Titanium Alloys for Biomedical Development and Applications: Design, Microstructure, Properties and Application systematically introduces basic theories and progress in the research of biomedical ss-Ti alloys achieved by researchers from different fields. It focuses on a high-strength and low elastic modulus biomedical ss-Ti alloy (TLM), etc. designed by the authors. The alloy design methods, microstructural characteristics, mechanical properties, surface treatment methods and biocompatibility of the TLM alloy are discussed in detail, along with a concise description of the medical devices made from this alloy and the application examples. This book will appeal to researchers as well as students from different disciplines, including materials science, biology, medicine and engineering fields.

Biomedical Applications of Electrospinning and Electrospraying describes the principles and laboratory set up for electrospinning and electrospraying, addressing a range of biomedical applications. Sections cover novel combinational approaches, such as electrospinning/spraying and 3D printing. Electrospinning has evolved from being a technique to prepare random networks of textile fibers to a technique to fabricate highly ordered patterns of biomedical materials of defined scale. The technological advancements in recent years with regard to the way the jet is facilitated, how the jet path is controlled, and how the fibers are collected have provided invaluable insights into controlled fabrication of a material of choice. Additionally, the electrospray technique has also evolved from being a technique to prepare food formulations to a technique to prepare cell encapsulated beads for transplantation in clinics. Several innovations in this line, such as those leading to core-shell materials have tremendously changed the way the technique is used. Thus, a combinational approach using electrospinning, electrospraying and 3D printing has emerged.

Introduction to Biomedical Instrumentation and Its Applications delivers a detailed overview of the various instruments used in the biomedical and healthcare domain, focusing on both their main features and their uses in the medical industry. Each chapter focuses on biomedical instrumentation in a different medical discipline, covering a range of different topics including radiological devices, instruments used for blood analysis, defibrillators, ventilators, nerve stimulators and baby incubators. This book seeks to provide the reader with in-depth knowledge on biomedical devices, thus enabling them to contribute to the future development of instruments in the healthcare domain. This is a concise handbook that will be useful to students, researchers and practitioners involved in biomedical engineering, as well as doctors and clinicians who specialize in areas such as cardiology, anesthesiology and physiotherapy.

Emerging Nanomedicines for Diabetes Mellitus Theranostics provides readers with information on the development of efficacious nanomedicines as potential theranostic agents for diabetes. The book discusses the application of various novel nanomaterials and nanocomposites for targeted delivery of insulin, glucose sensing, including nano-tattoos as glucose monitors, biosynthesized nanoparticles for diabetes treatment, and pre-clinical and clinical assays to evaluate the efficacy of nanomedicines for diabetes treatment. This is an important references source for materials scientists, pharmaceutical scientists and biomedical engineers who want to increase their understanding of how nanotechnology is being used to improve diabetes treatment. Diabetes has emerged as one of the most common diseases associated with lifestyle choices in the modern world, with significant mortality rates. Conventional treatment methods mainly involve insulin-based therapies. However, insulin therapy possesses several limitations such as weight gain and hypoglycemia. Thus, advanced research in nanomedicine is targeting the development of new and improved diagnostics and treatment methods for diabetes.

Nanotherapeutics in Cancer Vaccination and Challenges consolidates the current research on cancer nanomedicine and therapeutic cancer vaccination to explore the most effective and promising avenues. The book covers cancer vaccines before exploring nanotherapeutics, DNA and mRNA vaccines in cancer treatment. Finally, it considers regulatory and industrial perspectives on cancer vaccination and nanotherapeutics. This resource will be useful for pharmaceutical scientists and researchers focused on biomedical engineering, chemical engineering, vaccine development, and cancer immunotherapy, along with advanced students in these subjects. Cancer is arguably the most complex and challenging disease known to mankind. Over the last two-decades, significant advancements have been made in new and novel concepts of cancer nanomedicines. Therapeutic cancer vaccines may be utilized to inhibit further growth of advanced cancers and/or relapsed tumors that are refractory to conventional therapies, such as surgery, radiation therapy and chemotherapy.

Generative Adversarial Networks (GAN) have started a revolution in Deep Learning, and today GAN is one of the most researched topics in Artificial Intelligence. Generative Adversarial Networks for Image-to-Image Translation provides a comprehensive overview of the GAN (Generative Adversarial Network) concept starting from the original GAN network to various GAN-based systems such as Deep Convolutional GANs (DCGANs), Conditional GANs (cGANs), StackGAN, Wasserstein GANs (WGAN), cyclical GANs, and many more. The book also provides readers with detailed real-world applications and common projects built using the GAN system with respective Python code. A typical GAN system consists of two neural networks, i.e., generator and discriminator. Both of these networks contest with each other, similar to game theory. The generator is responsible for generating quality images that should resemble ground truth, and the discriminator is accountable for identifying whether the generated image is a real image or a fake image generated by the generator. Being one of the unsupervised learning-based architectures, GAN is a preferred method in cases where labeled data is not available. GAN can generate high-quality images, images of human faces developed from several sketches, convert images from one domain to another, enhance images, combine an image with the style of another image, change the appearance of a human face image to show the effects in the progression of aging, generate images from text, and many more applications. GAN is helpful in generating output very close to the output generated by humans in a fraction of second, and it can efficiently produce high-quality music, speech, and images.

State of the Art in Neural Networks and Their Applications presents the latest advances in artificial neural networks and their applications across a wide range of clinical diagnoses. Advances in the role of machine learning, artificial intelligence, deep learning, cognitive image processing and suitable data analytics useful for clinical diagnosis and research applications are covered, including relevant case studies. The application of Neural Network, Artificial Intelligence, and Machine Learning methods in biomedical image analysis have resulted in the development of computer-aided diagnostic (CAD) systems that aim towards the automatic early detection of several severe diseases. State of the Art in Neural Networks and Their Applications is presented in two volumes. Volume 1 covers the state-of-the-art deep learning approaches for the detection of renal, retinal, breast, skin, and dental abnormalities and more.

Deep Learning for Medical Applications with Unique Data informs readers about the most recent deep learning-based medical applications in which only unique data gathered in real cases are used. The book provides examples of how deep learning can be used in different problem areas and frameworks in both clinical and research settings, including medical image analysis, medical image registration, time series analysis, medical data synthesis, drug discovery, and pre-processing operations. The volume discusses not only positive findings, but also negative ones obtained by deep learning techniques, including the use of newly developed deep learning techniques rarely reported in the existing literature. The book excludes research works with ready data sets and includes only unique data use to better understand the state of deep learning in real-world cases, along with the feedback and user experiences from physicians and medical staff for applied deep learning-based solutions. Other applications presented in the book include hybrid solutions with deep learning support, disease diagnosis with deep learning focusing on rare diseases and cancer, patient care and treatment, genomics research, as well as research on robotics and autonomous systems.

Polysaccharide-Based Nanocomposites for Gene Delivery and Tissue Engineering presents quantitative background on new polysaccharide nanocomposites in a clear and logical way, highlighting the most exciting applications in gene delivery and tissue engineering and their progress. The book focuses on the different types of polysaccharide nanocomposites for gene delivery and tissue engineering and covers polysaccharide hydrogels for tissue engineering and polysaccharide magnetic nanocomposites for gene delivery. Chapters cover various nanocomposites presented in twenty-one separate chapters. This book will be of great interest to all those researching the development and applications of polysaccharide-based nanocomposites for modeling. As polysaccharide-based nanocomposites promise cutting-edge applications in gene delivery and tissue engineering, with their development at the forefront of modern medicine, this book is a welcome title on this exciting science.

Autonomous Robot-Aided Optical Manipulation for Biological Cells gives a systematically and almost self-contained description of the many facets of modeling, sensing, and control techniques or experimentally exploring emerging trends in optical manipulation of biological cell in micro/nanorobotics systems. To achieve biomedical applications, reliability design, modeling, and precision control are vitally important for the development of engineering systems. With the advances in modeling, sensing, and control techniques, it is opportunistic to exploit them for the benefit of reliability design, actuation, and precision control of micro/nanomanipulation systems to expanding the applications of robot at the micro and nano scales, especially in biomedical engineering. This book presents new techniques in reliability modeling and advanced control of robot-aided optical manipulation of biological cells systems. The book will be beneficial to the researchers within robotics, mechatronics, biomedical engineering, and automatic control society, including both academic and industrial parts.

Nanotechnology in Conservative Dentistry provides a detailed review of the use of nanotechnology in conservative dentistry, from diagnosis and restorative materials, through to tissue engineering and regeneration. This book covers fundamental topics in the field of conservative dentistry, including caries therapy, dentin reconstruction, pulp protection and more; each chapter reviews and discusses how nanotechnology can be implemented as a novel approach to traditional conservative dentistry techniques, exploring the many uses and advantages of this fast-growing technology. Various nanobiomaterials and technologies are covered, as well as assessment of the biocompatibility and toxicological risks of utilizing nanotechnology in dentistry. Nanotechnology in Conservative Dentistry will help dentists and materials science academics alike, understand the potential of nanotechnology in dentistry, building on and going beyond traditional concepts and techniques in this field.

Biomaterials have existed for millennia as mechanical replacement structures following disease or injury. Biomaterial design has changed markedly from structural support with an "inert" immune profile as the primary objective to designs that elicit an integrative local tissue response and a pro-repair immune cell phenotype. Immunomodulatory Biomaterials: Regulating the Immune Response with Biomaterials to Affect Clinical Outcome offers a single, comprehensive reference on biomaterials for modulation of the host response, for materials scientists, tissue engineers and those working in regenerative medicine. This book details methods, materials and strategies designed to regulate the host immune response following surgical implantation and thus facilitate specific local cell infiltration and tissue deposition. There has been a dramatic transformation in our understanding of the role of the immune system, both innate and adaptive; these changes include recognition of the plasticity of immune cells, especially macrophages, cross-talk between the immune system and stem cells, and the necessity for in situ transition between inflammatory and regulatory immune cell phenotypes. The exploitation of these findings and the design and manufacture of new biomaterials is occurring at an astounding pace. There is currently no book directed at the interdisciplinary principles guiding the design, manufacture, testing, and clinical translation of biomaterials that proactively regulate the host tissue immune response. The challenge for academia, industry, and regulatory agencies to encourage innovation while assuring safety and maximizing efficacy has never been greater. Given the highly interdisciplinary requirements for the design, manufacture and use of immunomodulatory biomaterials, this book will prove a useful single resource across disciplines.

Bioinspired and Biomimetic Materials for Drug Delivery delves into the potential of bioinspired materials in drug delivery, detailing each material type and its latest developments. In the last decade, biomimetic and bioinspired materials and technology has garnered increased attention in drug delivery research. Various material types including polymer, small molecular, protein, peptide, cholesterol, polysaccharide, nano-crystal and hybrid materials are widely considered in drug delivery research. However, biomimetic and bioinspired materials and technology have shown promising results for use in therapeutics, due to their high biocompatibility and reduced immunogenicity. Such materials include dopamine, extracellular exosome, bile acids, ionic liquids, and red blood cell. This book covers each of these materials in detail, reviewing their potential and usage in drug delivery. As such, this book will be a great source of information for biomaterials scientists, biomedical engineers and those working in pharmaceutical research.

Advances in Cancer Biomarkers Research provides a thorough and detailed description of cancer biomarkers for diagnostic, prognostic and therapeutics in several cancer types. The book presents a compendium of topics related to current advanced research, along with fundamental knowledge that will help readers fully comprehend the field of cancer biomarkers. Topics discussed include such the role of genetic mechanisms, epigenetics, DNA and microRNA in different cancers, signaling pathways and exosomes. In addition, the book discusses biomarker research applied to several cancer types, such as head and neck, urological, lung, bone tumors, hematological and neurological malignancies and breast cancers.This will be a valuable resource for cancer researchers, oncologists, graduate students and members of the biomedical field who are interested in the potential of biomarkers in cancer research and treatment.

In recent years, scientific research and translation medicine have placed increased emphasis on computational methodology and data curation across many disciplines, both to advance underlying science and to instantiate precision-medicine protocols in the lab and in clinical practice. The nexus of concerns related to oncology, cardiology, and virology (SARS-CoV-2) presents a fortuitous context within which to examine the theory and practice of biomedical data curation. Innovative Data Integration and Conceptual Space Modeling for COVID, Cancer, and Cardiac Care argues that a well-rounded approach to data modeling should optimally embrace multiple perspectives inasmuch as data-modeling is neither a purely formal nor a purely conceptual discipline, but rather a hybrid of both. On the one hand, data models are designed for use by computer software components, and are, consequently, constrained by the mechanistic demands of software environments; data modeling strategies must accept the formal rigors imposed by unambiguous data-sharing and query-evaluation logic. In particular, data models are not well-suited for software-level deployment if such models do not translate seamlessly to clear strategies for querying data and ensuring data integrity as information is moved across multiple points. On the other hand, data modeling is, likewise, constrained by human conceptual tendencies, because the information which is managed by databases and data networks is ultimately intended to be visualized/utilized by humans as the end-user. Thus, at the intersection of both formal and humanistic methodology, data modeling takes on elements of both logico-mathematical frameworks (e.g., type systems and graph theory) and conceptual/philosophical paradigms (e.g., linguistics and cognitive science). The authors embrace this two-sided aspect of data models by seeking non-reductionistic points of convergence between formal and humanistic/conceptual viewpoints, and by leveraging biomedical contexts (viz., COVID, Cancer, and Cardiac Care) so as to provide motivating examples and case-studies in this volume.

Principles and Labs for Deep Learning provides the knowledge and techniques needed to help readers design and develop deep learning models. Deep Learning techniques are introduced through theory, comprehensively illustrated, explained through the TensorFlow source code examples, and analyzed through the visualization of results. The structured methods and labs provided by Dr. Huang and Dr. Le enable readers to become proficient in TensorFlow to build deep Convolutional Neural Networks (CNNs) through custom APIs, high-level Keras APIs, Keras Applications, and TensorFlow Hub. Each chapter has one corresponding Lab with step-by-step instruction to help the reader practice and accomplish a specific learning outcome. Deep Learning has been successfully applied in diverse fields such as computer vision, audio processing, robotics, natural language processing, bioinformatics and chemistry. Because of the huge scope of knowledge in Deep Learning, a lot of time is required to understand and deploy useful, working applications, hence the importance of this new resource. Both theory lessons and experiments are included in each chapter to introduce the techniques and provide source code examples to practice using them. All Labs for this book are placed on GitHub to facilitate the download. The book is written based on the assumption that the reader knows basic Python for programming and basic Machine Learning.

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.

Wearable Telemedicine Technology for the Healthcare Industry: Product Design and Development focuses on recent advances and benefits of wearable telemedicine techniques for remote health monitoring and prevention of chronic conditions, providing real time feedback and help with rehabilitation and biomedical applications. Readers will learn about various techniques used by software engineers, computer scientists and biomedical engineers to apply intelligent systems, artificial intelligence, machine learning, virtual reality and augmented reality to gather, transmit, analyze and deliver real-time clinical and biological data to clinicians, patients and researchers. Wearable telemedicine technology is currently establishing its place with large-scale impact in many healthcare sectors because information about patient health conditions can be gathered anytime and anywhere outside of traditional clinical settings, hence saving time, money and even lives.

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.

Mechanical Design of Piezoelectric Energy Harvesters: Generating Electricity from Human Walking provides the state-of-the-art, recent mechanical designs of piezoelectric energy harvesters based on piezoelectric stacks. The book discusses innovative mechanism designs for energy harvesting from multidimensional force excitation, such as human walking, which offers higher energy density. Coverage includes analytical modeling, optimal design, simulation study, prototype fabrication, and experimental investigation. Detailed examples of their analyses and implementations are provided. The book's authors provide a unique perspective on this field, primarily focusing on novel designs for PZT Energy harvesting in biomedical engineering as well as in integrated multi-stage force amplification frame. This book presents force-amplification compliant mechanism design and force direction-transmission mechanism design. It explores new mechanism design approaches using piezoelectric materials and permanent magnets. Readers can expect to learn how to design new mechanisms to realize multidimensional energy harvesting systems.

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.