Gross proteinuria is a strong risk predictor for cardiovascular mortality in Brazilian type 2 diabetic patients

Increased proteinuria is recognized as a risk predictor for all-cause and cardiovascular mortality in diabetic patients; however, no study has evaluated these relationships in Brazilian patients. The aim of this study was to investigate the prognostic value of gross proteinuria for all-cause and cardiovascular mortalities and for cardiovascular morbidity in a cohort study of 471 type 2 diabetic individuals followed for up to 7 years. Several clinical, laboratory and electrocardiographic variables were obtained at baseline. The relative risks for all-cause, cardiovascular and cardiac mortalities and for cardiovascular and cardiac events associated with the presence of overt proteinuria (>0.5 g/24 h) were assessed by Kaplan-Meier survival curves and by multivariate Cox regression model. During a median follow-up of 57 months (range 2-84 months), 121 patients (25.7%) died, 44 from cardiovascular and 30 from cardiac causes, and 106 fatal or non-fatal cardiovascular events occurred. Gross proteinuria was an independent risk predictor of all-cause, cardiovascular and cardiac mortalities and of cardiovascular morbidity with adjusted relative risks ranging from 1.96 to 4.38 for the different endpoints. This increased risk remained significant after exclusion of patients with prior cardiovascular disease at baseline from the multivariate analysis. In conclusion, gross proteinuria was a strong predictor of all-cause, cardiovascular and cardiac mortalities and also of cardiovascular morbidity in a Brazilian cohort of type 2 diabetic patients. Intervention studies are necessary to determine whether the reduction of proteinuria can decrease morbidity and mortality of type 2 diabetes in Brazil.

REGγ is a strong candidate for the regulation of cell cycle, proliferation and the invasion by poorly differentiated thyroid carcinoma cells

REGγ is a proteasome activator that facilitates the degradation of small peptides. Abnormally high expression of REGγ has been observed in thyroid carcinomas. The purpose of the present study was to explore the role of REGγ in poorly differentiated thyroid carcinoma (PDTC). For this purpose, small interfering RNA (siRNA) was introduced to down-regulate the level of REGγ in the PDTC cell line SW579. Down-regulation of REGγ at the mRNA and protein levels was confirmed by RT-PCR and Western blot analyses. FACS analysis revealed cell cycle arrest at the G1/S transition, the MTT assay showed inhibition of cell proliferation, and the Transwell assay showed restricted cell invasion. Furthermore, the expression of the p21 protein was increased, the expression of proliferating cell nuclear antigen (PCNA) protein decreased, and the expression of the p27 protein was unchanged as shown by Western blot analyses. REGγ plays a critical role in the cell cycle, proliferation and invasion of SW579 cells. The alteration of p21 and PCNA proteins related to the down-regulation of REGγ suggests that p21 and PCNA participate in the process of REGγ regulation of cell cycle progression and cell proliferation. Thus, targeting REGγ has a therapeutic potential in the management of PDTC patients.

Estimating the average length of hospitalization due to pneumonia: a fuzzy approach

Exposure to air pollutants is associated with hospitalizations due to pneumonia in children. We hypothesized the length of hospitalization due to pneumonia may be dependent on air pollutant concentrations. Therefore, we built a computational model using fuzzy logic tools to predict the mean time of hospitalization due to pneumonia in children living in São José dos Campos, SP, Brazil. The model was built with four inputs related to pollutant concentrations and effective temperature, and the output was related to the mean length of hospitalization. Each input had two membership functions and the output had four membership functions, generating 16 rules. The model was validated against real data, and a receiver operating characteristic (ROC) curve was constructed to evaluate model performance. The values predicted by the model were significantly correlated with real data. Sulfur dioxide and particulate matter significantly predicted the mean length of hospitalization in lags 0, 1, and 2. This model can contribute to the care provided to children with pneumonia.

Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network

In this study, a neuro-fuzzy estimator was developed for the estimation of biomass concentration of the microalgae Synechococcus nidulans from initial batch concentrations, aiming to predict daily productivity. Nine replica experiments were performed. The growth was monitored daily through the culture medium optic density and kept constant up to the end of the exponential phase. The network training followed a full 3³ factorial design, in which the factors were the number of days in the entry vector (3,5 and 7 days), number of clusters (10, 30 and 50 clusters) and internal weight softening parameter (Sigma) (0.30, 0.45 and 0.60). These factors were confronted with the sum of the quadratic error in the validations. The validations had 24 (A) and 18 (B) days of culture growth. The validations demonstrated that in long-term experiments (Validation A) the use of a few clusters and high Sigma is necessary. However, in short-term experiments (Validation B), Sigma did not influence the result. The optimum point occurred within 3 days in the entry vector, 10 clusters and 0.60 Sigma and the mean determination coefficient was 0.95. The neuro-fuzzy estimator proved a credible alternative to predict the microalgae growth.