Equidade na utilização dos serviços de saúde no Brasil: um estudo comparativo entre as regiões brasileiras no período 1998-2008

Brazil presents severe socioeconomic inequalities among regions and individuals. Several studies analyze the determinants of these inequalities and its effects on social welfare indicators, such as health. This paper measures the socioeconomic inequalities in healthcare utilization in Brazil and in Brazilian regions over the period 1998-2008, using the Brazilian household survey, Pesquisa Nacional por Amostra de Domicílios (PNAD). Health concentration curves and indexes – CC and CI – were estimated. This methodology takes into account differences throughout the income distribution. The results show a consistent improvement during the period. These improvements were largest among individuals without health insurance, suggesting an improvement at Brazilian Health System (SUS) services. The estimation of CC and IC suggests a small magnitude of inequality in outpatient and hospital services. The dental service is the only one, among the healthcare utilization variables, with relevant magnitude of inequality favoring of the richest groups. The analysis of healthcare access suggests the presence of constrained demand more concentrated among the poorest groups, especially for the population without health insurance. This study moves forward in the health equity literature since it analyzes equity at SUS in the last ten years considering differences among socioeconomic groups and Brazilian regions.Healthcare inequalities. Brazilian Health System. Brazilian regions.

Compatibility studies of Olanzapine pre-formulated with excipients by thermal analysis: preliminary study

Thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) were used to investigate drug-excipient interactions and, in consequence, their compatibility. For this purpose, binary mixtures of olanzapine drug substance and the excipients croscarmellose sodium, magnesium stearate and microcrystalline cellulose, were prepared and analysed. By the analysis of the binary mixtures DSC and TG curves it were observed changes on the temperature and enthalpy values of the drug melting and decomposition peak, with the likely formation of intermediate substances.Colegio de Farmacéuticos de la Provincia de Buenos Aire

(-)-tarchonanthuslactone Exerts A Blood Glucose-increasing Effect In Experimental Type 2 Diabetes Mellitus

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)A number of studies have proposed an anti-diabetic effect for tarchonanthuslactone based on its structural similarity with caffeic acid, a compound known for its blood glucose-reducing properties. However, the actual effect of tarchonanthuslactone on blood glucose level has never been tested. Here, we report that, in opposition to the common sense, tarchonanthuslactone has a glucose-increasing effect in a mouse model of obesity and type 2 diabetes mellitus. The effect is acute and non-cumulative and is present only in diabetic mice. In lean, glucose-tolerant mice, despite a slight increase in blood glucose levels, the effect was not significant.20350385049Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Institute of Chemistry/UNICAMPFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)FAPESP [proc. 13/07607-8]FAPESP [11/50514-5, 12/09254-2, 10/08673-6, 2009/53606-8

Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images

Deep learning has been successfully applied to several problems related to autonomous driving. Often, these solutions rely on large networks that require databases of real image samples of the problem (i.e., real world) for proper training. The acquisition of such real-world data sets is not always possible in the autonomous driving context, and sometimes their annotation is not feasible (e.g., takes too long or is too expensive). Moreover, in many tasks, there is an intrinsic data imbalance that most learning-based methods struggle to cope with. It turns out that traffic sign detection is a problem in which these three issues are seen altogether. In this work, we propose a novel database generation method that requires only (i) arbitrary natural images, i.e., requires no real image from the domain of interest, and (ii) templates of the traffic signs, i.e., templates synthetically created to illustrate the appearance of the category of a traffic sign. The effortlessly generated training database is shown to be effective for the training of a deep detector (such as Faster R-CNN) on German traffic signs, achieving 95.66% of mAP on average. In addition, the proposed method is able to detect traffic signs with an average precision, recall and F1-score of about 94%, 91% and 93%, respectively. The experiments surprisingly show that detectors can be trained with simple data generation methods and without problem domain data for the background, which is in the opposite direction of the common sense for deep learning