Perfil de mutações e resistência do Vírus da Imunodeficiência Humana (HIV) em pacientes coinfectados com o Vírus da Hepatite B (VHB)

Pós-graduação em Pesquisa e Desenvolvimento (Biotecnologia Médica) - FMB

Staphylococcal enterotoxins: molecular aspects and detection methods

Members of the Staphylococcus genus, especially Staphylococcus aureus, are the most common pathogens found in hospitals and in community-acquired infections. Some of their pathogenicity is associated with enzyme and toxin production. Until recently, S. aureus was the most studied species in the genus; however, in last few years, the rise of infections caused by coagulase-negative staphylococci has pointed out the need for further studies on virulence factors that have not yet been completely elucidated so as to better characterize the pathogenic potential of this group of microorganisms. Several staphylococcal species produce enterotoxins, a family of related proteins responsible for many diseases, such as the toxic-shock syndrome, septicemia and food poisoning. To this date, 23 different enterotoxin types have been identified besides toxic-shock syndrome toxin-1 (TSST-1), and they can be divided into five phylogenetic groups. The mechanism of action of these toxins includes superantigen activity and emetic properties, which can lead to biological effects of infection. Various methods can detect genes that encode enterotoxins and their production. Molecular methods are the most frequently used at present. This review article has the objective to describe aspects related to the classification, structure and regulation of enterotoxins and toxic-shock syndrome toxin-1 detection methods.

The use of artificial neural networks in analysing the nutritional ecology of Chrysomya megacephala (F.) (Diptera: Calliphoridae), compared with a statistical model

Artificial neural networks (ANNs) have been widely applied to the resolution of complex biological problems. An important feature of neural models is that their implementation is not precluded by the theoretical distribution shape of the data used. Frequently, the performance of ANNs over linear or non-linear regression-based statistical methods is deemed to be significantly superior if suitable sample sizes are provided, especially in multidimensional and non-linear processes. The current work was aimed at utilising three well-known neural network methods in order to evaluate whether these models would be able to provide more accurate outcomes in relation to a conventional regression method in pupal weight predictions of Chrysomya megacephala, a species of blowfly (Diptera: Calliphoridae), using larval density (i.e. the initial number of larvae), amount of available food and pupal size as input data. It was possible to notice that the neural networks yielded more accurate performances in comparison with the statistical model (multiple regression). Assessing the three types of networks utilised (Multi-layer Perceptron, Radial Basis Function and Generalised Regression Neural Network), no considerable differences between these models were detected. The superiority of these neural models over a classical statistical method represents an important fact, because more accurate models may clarify several intricate aspects concerning the nutritional ecology of blowflies.