Possible underestimation by sports medicine of the effects of early physical exercise practice on the prevention of diseases in adulthood

In modern society, combatting cardiovascular and metabolic diseases has been highlighted as an urgent global challenge. In recent decades, the scientific literature has identified that behavioral variables (e.g. smoking, unhealthy diet and physical inactivity) are related to the development of these outcomes and, therefore, preventive actions should focus on the promotion of physical exercise practice and a healthy diet, as well as combatting the smoking habit from an early age. The promotion of physical exercise in the general population has been suggested as a relevant goal by significant health organizations around the world. On the other hand, recent literature has indicated that physical exercise performed in early life prevents the development of diabetes mellitus, dyslipidemia and arterial hypertension during adulthood, although this protective effect seems to be independent of the physical activity performed during adulthood. Apparently, the interaction between physical exercise and human growth in early life constitutes an issue which is not completely understood by sports medicine. The aim of the present review was therefore to discuss recent evidence on the effects of physical exercise performed during childhood and adolescence on cardiovascular and metabolic outcomes in adulthood.

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.

Impacto do exercício físico combinado na função diastólica de pacientes com insuficiência cardíaca e fração de ejeção reduzida

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)