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.

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.

Manipulação gênica do protozoário Neospora caninum como ferramenta para o estudo das interações parasito-hospedeiro

Neospora caninum is an obligate intracellular parasite classified in the phylum Apicomplexa, characterized by the presence of the apical complex composed by micronemes proteins, rhoptries and dense granules, used by parasite during the adhesion and invasion process of the host cell. This is the mean event in infection pathogenesis generated by N. caninum and other parasites from the phylum Apicomplexa, promoting influence in the parasite biology and the interface between the parasite and its host. Therefore, molecular tools have been developed in order to identify and characterize these possible virulence factors. Thus, the present study sought to establish a specific system of genetic manipulation of N. caninum, searching for the improvement of the genetics manipulation of this parasite. So, we developed genetically depleted N. caninum to Rop9 rhoptry using the pU6-Universal CRISPR-Cas9 plasmid of T. gondii modified by the insertion of Ku80. The Rop9 depleted parasite showed important during initial phase of invasion and replication of the parasite, however it was not characterized as a potential virulence fator for N. caninum. Furthermore, T. gondii proteins were expressed in N. caninum by the use of specific vectors for this parasite, showing an heterologous system for the study of Toxoplasma proteins, due to the fact that Gra15 or Gra24 of type II T. gondii and Rop16 of type I T. gondii were expressed in N. caninum tachyzoites in a stable way and keept its biological phenotype, as already presented the former parasite, that naturaly expresses these proteins. In addition, it was observed that N. caninum induced an inflammasome activation through NLRP3, ASC and Caspase-1. IL-1R/MyD88 demonstrated an indirect pathway in the control of parasite replication. Furthermore, it was observed that this activation is dependent of the potassium efflux and that different strains of N. caninum keep this activation profile. However, T. gondii strains block this activation, making necessary a prior signal in order to active the inflamosome pathway. Type I T. gondii Rop16 was identified as responsible for blocking this activation, in a dependent way to the STAT3 activation. Therefore, the development of molecular tools and their application in N. caninum may prove to be useful to identify and characterize virulent factors involved in the pathogenesis by these two protozoans.