Eventos adversos em hemodiálise: relatos de profissionais de enfermagem

O presente trabalho trata-se de estudo transversal e quantitativo que analisou o conhecimento dos profissionais de enfermagem sobre Eventos Adversos (EA) em uma unidade de hemodiálise de um hospital de ensino. A coleta dos dados ocorreu de fevereiro a abril de 2011, a partir de entrevistas com 25 profissionais. A análise dos dados identificou 517 relatos de 32 tipos, sendo os mais citados: cateter obstruído, retirada acidental da agulha e coagulação do sistema extracorpóreo. As causas relacionadas ao paciente foram mencionadas em 42,8% dos relatos. As principais condutas foram implementação/alteração de protocolos e educação continuada, sendo a última a principal sugestão para a prevenção. Os resultados podem contribuir para uma análise crítica sobre a qualidade do cuidado em unidades de hemodiálise, gerando o desenvolvimento de ações que auxiliem a promoção da segurança dos pacientes.

Raios-x: fascinação, medo e ciência

This work presents the discovery and the use of x-rays at the end of the XIXth and the beginning of the XXth century. X-rays greatly impacted science and everyday life. Their existence broke the idea that knowledge had reached a limiting step. In general, people regarded x-rays as a marvel of science, but reactions against their use were also found. Several applications were proposed, especially in medicine. However, little or no attention was paid to security measures, leading to health damages and even death. The development of the radiological protection took into account the accidents with the x-rays.

Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.

Classification of antimicrobial resistance using artificial neural networks and the relationship of 38 genes associated with the virulence of Escherichia coli isolates from broilers

Avian pathogenic Escherichia coli (APEC) is responsible for various pathological processes in birds and is considered as one of the principal causes of morbidity and mortality, associated with economic losses to the poultry industry. The objective of this study was to demonstrate that it is possible to predict antimicrobial resistance of 256 samples (APEC) using 38 different genes responsible for virulence factors, through a computer program of artificial neural networks (ANNs). A second target was to find the relationship between (PI) pathogenicity index and resistance to 14 antibiotics by statistical analysis. The results showed that the RNAs were able to make the correct classification of the behavior of APEC samples with a range from 74.22 to 98.44%, and make it possible to predict antimicrobial resistance. The statistical analysis to assess the relationship between the pathogenic index (PI) and resistance against 14 antibiotics showed that these variables are independent, i.e. peaks in PI can happen without changing the antimicrobial resistance, or the opposite, changing the antimicrobial resistance without a change in PI.