Bionanocomposites in Tissue Engineering and Regenerative Medicine explores novel uses of these in tissue engineering and regenerative medicine. This book offers an interdisplinary approach, combining chemical, biomedical engineering, materials science and pharmacological aspects of the characterization, synthesis and application of bionanocomposites. Chapters cover a broad selection of bionanocomposites including chitosan, alginate and more, which are utilized in tissue engineering, wound healing, bone repair, drug formulation, cancer therapy, drug delivery, cartilage regeneration and dental implants. Additional sections of Bionanocomposites in Tissue Engineering and Regenerative Medicine discuss, in detail, the safety aspects and circular economy of bionanocomposites - offering an insight into the commercial and industrial aspects of these important materials. Bionanocomposites in Tissue Engineering and Regenerative Medicine will prove a highly useful text for for those in the fields of biomedical engineering, chemistry, pharmaceutics and materials science, both in academia and industrial R&D groups.

Current Research in Neuroadaptive Technology provides readers with insight into the state-of-the-art field of neuroadaptive technology. The book covers the breadth and depth of current research in this field, covering a range of application domains in sufficient technical detail. The multidisciplinary character of this field means that the publication of key research is often fragmented across specialist journals. Here, the editors have consolidated current research, carefully selecting key topics that are clustered around the concept of neuroadaptive technology. In summary, the book meets the needs of readers by consolidating multidisciplinary research around a nascent technological concept. The topic of neuroadaptive technology is novel and contemporary and editors Dr. Stephen H. Fairclough and Dr. Thorsten O. Zander have captured issues related to this emerging technology at the point of inception. It is a key reference for biomedical engineers and researchers in neural engineering, biomedical engineering, computer science, and mathematics.

Data Science for COVID-19 presents leading-edge research on data science techniques for the detection, mitigation, treatment and elimination of COVID-19. Sections provide an introduction to data science for COVID-19 research, considering past and future pandemics, as well as related Coronavirus variations. Other chapters cover a wide range of Data Science applications concerning COVID-19 research, including Image Analysis and Data Processing, Geoprocessing and tracking, Predictive Systems, Design Cognition, mobile technology, and telemedicine solutions. The book then covers Artificial Intelligence-based solutions, innovative treatment methods, and public safety. Finally, readers will learn about applications of Big Data and new data models for mitigation.

Data Science for COVID-19, Volume 2: Societal and Medical Perspectives presents the most current and leading-edge research into the applications of a variety of data science techniques for the detection, mitigation, treatment and elimination of the COVID-19 virus. At this point, Cognitive Data Science is the most powerful tool for researchers to fight COVID-19. Thanks to instant data-analysis and predictive techniques, including Artificial Intelligence, Machine Learning, Deep Learning, Data Mining, and computational modeling for processing large amounts of data, recognizing patterns, modeling new techniques, and improving both research and treatment outcomes is now possible.

Artificial Intelligence in Data Mining: Theories and Applications offers a comprehensive introduction to data mining theories, relevant AI techniques, and their many real-world applications. This book is written by experienced engineers for engineers, biomedical engineers, and researchers in neural networks, as well as computer scientists with an interest in the area.

In its second, extensively revised second edition, Semiconducting Silicon Nanowires for Biomedical Applications reviews the fabrication, properties, and biomedical applications of this key material. The book begins by reviewing the basics of growth, characterization, biocompatibility, and surface modification of semiconducting silicon nanowires. Attention then turns to use of these structures for tissue engineering and delivery applications, followed by detection and sensing. Reflecting the evolution of this multidisciplinary subject, several new key topics are highlighted, including our understanding of the cell-nanowire interface, latest advances in associated morphologies (including silicon nanoneedles and nanotubes for therapeutic delivery), and significantly, the status of silicon nanowire commercialization in biotechnology. Semiconducting Silicon Nanowires for Biomedical Applications is a comprehensive resource for biomaterials scientists who are focused on biosensors, drug delivery, and the next generation of nano-biotech platforms that require a detailed understanding of the cell-nanowire interface, along with researchers and developers in industry and academia who are concerned with nanoscale biomaterials, in particular electronically-responsive structures.

Spatiotemporal Modeling of Stem Cell Differentiation: Partial Differentiation Equation Analysis in R covers topics surrounding how stem cells evolve into specialized cells during tissue formation and in diseased tissue regeneration. As the process of stem cell differentiation occurs in space and time, the mathematical modeling of spatiotemporal development is expressed in this book as systems of partial differential equations (PDEs). In addition, the book explores important feature of six PDE model which can represent, for example, the development of tissue in organs. In addition, the book covers the computer-based implementation of example models through routines coded (programmed) in R. The routines described in the book are available from a download link so that example models can be executed without having to first study numerical methods and computer coding. The routines can then be applied to variations and extensions of the stem differentiation models, such as changes in the PDE parameters (constants) and the form of the model equations.

Micro and Nano Systems for Biophysical Studies of Cells and Small Organisms provides a comprehensive introduction to the state-of-the-art micro and nano systems that have recently been developed and applied to biophysical studies of cells and small organisms. These micro and nano systems span from microelectromechanical systems (MEMS) and microfluidic devices to robotic micro-nanomanipulation systems. These biophysical studies range from cell mechanics to the neural science of worms and Drosophila. This book will help readers understand the fundamentals surrounding the development of these tools and teach them the most recent advances in cellular and organismal biophysics enabled by these technologies.

Theory and Applications of Nonparenteral Nanomedicines presents thoroughly analysed data and results regarding the potential of nanomedicines conceived by diverse non-parenteral routes. In the context of nanotechnology-based approaches, various routes such as oral, pulmonary, transdermal, delivery and local administration of nanomedicine have been utilized for the delivery of nanomedicine. This book discusses the non-parenteral application of nanomedicine, its regulatory implications, application of mucus penetrating nanocarrier, and detailed chapters on development of nanomedicines developed for drug delivery by various route. Beginning with a brief introduction to the non-parenteral delivery of nanomedicine and the safety and regulatory implications of the nanoformulations, further chapters discuss the physiology of the biological barriers, the specificity of the nanocarriers as well as their multiple applications. Theory and Applications of Nonparenteral Nanomedicines helps clinical researchers, researchers working in pharmaceutical industries, graduate students, and anyone working in the development of non-parenteral nanomedicines to understand the recent progress in the design and development of nanoformulations compatible with non-parenteral applications.

Cell Instructive Materials to Control and Guide Cell Function: Programmable Bioactive Interfaces looks at the key determinants of the dynamic interface between cell and materials and how this can be applied in developing new, bioactive biomaterials surfaces. The interface between cell and synthetic materials has attracted considerable scientific and technological interest, leading to the awareness that functional interfaces can actively guide and control specific adhesion and recognitions events.

Metallic Biomaterials Processing and Medical Device Manufacturing details the principles and practices of the technologies used in biomaterials processing and medical device manufacturing. The book reviews the main categories of metallic biomaterials and the essential considerations in design and manufacturing of medical devices. It bridges the gap between the designing of biomaterials and manufacturing of medical devices including requirements and standards. Main themes of the book include, manufacturing, coatings and surface modifications of medical devices, metallic biomaterials and their mechanical behaviour, degradation, testing and characterization, and quality controls, standards and FDA regulations of medical devices. The leading experts in the filed discuss the requirements, challenges, recent progresses and future research directions in the processing of materials and manufacturing of medical devices. Metallic Biomaterials Processing and Medical Device Manufacturing is ideal for those working in the disciplines of materials science, manufacturing, biomedical engineering, and mechanical engineering.

Computational Intelligence and Its Applications in Healthcare presents rapidly growing applications of computational intelligence for healthcare systems, including intelligent synthetic characters, man-machine interface, menu generators, user acceptance analysis, pictures archiving, and communication systems. Computational intelligence is the study of the design of intelligent agents, which are systems that act intelligently: they do what they think are appropriate for their circumstances and goals; they're flexible to changing environments and goals; they learn from experience; and they make appropriate choices given perceptual limitations and finite computation. Computational intelligence paradigms offer many advantages in maintaining and enhancing the field of healthcare.

Health Care Paradigms in the Internet of Things Ecosystem brings all IoT-enabled health care related technologies into a single platform so that undergraduate and postgraduate students, researchers, academicians and industry leaders can easily understand IoT-based healthcare systems. The book uses data and network engineering and intelligent decision support system-by-design principles to design a reliable IoT-enabled health care ecosystem and to implement cyber-physical pervasive infrastructure solutions. It takes the reader on a journey that begins with understanding the healthcare monitoring paradigm in IoT-enabled technologies and how it can be applied in various aspects. In addition, the book walks readers through real-time challenges and presents a guide on how to build a safe infrastructure for IoT-based health care. It also helps researchers and practitioners understand the e-health care architecture through IoT and the state-of-the-art in IoT countermeasures. Readers will find this to be a comprehensive discussion on functional frameworks for IoT-based healthcare systems, intelligent medicine, RFID technology, HMI, Cognitive Interpretation, Brain-Computer Interface, Remote Health Monitoring systems, wearable sensors, WBAN, and security and privacy issues in IoT-based health care monitoring systems.

Foundational Handbook of Artificial Intelligence in Healthcare and Bioscience: A User Friendly Guide for IT Professionals, Healthcare Providers, Researchers, and Clinicians uses color-coded illustrations to explain AI from its basics to modern technologies. Other sections cover extensive, current literature research and citations regarding AI's role in the business and clinical aspects of health care. The book provides readers with a unique opportunity to appreciate AI technology in practical terms, understand its applications, and realize its profound influence on the clinical and business aspects of health care. Artificial Intelligence is a disruptive technology that is having a profound and growing influence on the business of health care as well as medical diagnosis, treatment, research and clinical delivery. The AI relationships in health care are complex, but understandable, especially when discussed and developed from their foundational elements through to their practical applications in health care.

Nature-Inspired Optimization Algorithms, Second Edition provides an introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, and multi-objective optimization. This book can serve as an introductory book for graduates, for lecturers in computer science, engineering and natural sciences, and as a source of inspiration for new applications.

Deep Learning (DL) is a method of machine learning, running over Artificial Neural Networks, that uses multiple layers to extract high-level features from large amounts of raw data. Deep Learning methods apply levels of learning to transform input data into more abstract and composite information. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications gives readers a complete overview of the essential concepts of Deep Learning and its applications in the field of Biomedical Engineering. Deep learning has been rapidly developed in recent years, in terms of both methodological constructs and practical applications. Deep Learning provides computational models of multiple processing layers to learn and represent data with higher levels of abstraction. It is able to implicitly capture intricate structures of large-scale data and is ideally suited to many of the hardware architectures that are currently available. The ever-expanding amount of data that can be gathered through biomedical and clinical information sensing devices necessitates the development of machine learning and AI techniques such as Deep Learning and Convolutional Neural Networks to process and evaluate the data. Some examples of biomedical and clinical sensing devices that use Deep Learning include: Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), Magnetic Particle Imaging, EE/MEG, Optical Microscopy and Tomography, Photoacoustic Tomography, Electron Tomography, and Atomic Force Microscopy. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications provides the most complete coverage of Deep Learning applications in biomedical engineering available, including detailed real-world applications in areas such as computational neuroscience, neuroimaging, data fusion, medical image processing, neurological disorder diagnosis for diseases such as Alzheimer's, ADHD, and ASD, tumor prediction, as well as translational multimodal imaging analysis.

Nanoemulsions are produced by mixing an oil phase with an aqueous phase under shear pressure. This procedure yields uniform populations of oil droplets ranging in diameter from 200 to 8 nm that are kinetically stable colloidal substances with enhanced properties compared to the conventional emulsion substances. Nanoemulsions have broad potential applications in agriculture, food, health, and biomedical sciences. Nanoemulsion Applications in Agriculture, Food, Health, and Biomedical Sciences focuses on the aspects of nanoemulsion-like synthesis, characterization, and more and examines recent trends in their applications within a variety of relevant fields. Nanoemulsions have broad application in many different fields; without emulsification, process product development would not be possible. Covering topics such as cancer treatment, healthcare applications, and food manufacturing, this book is essential for scientists, doctors, researchers, post-graduate students, medical students, government officials, hospital directors, professors, and academicians.

Terahertz Biomedical and Healthcare Technologies: Materials to Devices reviews emerging advances in terahertz biomedical and healthcare technologies, including advances in fundamental materials science research, device design and fabrication, applications, and challenges and opportunities for improved performance. In addition, the improvement of materials, optical elements, and measuring techniques are also explored. Other sections cover the design and development of wide bandgap semiconductors for terahertz device applications, including their physics, device modeling, characterization and fabrication concepts. Finally, the book touches on potential defense, medical imaging, internet of things, and the machine learning applications of terahertz technologies.

3D Printing in Medicine and Surgery: Applications in Healthcare is an advanced book on surgical and enhanced medical applications that can be achieved with 3D printing. It is an essential handbook for medical practitioners, giving access to a range of practical methods, while also focusing on applied knowledge. This comprehensive resource features practical experiments and processes for preparing 3D printable materials. Early chapters cover foundational knowledge and background reading, while later chapters discuss and review the current technologies used to engineer specific tissue types, experiments and methods, medical approaches and the challenges that lie ahead for future research. The book is an indispensable reference guide to the various methods used by current medical practitioners working at the forefront of 3D printing applications in medicine.

ODE/PDE Analysis of Antibiotic/Antimicrobial Resistance: Programming in R presents mathematical models for antibiotic/antimicrobial resistance based on ordinary and partial differential equations (ODE/PDEs). Sections cover the basic ODE model, the detailed PDE model that gives the spatiotemporal distribution of four dependent variable components, including susceptible bacteria population density, resistant bacteria population density, plasmid number, and antibiotic concentration. The computer-based implementation of the example models is presented through routines coded (programmed) in R, a quality, open-source scientific computing system that is readily available from the Internet. As such, formal mathematics is minimized and no theorems and proofs are required. The PDE analysis is based on the method of lines (MOL), an established general algorithm for PDEs that is implemented with finite differences. Routines are available from a download link so that the example models can be executed without having to first study numerical methods and computer coding. Routines can then be applied to variations and extensions of the antibiotic/antimicrobial models, such as changes in the ODE/PDE parameters (constants) and the form of the model equations.

Digital Health: Mobile and Wearable Devices for Participatory Health Applications is a key reference for engineering and clinical professionals considering the development or implementation of mobile and wearable solutions in the healthcare domain. The book presents a comprehensive overview of devices and appropriateness for the respective applications. It also explores the ethical, privacy, and cybersecurity aspects inherent in networked and mobile technologies. It offers expert perspectives on various approaches to the implementation and integration of these devices and applications across all areas of healthcare. The book is designed with a multidisciplinary audience in mind; from software developers and biomedical engineers who are designing these devices to clinical professionals working with patients and engineers on device testing, human factors design, and user engagement/compliance.

Transhumanism is widely misunderstood, in part because the media have exaggerated current technologies and branded the movement as dangerous, leading many to believe that hybrid humans may soon walk among us and that immortality, achieved by means of mind-uploading, is imminent. In this essential and clarifying volume, Stefan Lorenz Sorgner debunks widespread myths about transhumanism and tackles the most pressing ethical issues in the debate over technologically assisted human enhancement. On Transhumanism is a vital primer on the subject, written by a world-renowned expert. In this book, Sorgner presents an overview of the movement's history, capably summarizing the twelve pillars of transhumanist discourse and explaining the great diversity of transhumanist responses to each individual topic. He highlights the urgent ethical challenges related to the latest technological developments, inventions, and innovations and compares the unique cultural standing of transhumanism to other cultural movements, placing it within the broader context of the Enlightenment, modernity, postmodernity, and the philosophical writings of Nietzsche. Engagingly written and translated and featuring an introduction for North American readers, this comprehensive overview of the cultural and philosophical movement of transhumanism will be required reading for students of posthumanist philosophy and for general audiences interested in learning about the transhumanist movement.

Biomaterials for 3D Tumor Modeling reviews the fundamentals and most relevant areas of the latest advances of research of 3D cancer models, focusing on biomaterials science, tissue engineering, drug delivery and screening aspects. The book reviews advanced fundamental topics, including the causes of cancer, existing cancer models, angiogenesis and inflammation during cancer progression, and metastasis in 3D biomaterials. Then, the most relevant biomaterials are reviewed, including methods for engineering and fabrication of biomaterials. 3D models for key biological systems and types of cancer are also discussed, including lung, liver, oral, prostate, pancreatic, ovarian, bone and pediatric cancer. This book is suitable for those working in the disciplines of materials science, biochemistry, genetics, molecular biology, drug delivery and regenerative medicine.

Storing Digital Binary Data into Cellular DNA demonstrates how current digital information storage systems have short longevity and limited capacity, also pointing out that their production and consumption of data exceeds supply. Author Rocky Termanini explains the DNA system and how it encodes vast amounts of data, then presents information on the emergence of DNA as a storage technology for the ever-growing stream of data being produced and consumed. The book will be of interest to a range of readers looking to understand this game-changing technology, including researchers in computer science, biomedical engineers, geneticists, physicians, clinicians, law enforcement and cybersecurity experts.

Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning in health sciences and biomedical data. The book is useful for those working with big data analytics in biomedical research, medical industries, and medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data, covering areas such as predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction using predictive modeling. Case studies demonstrate big data applications in healthcare using the MapReduce and Hadoop frameworks.