Personalized Metabolic Avatar: A Data Driven Model of Metabolism for Weight Variation Forecasting and Diet Plan Evaluation.

Development of predictive computational models of metabolism through mechanistic models is complex and resource demanding, and their personalization remains challenging. Data-driven models of human metabolism would constitute a reliable, fast, and continuously updating model for predictive analytics. Wearable devices, such as smart bands and impedance balances, allow the real time and remote monitoring of physiological parameters, providing for a flux of data carrying information on user metabolism.

Personalized Self-Monitoring of Energy Balance through Integration in a Web-Application of Dietary, Anthropometric, and Physical Activity Data.

Self-monitoring of weight, diet and physical activity is a valuable component of behavioral weight loss treatment. The validation and user-friendliness of this approach is not optimal since users are selected from homogeneous pools and rely on different applications, increasing the burden and achieving partial, generic and/or unrelated information about their metabolic state. Moreover, studies establishing type, time, duration, and adherence criteria for self-monitoring are lacking.

Evaluation of the World Health Organization 2009 classification of dengue severity in autopsied individuals, during the epidemics of 2011 and 2012 in Brazil.

Introduction: The dengue classification proposed by the World Health Organization (WHO) in 2009 is considered more sensitive than the classification proposed by the WHO in 1997. However, no study has assessed the ability of the WHO 2009 classification to identify dengue deaths among autopsied individuals suspected of having dengue. In the present study, we evaluated the ability of the WHO 2009 classification to identify dengue deaths among autopsied individuals suspected of having dengue in Northeast Brazil, where the disease is endemic.