like the kidney, liver or heart‐lung system have, among their functions, those of cleaning the blood from toxins or excesses of substances and of exchanging oxygen and carbon dioxide. When native organs are not able to correctly perform these functions for pathological reasons, an artificial kidney, artificial liver or lung oxygenation unit can substitute or at least support the damaged vital functions and allow the patient to stay alive indefinitely or, at least, long enough for the possibility of carrying out transplantation or revival of the native organ. Nowadays, ultrafiltration technology by selective membranes is an acquired asset in CE that can provide a key contribution to the development of increasingly effective and low‐cost artificial organs. Some chapters of the present book are devoted to artificial organs and their behavior.

However, just separation operations and selective transport are not enough in many cases to mimic the functions of the native organ: complex organs such as the liver or pancreas carry out synthesis functions and biochemical reactions not currently reproducible by artificial systems. This need has led to the development of bio‐artificial or hybrid organs in which the artificial component is coupled with a biological element; that is, a cell tissue able to perform functions not reproducible by a totally artificial system.

Therefore, hybrid organs are characterized by the presence of a kind of bioreactor where cells are kept in the optimal conditions for their survival and, in particular, to perform the functions of which they are responsible. The use of living cells as engineering materials is the basis of the so‐called tissue engineering [3] where chemical engineering, material science and life sciences skills are involved. In the field of tissue engineering, the scaffold or support technology provides an important step in the growth and differentiation of the desired tissue. Even in this case, chemical reaction engineering and theory of reactors, the typical hallmarks of chemical engineers’ activity, play a fundamental role in modeling, designing and properly running the bioreactor used for growing the new tissue.

A technology still under study is the development of an artificial pancreas. The purpose of this device is monitoring and properly releasing insulin, a hormone produced by β cells of the pancreatic islets of Langerhans, that regulates the absorption of glucose from blood and its conversion into glycogen or triglycerides. If β cells do not work, insulin can no longer be synthesized or secreted into the blood resulting in a high blood glucose concentration (type 1 diabetes). In order to solve this problem, chemical engineers are working on a computerized device able to monitor continuously blood glucose levels and to actuate micro‐pumps for delivering insulin contained in a small reservoir. Chapter 5 of the present book is devoted to the artificial pancreas.

A well‐known field of CE deals with scale‐up techniques; that is, similar criteria needed to develop an industrial plant from information on the behavior of a pilot or bench scale plant. In the past, these techniques have already provided fundamental results for designing and running plants devoted to manufacturing products essential to human health. A typical early example is the process of producing penicillin on an industrial scale, suggested at the end of the World War II by Margaret Hutchinson Rousseau [4], a young chemical engineer. The industrial production process is based on an aerobic submerged fermentation. When penicillin was first made, the fungus Penicillum notatum was used and the yield of the process was about 1 mg/dm³. Nowadays, using a different mold species (Penicillum chrysogenum) and by improving fermentation operating conditions and downstream processing, such as extraction techniques, a yield of 50 g/dm³ is reached.

The opposite side of the coin is represented by scale‐down techniques; that is, by the techniques to implement micro‐devices [5, 6]. These devices are composed of a network of microchannels connecting micro‐reactors, mixers, pumps and valves contained in vessels whose dimensions are in the order of micrometers with controlled volumes down to picolitres. At this scale, fluid transport in capillaries is laminar and the resulting very high surface area to volume ratio affects mass and heat transfer rates and catalytic reaction rates that depend on the interface area. Microfluidic devices (lab‐on‐chip assemblies) are increasingly used to carry out chemical and biochemical reactions for applications in the genomic field, immunoassays, sensors, drug discovery, new catalyst development and many other forthcoming uses.

Properties of materials is another field where CE together with material science can provide an important contribution to BE. Scaffolds used in tissue engineering have to be biocompatible and biodegradable [1] to allow their use in contact with biological material and their absorption by the surrounding tissues when scaffolds are used in implantable devices. In any case, the degradation rate of the support must be compatible with the rate of making new tissue and with the integration of this one with the surrounding tissues. A very important property of the scaffold is its porosity and the distribution of pore sizes to allow three‐dimensional tissue growth. A fractal geometry approach has proven to be useful for the characterization of these properties.

The knowledge of rheological properties of biological fluids [7] is another essential requirement for a proper design of extracorporeal devices. Blood is a suspension made of an aqueous solution (plasma) of electrolytes, sugars and proteins and of a corpuscular part composed of erithrocytes, leucocytes and platelets. Plasma is a Newtonian fluid with a viscosity of images at 37 °C. Whole blood, on the other hand, shows non‐Newtonian behavior ranging from Bingham to pseudoplastic fluid behavior depending on the value of the shear stress. Non‐Newtonian characteristics are clearer in the thinnest ducts as capillaries.

These rheological properties make the modeling of blood flow particularly complex [8, 9] in the various blood ducts, especially if one wishes to account for the transient behavior of the flow due to heartbeat and for non‐stiffness of blood vessels. Moreover, in the case of blood circulation in extracorporeal devices, a non‐negligible feature to take into account is the possible damage that shear stresses in circulation ducts and even more in pumping systems can cause to corpuscular part of the blood and to proteins [10].

These features cannot properly be accounted for without the knowledge of momentum transport that is one of the typical expertise areas of CE.

Another significant CE contribution can be found in the increasingly sophisticated techniques that are being tuned for an effective and safe administration of drugs or for their targeted delivery. The development of controlled drug delivery strategies has the purpose of assuring a precise drug dosage and of avoiding the risk of insufficient dosing or of over‐dosages that can appear in conventional methods of delivering (ingestion or injection). Some of these new methods have been developed at MIT in the 1970s by chemical engineer Robert Langer and his colleagues [11, 12] by exploiting the property of some highly porous structures (mixtures of hydrophobic polymers and proteins) impregnated with the drug to release it in slowly and controlled way under appropriate conditions. Such a device, in the form of an implantable wafer, has been successfully tested in delivering, with small side effects, a potent chemotherapy drug to brain cells attacked by glioblastoma.

Glucose‐sensitive hydrogels can be used for the controlled release of insulin to diabetic patients. A system based on this kind of materials has been developed at the Purdue University by the team of chemical engineers coordinated by Nicholas Peppas [13–15].

Overdoses of many antitumor drugs are dangerous for healthy tissues and organs. This drawback has started up an intensive research activity in the community of chemical and biomedical engineers for the development of targeted delivery systems of chemotherapy drugs, that is for the drug delivery in the sick tissue. Attempts have been made by the encapsulation of the drug in carriers as liposomes, nanoscaled C₆₀ fullerenes or entrapping it within a hydrogel matrix. These kinds of methods allow a reduction of drug degradation by the digestive system and avoid attack from the immune system if the carrier surface is properly masked. Using specific peptides or, in particular, monoclonal antibodies attached to the external carrier surface allows the drug to be led to target cells where it is released as a consequence of internal or external triggers such as pH, temperature, enzymes, ultrasound or magnetic signals. For example, hydrogels swell under different environmental conditions so that they can release a drug entrapped by changing the pH or the temperature. Furthermore, since hydrogels are stable in acidic environments, they do not undergo any degradation by passing through the stomach. Even in this BE field, CE fundamentals concerning mass transport phenomena and chemical and physical properties of polymers play a paramount role.

Finally, a few words about synthetic biology [16] to which a chapter of this book is devoted (Chapter 6). As the human body with its organs can be seen as an industrial plant, so cells can be considered as nanomachines on the molecular scale. The advances in biology indeed allow cell mechanisms to be described as an ensemble of biocatalytic reactions resulting in handling mass, energy and information. This consideration has induced researchers to use the molecular mechanisms of living matter for thinking up models, techniques and devices aimed to the development of new strategies of diagnosis and therapy more effective than the traditional ones. A typical example is the fight against infection, since increasing resistance to antibiotics has made their use partly ineffective.

This approach has led to the origin and evolution of the synthetic biology that is an interdisciplinary field of science based on the use of fundamentals of biology and engineering for the achievement of new and smart methods and devices useful in diagnosis and therapy.

Some examples in the field of therapy are:

bacteriophage‐based therapy that exploits the very specific action of the phages for killing infectious microorganisms;
synthetic probiotic therapy based on the controlled and confined release under specific conditions of drug‐molecules by engineered bacteria in the form of synthetic probiotics.

Similar strategies are evolving also in the field of diagnostics. In particular, a diagnostic technique is emerging based on the ability of specific bacteriophages to identify a particular bacterium on the basis of their ability to clear it.

These considerations clearly show that, as synergy between CE and BE has produced key contributions to the development of tools useful to human health by using typical methods of engineering, we are able to formulate promising achievable ideas. Ever‐closer cooperation between these branches of engineering allows us to foresee that results obtained so far are only the tip of the iceberg and that, in the near future, currently seeming science fiction achievements can become reality.

References

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2 Cybernetic Zoo. Images of Fritz Kahn. Online at: http://cyberneticzoo.com/tag/fritz‐kahn/ (accessed November 2017) (n.d.).
3 Hoque, M.E. Bioartificial Liver Assist System: Tissue Engineering Challenges, Lambert Academic Publishing (2010).
4 Maitland, G. Ten chemical engineers that shaped our world (Day 356). Online at: https//ichemeblog.org/ten‐chemical‐engineers‐that‐shaped‐our‐world (accessed October 2017). Institution of Chemical Engineers (2015).
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9 Apostolidis, A.J., M.J. Armstrong and A.N. Beris, “Modeling of human blood rheology in shear flows,” Journal of Rheology (2016), 59, 275.
10 Marrelli, L., F. Vitale, and L. Di Paola, “Shear stress induced blood damage in artificial organs and haematica circuits” Proceedings of Gr.I.C.U. Conference (2008), 177.
11 Langer, R. and J. Folkman, “Polymers for the sustained release of proteins and other macromolecules,” Nature (1976), 263, 797.
12 Bawa, R., R.A. Siegel, B. Marasca, M. Karel, and R. Langer, “An explanation for the controlled release of macromolecules from polymers,” Journal of Controlled Release (1985), 1(4), 259.
13 Marek, S.R. and N.A. Peppas, “Insulin release dynamics from poly(diethylaminoethyl methacrylate) hydrogel systems,” AIChE Journal (2013), 59(10), 3578.
14 Gaharwar, A.K., N.A. Peppas, and A. Khademhossein, “Nanocomposite hydrogels for biomedical applications,” Biotech. Bioeng. (2014), 111(3), 441.
15 Peppas, N.A. and B. Narasimhan, “Mathematical models in drug delivery: how modeling has shaped the way we design new drug delivery systems,” J. Control. Release (2014), 190, 75.
16 Braff, D., D. Shis, and J.J. Collins, “Synthetic biology platform technologies for antimicrobial applications,” Advanced Drug Delivery Reviews (2016), 105, 35.

Biomedical Engineering Challenges

A Chemical Engineering Insight

List of Contributors

Preface

1
Introduction

References