Nowadays, Virtual Reality (VR) is commonly used in various applications including entertainment, education and training, manufacturing, medical and rehabilitation. VR not only provides immersive stereoscopic visualization of virtual environments and the visualization effect and computer graphics are critical to enhancing the engagement of participants and thus increases education and training effectiveness. Nevertheless, constructing realistic 3D models and scenarios for a specific application of VR simulation is not an easy task. There are many different tools for 3D modelling such as ZBrush, Blender, SketchUp, AutoCAD, SolidWorks, 3Ds Max, Maya, Rhino3D, CATIA, and more. Many of the modelling tools are very professional and used for manufacturing and product design application. The advanced features and functions may not be applicable to different levels of users and various specialization. This book explores the application of virtual reality in healthcare settings. This includes 3D modelling techniques, texturing, assigning material, and more. It allows for not only modelling and rendering techniques, but modelling, dressing, and animation in healthcare applications. The potential market of readers, including those from the engineering disciplines such as computer sciences/ computer engineering, product designers, and more. Other potential readers are those studying nursing and medicine, healthcare workers, and anyone interested in the development of VR applications for industry use. In addition, this is suitable for readers from other industries that may need to apply virtual reality in their field.

The cybersecurity of connected medical devices is one of the biggest challenges facing healthcare today. The compromise of a medical device can result in severe consequences for both patient health and patient data. Cybersecurity for Connected Medical Devices covers all aspects of medical device cybersecurity, with a focus on cybersecurity capability development and maintenance, system and software threat modeling, secure design of medical devices, vulnerability management, and integrating cybersecurity design aspects into a medical device manufacturer's Quality Management Systems (QMS). This book is geared towards engineers interested in the medical device cybersecurity space, regulatory, quality, and human resources specialists, and organizational leaders interested in building a medical device cybersecurity program.

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.

The Epigenetics of Autoimmunity covers a topic directly related to translational epigenetics. Via epigenetic mechanisms, a number of internal and external environmental risk factors, including smoking, nutrition, viral infection and the exposure to chemicals, could exert their influence on the pathogenesis of autoimmune diseases. Such factors could impact the epigenetic mechanisms, which, in turn, build relationship with the regulation of gene expression, and eventually triggering immunologic events that result in instability of immune system. Since epigenetic aberrations are known to play a key role in a long list of human diseases, the translational significance of autoimmunity epigenetics is very high. To bridge the gap between environmental and genetic factors, over the past few years, great progress has been made in identifying detailed epigenetic mechanisms for autoimmune diseases. Furthermore, with rapid advances in technological development, high-throughput screening approaches and other novel technologies support the systematic investigations and facilitate the epigenetic identification. This book covers autoimmunity epigenetics from a disease-oriented perspective and several chapters are presented that provide advances in wide-spread disorders or diseases such as systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), multiple sclerosis (MS), type 1 diabetes (T1DM), systemic sclerosis (SSc), primary Sjoegren's syndrome (pSS) and autoimmune thyroid diseases (AITDs). These emerging epigenetic studies provide new insights into autoimmune diseases, raising great expectations among researchers and clinicians. This seminal book on this topic comprehensively covers the most recent advances in this exciting and rapidly developing new science. They might reveal not only new clinical biomarkers for diagnosis and disease progression, but also novel targets for potential epigenetic therapeutic treatment.

Deep Learning for Chest Radiographs enumerates different strategies implemented by the authors for designing an efficient convolution neural network-based computer-aided classification (CAC) system for binary classification of chest radiographs into "Normal" and "Pneumonia." Pneumonia is an infectious disease mostly caused by a bacteria or a virus. The prime targets of this infectious disease are children below the age of 5 and adults above the age of 65, mostly due to their poor immunity and lower rates of recovery. Globally, pneumonia has prevalent footprints and kills more children as compared to any other immunity-based disease, causing up to 15% of child deaths per year, especially in developing countries. Out of all the available imaging modalities, such as computed tomography, radiography or X-ray, magnetic resonance imaging, ultrasound, and so on, chest radiographs are most widely used for differential diagnosis between Normal and Pneumonia. In the CAC system designs implemented in this book, a total of 200 chest radiograph images consisting of 100 Normal images and 100 Pneumonia images have been used. These chest radiographs are augmented using geometric transformations, such as rotation, translation, and flipping, to increase the size of the dataset for efficient training of the Convolutional Neural Networks (CNNs). A total of 12 experiments were conducted for the binary classification of chest radiographs into Normal and Pneumonia. It also includes in-depth implementation strategies of exhaustive experimentation carried out using transfer learning-based approaches with decision fusion, deep feature extraction, feature selection, feature dimensionality reduction, and machine learning-based classifiers for implementation of end-to-end CNN-based CAC system designs, lightweight CNN-based CAC system designs, and hybrid CAC system designs for chest radiographs. This book is a valuable resource for academicians, researchers, clinicians, postgraduate and graduate students in medical imaging, CAC, computer-aided diagnosis, computer science and engineering, electrical and electronics engineering, biomedical engineering, bioinformatics, bioengineering, and professionals from the IT industry.

Self-assessment Q&A in Clinical Laboratory Science, III, adds a variety of subject matter that addresses new concepts and emerging technology, particularly in the areas of kidney biomarkers, cancer biomarkers, molecular diagnostics, multiple myeloma, pharmacogenomics, novel cardiovascular biomarkers and biomarkers of neurologic diseases. The field of Clinical Laboratory Science continues to evolve and editor Alan Wu has once again brought together experts in the field to cover the contemporary topics that are being tested today. This updated bank of questions and answers is a must-have to sharpen knowledge and skills.

Virtual Reality (VR) is the use of computer technology to construct an environment that is simulated. VR places the user inside and in the center of the experience, unlike conventional user interfaces. Users are immersed and able to connect with 3D environments instead of seeing a screen in front of them. The computer has to role to provide the experiences of the user in this artificial environment by simulating as many senses as possible, such as sight, hearing, touch and smell. In Augmented Reality (AR) we have an enhanced version of the real physical world that is achieved through the use of digital visual elements, sound, or other sensory stimuli delivered via technology. It can be seen as VR imposed into real life. In both VR and AR the experience is composed of a virtual or extended world, an immersion technology, sensory feedback and interactivity. These elements use a multitude of technologies that must work together and presented to the user seamlessly integrated and synchronized. This book is dedicated to applications, new technologies and emerging trends in the fields of virtual reality and augmented reality in healthcare. It is intended to cover technical areas as well as areas of applied intervention. It is expected to cover hardware and software technologies while encompassing all components of the virtual experience. The main goal of this book is to show how to put Virtual Reality in action by linking academic and informatics researchers with professionals who use and need VR in their day-a-day work, with a special focus on healthcare professionals and related areas. The idea is to disseminate and exchange the knowledge, information and technology provided by the international communities in the area of VR, AR and XR throughout the 21st century. Another important goal is to synthesize all the trends, best practices, methodologies, languages and tools which are used to implement VR. In order to shape the future of VR, new paradigms and technologies should be discussed, not forgetting aspects related to regulation and certification of VR technologies, especially in the healthcare area. These last topics are crucial for the standardization of VR. This book will present important achievements and will show how to use VR technologies in a full range of settings able to provide decision support anywhere and anytime using this new approach.

Computational Retinal Image Analysis: Tools, Applications and Perspectives gives an overview of contemporary retinal image analysis (RIA) in the context of healthcare informatics and artificial intelligence. Specifically, it provides a history of the field, the clinical motivation for RIA, technical foundations (image acquisition modalities, instruments), computational techniques for essential operations, lesion detection (e.g. optic disc in glaucoma, microaneurysms in diabetes) and validation, as well as insights into current investigations drawing from artificial intelligence and big data. This comprehensive reference is ideal for researchers and graduate students in retinal image analysis, computational ophthalmology, artificial intelligence, biomedical engineering, health informatics, and more.

Clinical Engineering: A Handbook for Clinical and Biomedical Engineers, Second Edition, helps professionals and students in clinical engineering successfully deploy medical technologies. The book provides a broad reference to the core elements of the subject, drawing from a range of experienced authors. In addition to engineering skills, clinical engineers must be able to work with both patients and a range of professional staff, including technicians, clinicians and equipment manufacturers. This book will not only help users keep up-to-date on the fast-moving scientific and medical research in the field, but also help them develop laboratory, design, workshop and management skills. The updated edition features the latest fundamentals of medical technology integration, patient safety, risk assessment and assistive technology.

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.

Genomics in the Clinic: A Practical Guide to Genetic Testing, Evaluation, and Counseling illustrates the current scope of the practice of genetics for healthcare professionals, so they can understand principles applicable to genetic testing and consultation. Written by an authoritative well-balanced team, including experienced clinical geneticists, genetic counselors, and medical subspecialists, this book adopts an accessible, easy-to-follow format. Sections are dedicated to basic genetic principles; clinical genetic and genomic testing; prenatal, clinical and cancer genetic diagnosis and counseling; and ethical and social implications in genomic medicine. Over 100 illustrative cases examine a range of prenatal, pediatric and adult genetic conditions and testing, putting these concepts and approaches into practice. Genomics in the Clinic: A Practical Guide to Genetic Testing, Evaluation, and Counseling is important for primary care providers, as patient care evolves in the current genomic-influenced world of precision medicine.

Resistance to Anti-CD20 Antibodies and Approaches for Their Reversal presents in-depth content written by international experts in the study of resistance to anti-CD20 antibodies and approaches for their reversal. Anti-CD20 antibodies are used to achieve B cell depletion and are developed to treat B cell proliferative disorders, including non-Hodgkin’s lymphoma and chronic lymphocytic leukemia. In the past two decades, anti-CD20 antibodies have revolutionized the treatment of all B cell malignancies, however, there are patients that fail to respond to initial therapy or relapse sooner. This book explores new and existing avenues surrounding Anti-CD20 antibodies. In recent years, several next-generation anti-CD20 therapies have been developed but predicting and reversing resistance is still a challenging task. These areas are being actively studied as they represent a potential to improve anti-CD20 therapies and are discussed thoroughly in the book. It is a valuable resource for researchers, students and member of the biomedical and medical fields who want to learn more about resistance to anti-CD20 antibodies and their reversal.

Data for Nurses: Understanding and Using Data to Optimize Care Delivery in Hospitals and Health Systems provides information for nurses on how to work with data to effectively evaluate and improve care delivery for patients. Quality, benchmarking, and research data are increasingly used to guide care in hospitals and health systems, and nurses are expected to actively use this information to identify interventions to optimize outcomes and meet reporting and financial targets. However, not all nurses receive formal training on data utilization, making interpretation and application of the different types of data difficult. This book provides information on topics such as benchmarking and reportable indicators, financial metrics, quality improvement, research, and implementation science, with applications to nursing practice. Important information on protective measures to guarantee integrity and security of personal patient data is also included. The book is a valuable resource for nurses and other healthcare professionals who require a basic understanding of key principles of data utilization in order to increase engagement in evidence-based practices, quality improvement, and mandatory reporting of key indices.

Advances in Cancer Research, Volume 144, the latest release in this ongoing, well-regarded serial, provides invaluable information on the exciting and fast-moving field of cancer research. Chapters in this new release include Gene-Environment-Microenvironment Interactions in Melanomagenesis, PP2A and the Cell Cycle, Current Progress Defining Calcium Signals as Therapeutic Targets in Cancer Cells, and much more.

Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research provides a comprehensive introduction to the fundamental principles and methods that healthcare researchers need when considering, conducting or using individual participant data (IPD) meta-analysis projects. Written and edited by researchers with substantial experience in the field, the book details key concepts and practical guidance for each stage of an IPD meta-analysis project, alongside illustrated examples and summary learning points. Split into five parts, the book chapters take the reader through the journey from initiating and planning IPD projects to obtaining, checking, and meta-analysing IPD, and appraising and reporting findings. The book initially focuses on the synthesis of IPD from randomised trials to evaluate treatment effects, including the evaluation of participant-level effect modifiers (treatment-covariate interactions). Detailed extension is then made to specialist topics such as diagnostic test accuracy, prognostic factors, risk prediction models, and advanced statistical topics such as multivariate and network meta-analysis, power calculations, and missing data. Intended for a broad audience, the book will enable the reader to: Understand the advantages of the IPD approach and decide when it is needed over a conventional systematic review Recognise the scope, resources and challenges of IPD meta-analysis projects Appreciate the importance of a multi-disciplinary project team and close collaboration with the original study investigators Understand how to obtain, check, manage and harmonise IPD from multiple studies Examine risk of bias (quality) of IPD and minimise potential biases throughout the project Understand fundamental statistical methods for IPD meta-analysis, including two-stage and one-stage approaches (and their differences), and statistical software to implement them Clearly report and disseminate IPD meta-analyses to inform policy, practice and future research Critically appraise existing IPD meta-analysis projects Address specialist topics such as effect modification, multiple correlated outcomes, multiple treatment comparisons, non-linear relationships, test accuracy at multiple thresholds, multiple imputation, and developing and validating clinical prediction models Detailed examples and case studies are provided throughout.