Chagas disease is endemic in Latin America and is caused by the protozoan hemoflagellate parasite Trypanosoma cruzi. Nowadays, it has also been disseminated to non-endemic countries due to the ease of global mobility. The nitroheterocycle benznidazole is currently used to treat this neglected tropical disease, although this drug causes severe side effects and has limited efficacy during the chronic phase of the disease. Proteomics and bioinformatics have recently become powerful tools in the identification of new drug targets. In the last decade, proteomic profiles of different T. cruzi forms under distinct experimental conditions were assessed. These reports have pointed to many potential drug targets, with ergosterol biosynthesis-related proteins and redox system enzymes being the most promising candidates. Nevertheless, the majority of the compounds active against T. cruzi still have unclear mechanisms of action, and most proteomic efforts have studied epimastigotes (the non-clinically relevant insect form of the parasite). Additional analyses with the clinically relevant parasite forms should be performed to identify proteins that actually bind drugs active against T. cruzi. Nonetheless, due to the known technical hurdles in generating such experimental data, bioinformatic approaches that integrate currently available data to generate additional knowledge will also be useful. Here, we review T. cruzi proteomics and describe the main chemoproteomic methods and their application to the identification of trypanosomatid drug targets. Finally, we discuss the potential benefits of more extensively integrating all proteomic data with other molecular databases via bioinformatic analyses to develop novel, viable strategies for alternative treatments of Chagas disease.
Educational Data Mining (EDM) may be a very useful technique as much to understand student behavior as to plan and manage government investments in education. EDM helps to analyzes and to expose the hidden information of educational data. Particularly, an important application of EDM is to predict or analyze the students' dropout. This problem affects several educational institutions in Brazil and the world, and identify its origin has been a relevant research motivator. This paper presents a brief introduction about EDM applied to predict students' dropout and analyzes some important articles during the period from 2013 to 2018.
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