ABSTRACT
Leprosy, also known as Hansen's, is one of the listed neglected tropical diseases as a major health problem global. Treatment is one of the main alternatives, however, the scarcity of medication and its poor distribution are important factors that have driven the spread of the disease, leading to irreversible and multi-resistant complications. This paper uses a distribution methodology to optimize medication administration, taking into account the most relevant attributes for the epidemiological profile of patients and the deficit in treatment via Polychemotherapy. Multi-criteria Decision Methods were applied based on AHP-Electre model in a database with information from patients in the state of Para between 2015 and 2020. The results pointed out that 84% of individuals did not receive any treatment and, among these, the method obtained a gain in the distribution of 68% in patients with positive diagnosis for leprosy.
Subject(s)
Leprosy , Humans , Pharmaceutical Preparations , Leprosy/drug therapy , Leprosy/epidemiology , Leprosy/diagnosis , Drug Therapy, Combination , Data Management , Databases, FactualABSTRACT
Noncoding RNAs (ncRNAs) have been focus of intense research over the last few years. Since characteristics and signals of ncRNAs are not entirely known, researchers use different computational tools together with their biological knowledge to predict putative ncRNAs. In this context, this work presents ncRNA-Agents, a multi-agent system to annotate ncRNAs based on the output of different tools, using inference rules to simulate biologists' reasoning. Experiments with data from the fungus Saccharomyces cerevisiae allowed to measure the performance of ncRNA-Agents, with better sensibility, when compared to Infernal, a widely used tool for annotating ncRNA. Besides, data of the Schizosaccharomyces pombe and Paracoccidioides brasiliensis fungi identified novel putative ncRNAs, which demonstrated the usefulness of our approach. NcRNA-Agents can be be found at: http://www.biomol.unb.br/ncrna-agents.
