3 resultados para Regression equation
em Biblioteca Digital da Produção Intelectual da Universidade de São Paulo
Resumo:
This research was conducted with objective to evaluate the effect of different zinc (Zn) sources and doses in the diet for Santa Ines sheep. Forty lambs at weaning, with 18.4 kg of body weight were supplemented with three different sources of zinc (zinc oxide (ZnO), zinc amino acid and zinc proteinate) and three doses of zinc (200, 400 and 600 mg/kg DM) added to the basal diet. At every 28 days, animals were weighted and blood samples were collected for analyses of zinc (Zn), alkaline phosphatase and immunoglobulin G (IgG) and M (IgM). At the end of experiment, liver samples were collected for determination of the hepatic zinc levels. Zinc was analyzed with atomic absorption spectrophotometer, while phosphatase alkaline and immunoglobulins G and M were analyzed using Laborlab and Bioclin kits, respectively. There was no effect of diets on phosphatase alkaline levels and hepatic zinc, but there was difference in the plasmatic zinc levels and IgG and IgM levels. Based on the accumulation of hepatic zinc, the estimate of the zinc bioavailability, through the regression equation, showed that supplementation with organic and inorganic sources of zinc did not differ in the diet of Santa Ines sheep.
Resumo:
Background: The double burden of obesity and underweight is increasing in developing countries and simple methods for the assessment of fat mass in children are needed. Aim: To develop and validate a new anthropometric predication equation for assessment of fat mass in children. Subjects and methods: Body composition was assessed in 145 children aged 9.8 +/- 1.3 (SD) years from Sao Paulo, Brazil using dual energy X-ray absorptiometry (DEXA) and skinfold measurements. The study sample was divided into development and validation sub-sets to develop a new prediction equation for FM (PE). Results: Using multiple linear regression analyses, the best equation for predicting FM (R-2 - 0.77) included body weight, triceps skinfold, height, gender and age as independent variables. When cross-validated, the new PE was valid in this sample (R-2 = 0.80), while previously published equations were not. Conclusion: The PE was more valid for Brazilian children that existing equations, but further studies are needed to assess the validity of this PE in other populations.
Resumo:
Objectives: To integrate data from two-dimensional echocardiography (2D ECHO), three-dimensional echocardiography (3D ECHO), and tissue Doppler imaging (TDI) for prediction of left ventricular (LV) reverse remodeling (LVRR) after cardiac resynchronization therapy (CRT). It was also compared the evaluation of cardiac dyssynchrony by TDI and 3D ECHO. Methods: Twenty-four consecutive patients with heart failure, sinus rhythm, QRS = 120 msec, functional class III or IV and LV ejection fraction (LVEF) = 0.35 underwent CRT. 2D ECHO, 3D ECHO with systolic dyssynchrony index (SDI) analysis, and TDI were performed before, 3 and 6 months after CRT. Cardiac dyssynchrony analyses by TDI and SDI were compared with the Pearson's correlation test. Before CRT, a univariate analysis of baseline characteristics was performed for the construction of a logistic regression model to identify the best predictors of LVRR. Results: After 3 months of CRT, there was a moderate correlation between TDI and SDI (r = 0.52). At other time points, there was no strong correlation. Nine of twenty-four (38%) patients presented with LVRR 6 months after CRT. After logistic regression analysis, SDI (SDI > 11%) was the only independent factor in the prediction of LVRR 6 months of CRT (sensitivity = 0.89 and specificity = 0.73). After construction of receiver operator characteristic (ROC) curves, an equation was established to predict LVRR: LVRR =-0.4LVDD (mm) + 0.5LVEF (%) + 1.1SDI (%), with responders presenting values >0 (sensitivity = 0.67 and specificity = 0.87). Conclusions: In this study, there was no strong correlation between TDI and SDI. An equation is proposed for the prediction of LVRR after CRT. Although larger trials are needed to validate these findings, this equation may be useful to candidates for CRT. (Echocardiography 2012;29:678-687)