Ecological study of socio-economic indicators and prevalence of asthma in schoolchildren in urban Brazil

BACKGROUND: There is evidence of higher prevalence of asthma in populations of lower socio-economic status in affluent societies, and the prevalence of asthma is also very high in some Latin American countries, where societies are characterized by a marked inequality in wealth. This study aimed to examine the relationship between estimates of asthma prevalence based on surveys conducted in children in Brazilian cities and health and socioeconomic indicators measured at the population level in the same cities. METHODS: We searched the literature in the medical databases and in the annals of scientific meeting, retrieving population-based surveys of asthma that were conducted in Brazil using the methodology defined by the International Study of Asthma and Allergies in Childhood. We performed separate analyses for the age groups 6-7 years and 13-14 years. We examined the association between asthma prevalence rates and eleven health and socio-economic indicators by visual inspection and using linear regression models weighed by the inverse of the variance of each survey. RESULTS: Six health and socioeconomic variables showed a clear pattern of association with asthma. The prevalence of asthma increased with poorer sanitation and with higher infant mortality at birth and at survey year, GINI index and external mortality. In contrast, asthma prevalence decreased with higher illiteracy rates. CONCLUSION: The prevalence of asthma in urban areas of Brazil, a middle income country, appears to be higher in cities with more marked poverty or inequality

4D cardiovascular magnetic resonance velocity mapping of alterations of right heart flow patterns and main pulmonary artery hemodynamics in tetralogy of Fallot

<p>Abstract</p> <p>Background</p> <p>To assess changes in right heart flow and pulmonary artery hemodynamics in patients with repaired Tetralogy of Fallot (rTOF) we used whole heart, four dimensional (4D) velocity mapping (VM) cardiovascular magnetic resonance (CMR).</p> <p>Methods</p> <p>CMR studies were performed in 11 subjects with rTOF (5M/6F; 20.1 ± 12.4 years) and 10 normal volunteers (6M/4F; 34.2 ± 13.4 years) on clinical 1.5T and 3.0T MR scanners. 4D VM-CMR was performed using PC VIPR (Phase Contrast Vastly undersampled Isotropic Projection Reconstruction). Interactive streamline and particle trace visualizations of the superior and inferior vena cava (IVC and SVC, respectively), right atrium (RA), right ventricle (RV), and pulmonary artery (PA) were generated and reviewed by three experienced readers. Main PA net flow, retrograde flow, peak flow, time-to-peak flow, peak acceleration, resistance index and mean wall shear stress were quantified. Differences in flow patterns between the two groups were tested using Fisher's exact test. Differences in quantitative parameters were analyzed with the Kruskal-Wallis rank sum test.</p> <p>Results</p> <p>4D VM-CMR was successfully performed in all volunteers and subjects with TOF. Right heart flow patterns in rTOF subjects were characterized by (a) greater SVC/IVC flow during diastole than systole, (b) increased vortical flow patterns in the RA and in the RV during diastole, and (c) increased helical or vortical flow features in the PA's. Differences in main PA retrograde flow, resistance index, peak flow, time-to-peak flow, peak acceleration and mean wall shear stress were statistically significant.</p> <p>Conclusions</p> <p>Whole heart 4D VM-CMR with PC VIPR enables detection of both normal and abnormal right heart flow patterns, which may allow for comprehensive studies to evaluate interdependencies of post-surgically altered geometries and hemodynamics.</p

Computational Comparative Study of Tuberculosis Proteomes Using a Model Learned from Signal Peptide Structures

Secretome analysis is important in pathogen studies. A fundamental and convenient way to identify secreted proteins is to first predict signal peptides, which are essential for protein secretion. However, signal peptides are highly complex functional sequences that are easily confused with transmembrane domains. Such confusion would obviously affect the discovery of secreted proteins. Transmembrane proteins are important drug targets, but very few transmembrane protein structures have been determined experimentally; hence, prediction of the structures is essential. In the field of structure prediction, researchers do not make assumptions about organisms, so there is a need for a general signal peptide predictor