The definitive guide to the law that all nurses need to know. Written specifically for student nurses as well as those already in practice, Dimond's Legal Aspects of Nursing is your essential practical guide to the legal principles you need to be aware of in your everyday nursing practice. Building on previous editions of the book by Bridgit Dimond, this 8th edition has been significantly reworked by a new author team with extensive experience in teaching nursing law. It has also been fully updated and revised in line with recent legal developments and the new Nursing standards to ensure it continues to meet the requirements of nursing law modules. New to this edition: Introduction of new and updated Nursing Midwifery Council (NMC) Fitness to Practise procedures Reference to the NMC Code 2015 (updated 2018) including Duty of candour Data Protection legislation updated including reference to the General Data Protection Regulation 2016 Greater reference to the devolved UK administrations Updated overview of a nurses' duty of care Reference to the new NMC approved curriculum, and the introduction of nursing associates Introduction of upcoming changes to the Mental Capacity Act 2005 Comprehensive discussion of the practice implications of the Supreme Court Decisions in Montgomery v Lanarkshire Health Board [2015] Consideration of the revised Health and Social Care Act 2008 (regulated activities) regulations 2014 Updated consideration of gross negligence manslaughter Practical implications of the extension of the crimes of ill treatment and willful neglect under the Criminal Justice and Courts Act 2015 section 20 and 21

Natural Biopolymers in Drug Delivery and Tissue Engineering systematically examines a broad range of natural polymers and their applications in drug delivery and tissue engineering. The book thoroughly collates the most relevant and up-to-date research on natural biopolymers, covering a variety of key natural polymer types such as chitin, chitosan, alginate, guar gum and collagen. It is divided into two sections, covering drug delivery and tissue engineering applications. Each section focuses on natural biopolymers in the form of scaffolds, membranes, films, gels and nanoparticles, thus helping the reader select not only the most appropriate polymer type, but also the most relevant structure. This comprehensive resource is ideal for materials scientists, biomedical engineers, tissue engineers, pharmaceutical scientists and anyone interested in developing novel materials for biomedical 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.

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.

From basic eye care services to visual performance training, this evidence-based resource explores a range of sports vision services, including assessment and treatment procedures, outcome expectations, and applications to a variety of sports. Optometrists, ophthalmologists, and sports medicine practitioners will find a thorough review and discussion of the role of vision care in an athlete's performance, as well as practical recommendations for applying current research findings to clinical practice. Contains practical, clinically oriented chapters on visual assessment, prescribing, and ocular injuries in athletes. Takes a task analysis approach allowing the reader to develop solid reasoning skills and evaluate information needed for clinical practice. Includes a new chapter on Assessment and Management of Sports-Related Concussion. Features visual aids throughout including photographs, tables, and boxes to help clarify and visualize important concepts. Addresses sports vision training approaches and updated digital options reflecting the collaboration between athletic trainers, optometrists, and ophthalmologists in helping optimize vision in athletes.

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.

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.

The PCP's Bicentennial Edition Remington: The Science and Practice of Pharmacy, Twenty Third Edition, offers a trusted, completely updated source of information for education, training, and development of pharmacists. Published for the first time with Elsevier, this edition includes coverage of biologics and biosimilars as uses of those therapeutics have increased substantially since the previous edition. Also discussed are formulations, drug delivery (including prodrugs, salts, polymorphism. With clear, detailed color illustrations, fundamental information on a range of pharmaceutical science areas, and information on new developments in industry, pharmaceutical industry scientists, especially those involved in drug discovery and development will find this edition of Remington an essential reference. Intellectual property professionals will also find this reference helpful to cite in patents and resulting litigations. Additional graduate and postgraduate students in Pharmacy and Pharmaceutical Sciences will refer to this book in courses dealing with medicinal chemistry and pharmaceutics.

Rehabilitation helps individuals maintain and optimize independence. Historically, people with dementia have received little rehabilitation and the focus has been on care to replace lost function. Dementia Rehabilitation is a resource for health and social professionals, service planners, policy makers, and academics. The book makes a compelling case for rehabilitation for people with dementia, including the views of people with dementia and the research evidence. For each area of function, the research evidence and relevant theory is summarized, followed by practical information on clinical assessment, and delivery of therapies.

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.