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A select group of highly renowned scientists - among them four Nobel Prize Winners - have been asked to summarize significant developments of their ownrecent research in the life sciences at a workshop organized on the occasionof the opening of the new Paul-Ehrlich-Institut in Langen near Frankfurt/ Main. They do this in a comparative fashion evaluating similar achievements in adjacent fields. Their intellectual state-of-the-art analysis and fascinating outlook on future perspectives provides exciting and stimulating reading. The authors address areas in virology, immunology, oncology and evolution. Intelligent design of vaccines and other immunologial drugs, virus evolution and viruses as nature's engineers, pathology of chronic autoimmune and central nervous system diseases and the biology of mammary cancer belong to the topics discussed. A book easy to read for scientists, doctors and students interested in rapidly developing fields in the life sciences.
On February 26, 2020, the Board on Health Sciences Policy of the National Academies of Sciences, Engineering, and Medicine hosted a 1-day public workshop in Washington, DC, to examine current and emerging bioethical issues that might arise in the context of biomedical research and to consider research topics in bioethics that could benefit from further attention. The scope of bioethical issues in research is broad, but this workshop focused on issues related to the development and use of digital technologies, artificial intelligence, and machine learning in research and clinical practice; issues emerging as nontraditional approaches to health research become more widespread; the role of bioethics in addressing racial and structural inequalities in health; and enhancing the capacity and diversity of the bioethics workforce. This publication summarizes the presentations and discussions from the workshop.
This report is the twelfth assessment of the National Institutes of Health National Research Service Awards program. The research training needs of the country in basic biomedical, clinical, and behavioral and social sciences are considered. Also included are the training needs of oral health, nursing, and health services research. The report has been broadly constructed to take into account the rapidly evolving national and international health care needs. The past and present are analyzed, and predictions with regard to future needs are presented.
The nature of biomedical research has been evolving in recent years. Technological advances that make it easier to study the vast complexity of biological systems have led to the initiation of projects with a larger scale and scope. In many cases, these large-scale analyses may be the most efficient and effective way to extract functional information from complex biological systems. Large-Scale Biomedical Science: Exploring Strategies for Research looks at the role of these new large-scale projects in the biomedical sciences. Though written by the National Academies' Cancer Policy Board, this book addresses implications of large-scale science extending far beyond cancer research. It also identifies obstacles to the implementation of these projects, and makes recommendations to improve the process. The ultimate goal of biomedical research is to advance knowledge and provide useful innovations to society. Determining the best and most efficient method for accomplishing that goal, however, is a continuing and evolving challenge. The recommendations presented in Large-Scale Biomedical Science are intended to facilitate a more open, inclusive, and accountable approach to large-scale biomedical research, which in turn will maximize progress in understanding and controlling human disease.
In the realm of health care, privacy protections are needed to preserve patients' dignity and prevent possible harms. Ten years ago, to address these concerns as well as set guidelines for ethical health research, Congress called for a set of federal standards now known as the HIPAA Privacy Rule. In its 2009 report, Beyond the HIPAA Privacy Rule: Enhancing Privacy, Improving Health Through Research, the Institute of Medicine's Committee on Health Research and the Privacy of Health Information concludes that the HIPAA Privacy Rule does not protect privacy as well as it should, and that it impedes important health research.
Drawing on the work of the Roundtable on Evidence-Based Medicine, the 2007 IOM Annual Meeting assessed some of the rapidly occurring changes in health care related to new diagnostic and treatment tools, emerging genetic insights, the developments in information technology, and healthcare costs, and discussed the need for a stronger focus on evidence to ensure that the promise of scientific discovery and technological innovation is efficiently captured to provide the right care for the right patient at the right time. As new discoveries continue to expand the universe of medical interventions, treatments, and methods of care, the need for a more systematic approach to evidence development and application becomes increasingly critical. Without better information about the effectiveness of different treatment options, the resulting uncertainty can lead to the delivery of services that may be unnecessary, unproven, or even harmful. Improving the evidence-base for medicine holds great potential to increase the quality and efficiency of medical care. The Annual Meeting, held on October 8, 2007, brought together many of the nation's leading authorities on various aspects of the issues - both challenges and opportunities - to present their perspectives and engage in discussion with the IOM membership.
This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.
Collaborations of physicians and researchers with industry can provide valuable benefits to society, particularly in the translation of basic scientific discoveries to new therapies and products. Recent reports and news stories have, however, documented disturbing examples of relationships and practices that put at risk the integrity of medical research, the objectivity of professional education, the quality of patient care, the soundness of clinical practice guidelines, and the public's trust in medicine. Conflict of Interest in Medical Research, Education, and Practice provides a comprehensive look at conflict of interest in medicine. It offers principles to inform the design of policies to identify, limit, and manage conflicts of interest without damaging constructive collaboration with industry. It calls for both short-term actions and long-term commitments by institutions and individuals, including leaders of academic medical centers, professional societies, patient advocacy groups, government agencies, and drug, device, and pharmaceutical companies. Failure of the medical community to take convincing action on conflicts of interest invites additional legislative or regulatory measures that may be overly broad or unduly burdensome. Conflict of Interest in Medical Research, Education, and Practice makes several recommendations for strengthening conflict of interest policies and curbing relationships that create risks with little benefit. The book will serve as an invaluable resource for individuals and organizations committed to high ethical standards in all realms of medicine.
Significant changes have taken place in the policy landscape surrounding cannabis legalization, production, and use. During the past 20 years, 25 states and the District of Columbia have legalized cannabis and/or cannabidiol (a component of cannabis) for medical conditions or retail sales at the state level and 4 states have legalized both the medical and recreational use of cannabis. These landmark changes in policy have impacted cannabis use patterns and perceived levels of risk. However, despite this changing landscape, evidence regarding the short- and long-term health effects of cannabis use remains elusive. While a myriad of studies have examined cannabis use in all its various forms, often these research conclusions are not appropriately synthesized, translated for, or communicated to policy makers, health care providers, state health officials, or other stakeholders who have been charged with influencing and enacting policies, procedures, and laws related to cannabis use. Unlike other controlled substances such as alcohol or tobacco, no accepted standards for safe use or appropriate dose are available to help guide individuals as they make choices regarding the issues of if, when, where, and how to use cannabis safely and, in regard to therapeutic uses, effectively. Shifting public sentiment, conflicting and impeded scientific research, and legislative battles have fueled the debate about what, if any, harms or benefits can be attributed to the use of cannabis or its derivatives, and this lack of aggregated knowledge has broad public health implications. The Health Effects of Cannabis and Cannabinoids provides a comprehensive review of scientific evidence related to the health effects and potential therapeutic benefits of cannabis. This report provides a research agendaâ€"outlining gaps in current knowledge and opportunities for providing additional insight into these issuesâ€"that summarizes and prioritizes pressing research needs.
Continuing advances in biomedical research and statistical methods call for a constant stream of updated, cohesive accounts of new developments so that the methodologies can be properly implemented in the biomedical field. Responding to this need, Computational Methods in Biomedical Research explores important current and emerging computational statistical methods that are used in biomedical research. Written by active researchers in the field, this authoritative collection covers a wide range of topics. It introduces each topic at a basic level, before moving on to more advanced discussions of applications. The book begins with microarray data analysis, machine learning techniques, and mass spectrometry-based protein profiling. It then uses state space models to predict US cancer mortality rates and provides an overview of the application of multistate models in analyzing multiple failure times. The book also describes various Bayesian techniques, the sequential monitoring of randomization tests, mixed-effects models, and the classification rules for repeated measures data. The volume concludes with estimation methods for analyzing longitudinal data. Supplying the knowledge necessary to perform sophisticated statistical analyses, this reference is a must-have for anyone involved in advanced biomedical and pharmaceutical research. It will help in the quest to identify potential new drugs for the treatment of a variety of diseases.