Anti-inflammatory effect of exercise training in subjects with type 2 diabetes and the metabolic syndrome is dependent on exercise modalities and independent of weight loss

We investigated the effect of different exercise modalities on high sensitivity-C reactive protein (hs-CRP) and other inflammatory markers in patients with type 2 diabetes and the metabolic syndrome. Eighty-two patients were randomized into 4 groups: sedentary control (A); receiving counseling to perform low-intensity physical activity (B); performing prescribed and supervised high-intensity aerobic (C) or aerobic + resistance (D) exercise (with the same caloric expenditure) for 12 months. Evaluation of leisure-time physical activity and assessment of physical fitness, cardiovascular risk factors and inflammatory biomarkers was performed at baseline and every 3 months. Volume of physical activity increased and HbA1c decreased in Groups B–D. VO2max, HOMA-IR index, HDL-cholesterol, waist circumference and albuminuria improved in Groups C and D, whereas strength and flexibility improved only in Group D. Levels of hs-CRP decreased in all three exercising groups, but the reduction was significant only in Groups C and D, and particularly in Group D. Changes in VO2max and the exercise modalities were strong predictors of hs-CRP reduction, independent of body weight. Leptin, resistin and interleukin-6 decreased, whereas adiponectin increased in Groups C and D. Interleukin-1β, tumor necrosis factor-α and interferon-γ decreased, whereas anti-inflammatory interleukin-4 and 10 increased only in Group D. In conclusion, physical exercise in type 2 diabetic patients with the metabolic syndrome is associated with a significant reduction of hs-CRP and other inflammatory and insulin resistance biomarkers, independent of weight loss. Long-term high-intensity (preferably mixed) training, in addition to daytime physical activity, is required to obtain a significant anti-inflammatory effect.

Optimal control of non-linear systems through hybrid cell-mapping/artificial-neural-networks techniques

In this paper, a real-time optimal control technique for non-linear plants is proposed. The control system makes use of the cell-mapping (CM) techniques, widely used for the global analysis of highly non-linear systems. The CM framework is employed for designing approximate optimal controllers via a control variable discretization. Furthermore, CM-based designs can be improved by the use of supervised feedforward artificial neural networks (ANNs), which have proved to be universal and efficient tools for function approximation, providing also very fast responses. The quantitative nature of the approximate CM solutions fits very well with ANNs characteristics. Here, we propose several control architectures which combine, in a different manner, supervised neural networks and CM control algorithms. On the one hand, different CM control laws computed for various target objectives can be employed for training a neural network, explicitly including the target information in the input vectors. This way, tracking problems, in addition to regulation ones, can be addressed in a fast and unified manner, obtaining smooth, averaged and global feedback control laws. On the other hand, adjoining CM and ANNs are also combined into a hybrid architecture to address problems where accuracy and real-time response are critical. Finally, some optimal control problems are solved with the proposed CM, neural and hybrid techniques, illustrating their good performance.