7 research outputs found

    Developing capacity in health informatics in a resource poor setting: lessons from Peru

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    The public sectors of developing countries require strengthened capacity in health informatics. In Peru, where formal university graduate degrees in biomedical and health informatics were lacking until recently, the AMAUTA Global Informatics Research and Training Program has provided research and training for health professionals in the region since 1999. The Fogarty International Center supports the program as a collaborative partnership between Universidad Peruana Cayetano Heredia in Peru and the University of Washington in the United States of America. The program aims to train core professionals in health informatics and to strengthen the health information resource capabilities and accessibility in Peru. The program has achieved considerable success in the development and institutionalization of informatics research and training programs in Peru. Projects supported by this program are leading to the development of sustainable training opportunities for informatics and eight of ten Peruvian fellows trained at the University of Washington are now developing informatics programs and an information infrastructure in Peru. In 2007, Universidad Peruana Cayetano Heredia started offering the first graduate diploma program in biomedical informatics in Peru

    CIBERER : Spanish national network for research on rare diseases: A highly productive collaborative initiative

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    Altres ajuts: Instituto de Salud Carlos III (ISCIII); Ministerio de Ciencia e Innovación.CIBER (Center for Biomedical Network Research; Centro de Investigación Biomédica En Red) is a public national consortium created in 2006 under the umbrella of the Spanish National Institute of Health Carlos III (ISCIII). This innovative research structure comprises 11 different specific areas dedicated to the main public health priorities in the National Health System. CIBERER, the thematic area of CIBER focused on rare diseases (RDs) currently consists of 75 research groups belonging to universities, research centers, and hospitals of the entire country. CIBERER's mission is to be a center prioritizing and favoring collaboration and cooperation between biomedical and clinical research groups, with special emphasis on the aspects of genetic, molecular, biochemical, and cellular research of RDs. This research is the basis for providing new tools for the diagnosis and therapy of low-prevalence diseases, in line with the International Rare Diseases Research Consortium (IRDiRC) objectives, thus favoring translational research between the scientific environment of the laboratory and the clinical setting of health centers. In this article, we intend to review CIBERER's 15-year journey and summarize the main results obtained in terms of internationalization, scientific production, contributions toward the discovery of new therapies and novel genes associated to diseases, cooperation with patients' associations and many other topics related to RD research

    Understanding the immune response in tuberculosis using different mathematical models and biological scales

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    Abstract. The use of different mathematical tools to study biological processes is necessary to capture effects occurring at different scales. Here we study as an example the immune response to infection with the bacteria Mycobacterium tuberculosis, the causative agent of tuberculosis (TB). Immune responses are both global (lymph nodes, blood, and spleen) as well as local (site of infection) in nature. Interestingly, the immune response in TB at the site of infection results in the formation of spherical structures comprised of cells, bacteria, and effector molecules known as granulomas. In this work, we use four different mathematical tools to explore both the global immune response as well as the more local one (granuloma formation) and compare and contrast results obtained using these methods. Applying a range of approaches from continuous deterministic models to discrete stochastic ones allows us to make predictions and suggest hypotheses about the underlying biology that might otherwise go unnoticed. The tools developed and applied here are also applicable in other settings such as tumor modeling
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