Evaluation of metabolic syndrome in adults of Talca city, Chile

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Adiposity and Pathogen Exposure Predict C-Reactive Protein in Filipino Women

Obesity and infectious agents are both sources of inflammatory stimuli that result in increased production of C-reactive protein (CRP). Rates of overweight and obesity are increasing globally, but for many populations, gains in body fat are set against a backdrop of high levels of pathogen exposure. Our primary objective was to evaluate the extent to which adiposity and pathogenicity contribute to a double burden of inflammation in a population currently undergoing the nutrition transition. Measures of adiposity, pathogen exposure, and infectious disease symptoms were evaluated as predictors of high-sensitivity CRP concentration in plasma samples from 1875 women participating in the Cebu Longitudinal Health and Nutrition Survey in the Philippines. Proxy measures of pathogen exposure included household crowding and cleanliness, quality of water source, mode of waste disposal, and fecal exposure. A series of maximum likelihood logistic regression models were used to predict a plasma CRP concentration > 3 mg/L. Waist circumference was the strongest anthropometric predictor of elevated CRP [odds ratio (OR) = 2.29; 95% CI = 2.00, 2.62; P < 0.001]. Presence of infectious disease symptoms (OR = 2.51; 95% CI = 1.84, 3.44; P < 0.001) and level of pathogen exposure (OR = 1.56; 95% CI = 1.15, 2.12; P < 0.01) were also associated with elevated CRP. These associations were independent of socioeconomic status and other health behaviors. Overweight/obesity and infectious exposures are associated with elevated CRP in the Philippines; it is likely that other populations undergoing the nutrition transition are experiencing comparable double burdens of inflammatory stimuli. These results underscore the need for additional research on the contributions of pathogenicity, adiposity, and inflammation to global epidemics of cardiovascular and metabolic diseases

Comprehensive Testing Guidelines to Increase Efficiency in INDOT Operations

When INDOT designs a pavement project, the decision for QC/QA or non-QC/QA is made solely based on the quantity of pavement materials. However, the actual risk will vary depending on the severity of road conditions. The question is how to differentiate the quality testing efforts according to the severity of road conditions in order to balance required testing resources. We found that the number of commercial vehicles (CV) and heat index (HI: number of hot days/freezing index) can be used as criteria in classifying the road conditions. Using these two criteria, CV and HI, we classify the road sections into four classes and provide different testing guidelines for different classes

TRANSFORM METHODS FOR AN ECONOMIC ANALYSIS OF PROBABILISTIC CASH FLOWS.

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