Classificação de atividade eletromiográfia facial de indivíduos saudáveis e com hanseníese por meio de máquina de vetores de suporte

A number of studies in the areas of Biomedical Engineering and Health Sciences have employed machine learning tools to develop methods capable of identifying patterns in different sets of data. Despite its extinction in many countries of the developed world, Hansen’s disease is still a disease that affects a huge part of the population in countries such as India and Brazil. In this context, this research proposes to develop a method that makes it possible to understand in the future how Hansen’s disease affects facial muscles. By using surface electromyography, a system was adapted so as to capture the signals from the largest possible number of facial muscles. We have first looked upon the literature to learn about the way researchers around the globe have been working with diseases that affect the peripheral neural system and how electromyography has acted to contribute to the understanding of these diseases. From these data, a protocol was proposed to collect facial surface electromyographic (sEMG) signals so that these signals presented a high signal to noise ratio. After collecting the signals, we looked for a method that would enable the visualization of this information in a way to make it possible to guarantee that the method used presented satisfactory results. After identifying the method's efficiency, we tried to understand which information could be extracted from the electromyographic signal representing the collected data. Once studies demonstrating which information could contribute to a better understanding of this pathology were not to be found in literature, parameters of amplitude, frequency and entropy were extracted from the signal and a feature selection was made in order to look for the features that better distinguish a healthy individual from a pathological one. After, we tried to identify the classifier that best discriminates distinct individuals from different groups, and also the set of parameters of this classifier that would bring the best outcome. It was identified that the protocol proposed in this study and the adaptation with disposable electrodes available in market proved their effectiveness and capability of being used in different studies whose intention is to collect data from facial electromyography. The feature selection algorithm also showed that not all of the features extracted from the signal are significant for data classification, with some more relevant than others. The classifier Support Vector Machine (SVM) proved itself efficient when the adequate Kernel function was used with the muscle from which information was to be extracted. Each investigated muscle presented different results when the classifier used linear, radial and polynomial kernel functions. Even though we have focused on Hansen’s disease, the method applied here can be used to study facial electromyography in other pathologies.

Atividade antiangiogênica da forma recombinante da proteína 21 de Trypanosoma cruzi

Chagas disease, caused by the parasite Trypanosoma cruzi, is the cause of Chronic chagasic cardiomyopathy (CCC). The prospection of innovative therapeutic agents against CCC is a major task. The recombinant form of 21 (rP21), a secreted T. cruzi protein involved in host cell invasion and on progression of chronic inflammatory processes have been studied as a potential novel therapeutic target. Our present work aimed to verify and investigate the impact of rP21 in the formation of blood vessels in vitro and in vivo. First, tEnd cells were treated with different concentrations of rP21 or bacterial extract and viability and cellular adhesion were evaluated by MTT and angiogenesis inhibition by Matrigel tube formation assay and murine model. To verify the proteolytic activity of rP21 on extracellular matrix (ECM) components, fibrinogen, matrigel and fibronectin was incubated with rP21 or not. In addition, we performed proliferation assays and cell cycle analysis. Furthermore, the accumulation and distribution of F-actin was determined by Phalloidin staining using ImageJ software. Finally, tEnd cells were incubated with rP21 and the mRNA levels were analyzed by real-time PCR. Our results showed that rP21 did not alter cell viability and adhesion, but strongly inhibited vessel formation in vitro and in vivo. Tube formation assay showed that angiogenesis inhibition was dependent of the CXCR4-rP21 binding. In addition to these results, we observed that the rP21 was able to inhibit cell proliferation and promoted a significant reduction in the number of 4n cells (G2/M phase). Moreover, we found that rP21 significantly increased F-actin levels and this protein was able to modulate expression of genes related to angiogenesis and actin cytoskeleton. However, rP21 showed no significant activity on the matrix components. In this sense, we conclude that the rP21-endothelial cells (ECs) interaction via CXCR4 promotes inhibition of vessel formation through a cascade of intracellular events, such as inhibition of ECs proliferation and modulation of the expression of molecules associated with angiogenic processes and actin cytoskeleton.

Classificação de atividade eletromiográfia facial de indivíduos saudáveis e com hanseníase por meio de máquina de vetores de suporte

A number of studies in the areas of Biomedical Engineering and Health Sciences have employed machine learning tools to develop methods capable of identifying patterns in different sets of data. Despite its extinction in many countries of the developed world, Hansen’s disease is still a disease that affects a huge part of the population in countries such as India and Brazil. In this context, this research proposes to develop a method that makes it possible to understand in the future how Hansen’s disease affects facial muscles. By using surface electromyography, a system was adapted so as to capture the signals from the largest possible number of facial muscles. We have first looked upon the literature to learn about the way researchers around the globe have been working with diseases that affect the peripheral neural system and how electromyography has acted to contribute to the understanding of these diseases. From these data, a protocol was proposed to collect facial surface electromyographic (sEMG) signals so that these signals presented a high signal to noise ratio. After collecting the signals, we looked for a method that would enable the visualization of this information in a way to make it possible to guarantee that the method used presented satisfactory results. After identifying the method's efficiency, we tried to understand which information could be extracted from the electromyographic signal representing the collected data. Once studies demonstrating which information could contribute to a better understanding of this pathology were not to be found in literature, parameters of amplitude, frequency and entropy were extracted from the signal and a feature selection was made in order to look for the features that better distinguish a healthy individual from a pathological one. After, we tried to identify the classifier that best discriminates distinct individuals from different groups, and also the set of parameters of this classifier that would bring the best outcome. It was identified that the protocol proposed in this study and the adaptation with disposable electrodes available in market proved their effectiveness and capability of being used in different studies whose intention is to collect data from facial electromyography. The feature selection algorithm also showed that not all of the features extracted from the signal are significant for data classification, with some more relevant than others. The classifier Support Vector Machine (SVM) proved itself efficient when the adequate Kernel function was used with the muscle from which information was to be extracted. Each investigated muscle presented different results when the classifier used linear, radial and polynomial kernel functions. Even though we have focused on Hansen’s disease, the method applied here can be used to study facial electromyography in other pathologies.