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Heitor Silvério Lopes

Publications and source records attributed to Heitor Silvério Lopes.

3 recordsLinked to original sources

A dose-response model for the conventional phototherapy of the newborn.

Jaundice of the newborn is a common problem as a consequence of the rapid increment of blood bilirubin in the first days of live. In most cases, it is considered a physiological transient situation, but unmanaged hyperbilirubinemia can lead to death or serious injuries for the survivors. For decades, phototherapy has been used as the main method for prevention and treatment of hyperbilirubinaemia of the newborn. This work aims at finding a predictive model for the decrement of blood bilirubin for patients submitted to conventional phototherapy. Data from the phototherapy of 90 term newborns were collected and used in a multiple regression method. A rigorous statistical analysis was done in order to guarantee a correct and valid model. The obtained model was able to explain 78% of the variation of the dependent variable. We show that it is possible to predict the total serum bilirubin of the patient under conventional phototherapy by knowing its birth weight, bilirubin level at the beginning of treatment and the radiant energy density (dose). Besides, it is possible to infer the time necessary for a given decrement of bilirubin, under approximately constant irradiance. Statistical analysis of the obtained model shows that it is valid for several ranges of birth weight, initial bilirubin level, and radiant energy density. It is expected that the proposed model can be useful in the clinical management of hyperbilirubinemia of the newborn.

Computer Simulation↗

Reconstruction of phylogenetic trees using the ant colony optimization paradigm.

We developed a new approach for the reconstruction of phylogenetic trees using ant colony optimization metaheuristics. A tree is constructed using a fully connected graph and the problem is approached similarly to the well-known traveling salesman problem. This methodology was used to develop an algorithm for constructing a phylogenetic tree using a pheromone matrix. Two data sets were tested with the algorithm: complete mitochondrial genomes from mammals and DNA sequences of the p53 gene from several eutherians. This new methodology was found to be superior to other well-known softwares, at least for this data set. These results are very promising and suggest more efforts for further developments.

Algorithms↗

Neural networks for protein classification.

This paper describes a biomolecular classification methodology based on multilayer perceptron neural networks. The system developed is used to classify enzymes found in the Protein Data Bank. The primary goal of classification, here, is to infer the function of an (unknown) enzyme by analysing its structural similarity to a given family of enzymes. A new codification scheme was devised to convert the primary structure of enzymes into a real-valued vector. The system was tested with a different number of neural networks, training set sizes and training epochs. For all experiments, the proposed system achieved a higher accuracy rate when compared with profile hidden Markov models. Results demonstrated the robustness of this approach and the possibility of implementing fast and efficient biomolecular classification using neural networks.

Algorithms↗