Trends of the Risk of Death due to Circulatory, Cerebrovascular, and Ischemic Heart Diseases in 11 Brazilian Capitals from 1980 to 1998

OBJECTIVE: To assess the trends of the risk of death due to circulatory (CD), cerebrovascular (CVD), and ischemic heart diseases (IHD) in 11 Brazilian capitals from 1980 to 1998. METHODS: Data on mortality due to CD, CVD and IHD were obtained from the Brazilian Health Ministry, and the population estimates were calculated by interpolation with the Lagrange method based on census data from 1980 and 1991 and the population count of 1996. The trends were analyzed with the multiple linear regression method. RESULTS: CD showed a trend towards a decrease in most capitals, except for Brasília, where a mild increase was observed. The cities of Porto Alegre, Curitiba, Rio de Janeiro, Cuiabá, Goiânia, Belém, and Manaus showed a decrease in the risk of death due to CVD and IHD, while the city of Brasília showed an increase in CVD and IHD. The city of São Paulo showed a mild increase in IHD for individuals of both sexes aged 30 to 39 years and for females aged 40 to 59 years. In the cities of Recife and Salvador, a reduction in CD was observed for all ages and both sexes. In the city of Recife, however, an increase in IHD was observed at younger ages (30 to 49 years), and this trend decreased until a mild reduction (-4%) was observed in males ³ 70 years. CONCLUSION: In general, a reduction in the risk of death due to CD and an increase in IHD were observed, mainly in the cities of Recife and Brasília.

Left Atrial Volume Determinants in Patients with Non-Ischemic Dilated Cardiomyopathy

Background:Left atrial volume (LAV) is a predictor of prognosis in patients with heart failure.Objective:We aimed to evaluate the determinants of LAV in patients with dilated cardiomyopathy (DCM).Methods:Ninety patients with DCM and left ventricular (LV) ejection fraction ≤ 0.50 were included. LAV was measured with real-time three-dimensional echocardiography (eco3D). The variables evaluated were heart rate, systolic blood pressure, LV end-diastolic volume and end-systolic volume and ejection fraction (eco3D), mitral inflow E wave, tissue Doppler e´ wave, E/e´ ratio, intraventricular dyssynchrony, 3D dyssynchrony index and mitral regurgitation vena contracta. Pearson´s coefficient was used to identify the correlation of the LAV with the assessed variables. A multiple linear regression model was developed that included LAV as the dependent variable and the variables correlated with it as the predictive variables.Results:Mean age was 52 ± 11 years-old, LV ejection fraction: 31.5 ± 8.0% (16-50%) and LAV: 39.2±15.7 ml/m2. The variables that correlated with the LAV were LV end-diastolic volume (r = 0.38; p < 0.01), LV end-systolic volume (r = 0.43; p < 0.001), LV ejection fraction (r = -0.36; p < 0.01), E wave (r = 0.50; p < 0.01), E/e´ ratio (r = 0.51; p < 0.01) and mitral regurgitation (r = 0.53; p < 0.01). A multivariate analysis identified the E/e´ ratio (p = 0.02) and mitral regurgitation (p = 0.02) as the only independent variables associated with LAV increase.Conclusion:The LAV is independently determined by LV filling pressures (E/e´ ratio) and mitral regurgitation in DCM.

Factors affecting phosphate fixation in soils of the state of São Paulo

A laboratory experiment was carried out to study the effects of chemical and physical characteristics of the soil on the phosphate fixing capacity. One hundred samples collected from various localities were at first characterized chemically and their particlesize distribution determined. They were then tested as to their phosphate fixing capacities. The results obtained were statistically analysed by means of both simple linear and multiple correlation. The following conclusions could be drawn: 1. simple linear regression analysis indicated that % C, exchangeable Al+3, CEC, % clay, pH and % sand were the soil characteristics which significantly affected phosphate fixing capacity of São Paulo State soils; 2. multiple linear regression analysis indicated that % C, exchangeable Mg(+2)9 exchangeable- Al+3 and % clay were the soil characteristics which significantly affected the phosphate fixing capacity of São Paulo State soils; 3. the phosphate phonomena fixing as they occur in the soils of the São Paulo State can be best described by the following equation: Y = -2,266 - 3,484 + 3,514 + 5,559 + 1,005 %C Mg+2 Al+3 % clay exchangeable exchangeable 4. phosphate fixation in the soil is affected by the combined effects of both soil chemical and physical characteristics.

Validity of serology for American Trypanosomiasis with eluates from filter paper

This study evaluates whether blood collected on filter paper kept at 4 degrees C and tested at different intervals of time (1, 7, 15, 30 and 60 days after collection) would present similar results when compared to the serum samples and whether the type of filter paper influences the results. Eluates from filter paper samples were tested for Trypanosoma cruzi antibodies using indirect immunofluorescence antibody test (IFAT), indirect haemagglutination (IHA) and enzyme-linked immunosorbent assay (ELISA) as reference, the antibody titer in sera. Analysis of data showed that results obtained with IFAT, IHA (cut off point = 1:40) and ELISA in sera had similar sensitivity and good concordance among reactions. The use of a multiple linear regression model indicated that titer fall in eluates occurs up to the 7th day after the collection, and it is more marked for samples with lower antibodies titers. However, no significant differences were observed by IFAT, IHA (cut off point = 1:20) and ELISA in the proportion of positive reactions between sera and eluates. The results also showed that Melitta, Klabin or Whatman (reference) filter papers could be indicated for surveys, since they have shown similar capacity of maintenance of anti-T. cruzi immunoglobulins.

A geoprocessing approach for studying and controlling schistosomiasis in the state of Minas Gerais, Brazil

Geographical information systems (GIS) are tools that have been recently tested for improving our understanding of the spatial distribution of disease. The objective of this paper was to further develop the GIS technology to model and control schistosomiasis using environmental, social, biological and remote-sensing variables. A final regression model (R² = 0.39) was established, after a variable selection phase, with a set of spatial variables including the presence or absence of Biomphalaria glabrata, winter enhanced vegetation index, summer minimum temperature and percentage of houses with water coming from a spring or well. A regional model was also developed by splitting the state of Minas Gerais (MG) into four regions and establishing a linear regression model for each of the four regions: 1 (R² = 0.97), 2 (R² = 0.60), 3 (R² = 0.63) and 4 (R² = 0.76). Based on these models, a schistosomiasis risk map was built for MG. In this paper, geostatistics was also used to make inferences about the presence of Biomphalaria spp. The result was a map of species and risk areas. The obtained risk map permits the association of uncertainties, which can be used to qualify the inferences and it can be thought of as an auxiliary tool for public health strategies.

The Estrada Real project and endemic diseases: the case of schistosomiasis, geoprocessing and tourism

Geographical Information System (GIS) is a tool that has recently been applied to better understand spatial disease distributions. Using meteorological, social, sanitation, mollusc distribution data and remote sensing variables, this study aimed to further develop the GIS technology by creating a model for the spatial distribution of schistosomiasis and to apply this model to an area with rural tourism in the Brazilian state of Minas Gerais (MG). The Estrada Real, covering about 1,400 km, is the largest and most important Brazilian tourism project, involving 163 cities in MG with different schistosomiasis prevalence rates. The model with three variables showed a R² = 0.34, with a standard deviation of risk estimated adequate for public health needs. The main variables selected for modelling were summer vegetation, summer minimal temperature and winter minimal temperature. The results confirmed the importance of Remote Sensing data and the valuable contribution of GIS in identifying priority areas for intervention in tourism regions which are endemic to schistosomiasis.

Seasonality in insect abundance in the "Cerrado" of Goiás State, Brazil

Seasonality in insect abundance in the "Cerrado" of Goiás State, Brazil. Many studies have provided evidence that tropical insects undergo seasonal changes in abundance and that this is partly due to alternation between the dry and rainy seasons. In the Brazilian "Cerrado" (savannah), this season alternation is particularly evident. The purpose of this work was to study the seasonal abundance of insects in a "Cerrado" area in the municipality of Pirenópolis, Goiás State, Brazil. The insects were captured fortnightly using a light trap between September 2005 and August 2006. The insects collected were separated at the order level and counted. Faunistic analysis was performed to select the predominant insect orders, a multiple linear regression to examine the relation between climatic variables (temperature and precipitation) with the abundance of insects and a circular distribution analysis to evaluate the existence of seasonality in the abundance of insect orders. A total of 34,741 insect specimens were captured, belonging to 19 orders. The orders with the greatest number of specimens were Hymenoptera (8,022), Coleoptera (6,680), Diptera (6,394), Lepidoptera (6,223), Isoptera (2,272), Hemiptera (2,240) and Trichoptera (1,967), which represent 97.3% of all the specimens collected. All the orders, except for Diptera, Isoptera and Trichoptera, showed a relationship with the climate variables (temperature), and all the orders, except for Diptera, presented a grouped distribution, with greater abundance in the transition from the end of the dry season (September) to the start of the rainy one (October/November). A discussion about seasonality on the abundance of the insects is presented.

Selecting statistical models to study the relationship between soybean yield and soil physical properties

Statistical models allow the representation of data sets and the estimation and/or prediction of the behavior of a given variable through its interaction with the other variables involved in a phenomenon. Among other different statistical models, are the autoregressive state-space models (ARSS) and the linear regression models (LR), which allow the quantification of the relationships among soil-plant-atmosphere system variables. To compare the quality of the ARSS and LR models for the modeling of the relationships between soybean yield and soil physical properties, Akaike's Information Criterion, which provides a coefficient for the selection of the best model, was used in this study. The data sets were sampled in a Rhodic Acrudox soil, along a spatial transect with 84 points spaced 3 m apart. At each sampling point, soybean samples were collected for yield quantification. At the same site, soil penetration resistance was also measured and soil samples were collected to measure soil bulk density in the 0-0.10 m and 0.10-0.20 m layers. Results showed autocorrelation and a cross correlation structure of soybean yield and soil penetration resistance data. Soil bulk density data, however, were only autocorrelated in the 0-0.10 m layer and not cross correlated with soybean yield. The results showed the higher efficiency of the autoregressive space-state models in relation to the equivalent simple and multiple linear regression models using Akaike's Information Criterion. The resulting values were comparatively lower than the values obtained by the regression models, for all combinations of explanatory variables.

Building predictive models of soil particle-size distribution

Is it possible to build predictive models (PMs) of soil particle-size distribution (psd) in a region with complex geology and a young and unstable land-surface? The main objective of this study was to answer this question. A set of 339 soil samples from a small slope catchment in Southern Brazil was used to build PMs of psd in the surface soil layer. Multiple linear regression models were constructed using terrain attributes (elevation, slope, catchment area, convergence index, and topographic wetness index). The PMs explained more than half of the data variance. This performance is similar to (or even better than) that of the conventional soil mapping approach. For some size fractions, the PM performance can reach 70 %. Largest uncertainties were observed in geologically more complex areas. Therefore, significant improvements in the predictions can only be achieved if accurate geological data is made available. Meanwhile, PMs built on terrain attributes are efficient in predicting the particle-size distribution (psd) of soils in regions of complex geology.

Evaluation of statistical and geostatistical models of digital soil properties mapping in tropical mountain regions

Soil properties have an enormous impact on economic and environmental aspects of agricultural production. Quantitative relationships between soil properties and the factors that influence their variability are the basis of digital soil mapping. The predictive models of soil properties evaluated in this work are statistical (multiple linear regression-MLR) and geostatistical (ordinary kriging and co-kriging). The study was conducted in the municipality of Bom Jardim, RJ, using a soil database with 208 sampling points. Predictive models were evaluated for sand, silt and clay fractions, pH in water and organic carbon at six depths according to the specifications of the consortium of digital soil mapping at the global level (GlobalSoilMap). Continuous covariates and categorical predictors were used and their contributions to the model assessed. Only the environmental covariates elevation, aspect, stream power index (SPI), soil wetness index (SWI), normalized difference vegetation index (NDVI), and b3/b2 band ratio were significantly correlated with soil properties. The predictive models had a mean coefficient of determination of 0.21. Best results were obtained with the geostatistical predictive models, where the highest coefficient of determination 0.43 was associated with sand properties between 60 to 100 cm deep. The use of a sparse data set of soil properties for digital mapping can explain only part of the spatial variation of these properties. The results may be related to the sampling density and the quantity and quality of the environmental covariates and predictive models used.

Amino acid composition of soybean seeds as affected by climatic variables

The objective of this work was to perform a quantitative analysis of the amino acid composition of soybean seeds as affected by climatic variables during seed filling. Amino acids were determined from seed samples taken at harvest in 31 multi-environment field trials carried out in Argentina. Total amino acids ranged from 31.69 to 49.14%, and total essential and nonessential amino acids varied from 12.83 to 19.02% and from 18.86 to 31.15%, respectively. Variance components expressed as the percentage of total variation showed that the environment was the most important source of variation for all traits, followed by the genotype x environment interaction. Significant explanatory linear regressions were detected for amino acid content regarding: average daily mean air temperature and cumulative solar radiation, during seed filling; precipitation minus potential evapotranspiration, during the whole reproductive period; and the combinations of these climatic variables. Each amino acid behaves differently according to environmental conditions, indicating compensatory effects among them.

Proposição, validação e análise dos modelos que correlacionam estrutura química e atividade biológica

The present paper aims to bring under discussion some theoretical and practical aspects about the proposition, validation and analysis of QSAR models based on multiple linear regression. A comprehensive approach for the derivation of extrathermodynamic equations is reviewed. Some examples of QSAR models published in the literature are analyzed and criticized.

Aplicação de algoritmos genéticos na seleção de variáveis em espectroscopia no infravermelho médio: determinação simultânea de glicose, maltose e frutose

Genetic algorithm was used for variable selection in simultaneous determination of mixtures of glucose, maltose and fructose by mid infrared spectroscopy. Different models, using partial least squares (PLS) and multiple linear regression (MLR) with and without data pre-processing, were used. Based on the results obtained, it was verified that a simpler model (multiple linear regression with variable selection by genetic algorithm) produces results comparable to more complex methods (partial least squares). The relative errors obtained for the best model was around 3% for the sugar determination, which is acceptable for this kind of determination.

Quantitative structure-retention relationships analysis of retention index of essential oils

Genetic algorithm and multiple linear regression (GA-MLR), partial least square (GA-PLS), kernel PLS (GA-KPLS) and Levenberg-Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlation between retention index (RI) and descriptors for 116 diverse compounds in essential oils of six Stachys species. The correlation coefficient LGO-CV (Q²) between experimental and predicted RI for test set by GA-MLR, GA-PLS, GA-KPLS and L-M ANN was 0.886, 0.912, 0.937 and 0.964, respectively. This is the first research on the QSRR of the essential oil compounds against the RI using the GA-KPLS and L-M ANN.

Problems and potentialities of e-Learning for regular undergraduate courses in emergency medicine

BACKGROUND: E-learning techniques are spreading at great speed in medicine, raising concerns about the impact of adopting them. Websites especially designed to host courses are becoming more common. There is a lack of evidence that these systems could enhance student knowledge acquisition. GOAL: To evaluate the impact of using dedicated-website tools over cognition of medical students exposed to a first-aid course. METHODS: Prospective study of 184 medical students exposed to a twenty-hour first-aid course. We generated a dedicated-website with several sections (lectures, additional reading material, video and multiple choice exercises). We constructed variables expressing the student's access to each section. The evaluation was composed of fifty multiple-choice tests, based on clinical problems. We used multiple linear regression to adjust for potential confounders. RESULTS: There was no association of website intensity of exposure and the outcome - beta-coeficient 0.27 (95%CI - 0.454 - 1.004). These findings were not altered after adjustment for potential confounders - 0.165 (95%CI -0.628 - 0.960). CONCLUSION: A dedicated website with passive and active capabilities for aiding in person learning had not shown association with a better outcome.