Effects of chronic stress and high-fat diet on metabolic and nutritional parameters in Wistar rats

Objective: The aim of this study was assess the role of chronic stress on the metabolic and nutritional profile of rats exposed to a high-fat diet. Materials and methods: Thirty-day-old male Wistar rats (70-100 g) were distributed into four groups: normal-diet (NC), chronic stress (St), high-fat diet (HD), and chronic stress/high-fat diet (HD/St). Stress consisted at immobilization during 15 weeks, 5 times per week, 1h per day; and exposure to the high-fat diet lasted 15 weeks. Nutritional and metabolic parameters were assessed. The level of significance was 5%. Results: The HD group had final body weight, total fat, as well as insulin and leptin increased, and they were insulin resistant. The St and HD/St had arterial hypertension and increased levels of corticosterone. Stress blocked the effects of the high-fat diet. Conclusion: Chronic stress prevented the appearance of obesity. Our results help to clarify the mechanisms involved in metabolic and nutritional dysfunction, and contribute to clinical cases linked to stress and high-fat diet.

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.