Articles: disease.
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Sporadic outbreaks of epizootics including SARS coronavirus and H5N1 avian influenza remind us of the potential for communicable diseases to quickly spread into worldwide epidemics. To confront emerging viral threats, nations have implemented strategies to prepare for pandemics and to control virus spread. Despite improved surveillance and quarantine measures, we find ourselves in the midst of a H1N1 influenza pandemic. ⋯ The best route to effective therapeutics and vaccines is through a detailed and global view of virus-host interactions that can be achieved using a systems biology approach. Here, we provide our perspective on the role of systems biology in deepening our understanding of virus-host interactions and in improving drug and vaccine development. We offer examples from influenza virus research, as well as from research on other pandemics of our time - HIV/AIDS and HCV - to demonstrate that systems biology offers one possible key to stopping the cycle of viral pandemics.
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We estimated U.S. biomedical research funding across therapeutic areas, determined the association with disease burden, and evaluated new drug approvals that resulted from this investment. ⋯ Across therapeutic areas, biomedical research funding increased substantially, appears aligned with disease burden in high income countries, but is not linked to new drug approvals. The translational gap between funding and new therapies is affecting all of medicine, and remedies must include changes beyond additional financial investment.
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Multicenter Study
A prospective study of the impact of comorbid medical disease on bipolar disorder outcomes.
Several studies suggest that medical comorbidity is associated with worse clinical status in bipolar disorder. It is unclear which aspect of medical comorbidity is responsible: simple disease count, risk for future morbidity, or current physical burden. ⋯ This long-term prospective study extends cross-sectional and retrospective research on the link between medical illness and bipolar outcomes. It is the current experience of burden of physical illness, rather than an unweighted or weighted disease count, that leads to worse bipolar outcomes.
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Statistics in medicine · Apr 2009
Modeling disease-state transition heterogeneity through Bayesian variable selection.
In many diseases, Markov transition models are useful in describing transitions between discrete disease states. Often the probability of transitioning from one state to another varies widely across subjects. This heterogeneity is driven, in part, by a possibly unknown number of previous disease states and by potentially complex relationships between clinical data and these states. ⋯ Our approach simultaneously estimates the order of the Markov process and the transition-specific covariate effects. The methods are assessed using simulation studies and applied to model disease-state transition on the expanded disability status scale (EDSS) in multiple sclerosis (MS) patients from the Partners MS Center in Boston, MA. The proposed methodology is shown to accurately identify complex covariate-transition relationships in simulations and identifies a clinically significant interaction between relapse history and EDSS history in MS patients.