Miguel Angel Campos Hernández

Manual lymphatic drainage therapy in patients with breast cancer related lymphoedema

BackgroundLymphoedema is a common and troublesome condition that develops following breast cancer treatment. The aim of this study is to analyze the effectiveness of Manual Lymphatic Drainage in the treatment of postmastectomy lymphoedema in order to reduce the volume of lymphoedema and evaluate the improvement of the concomitant symptomatology.MethodsA randomized, controlled clinical trial in 58 women with post-mastectomy lymphoedema. The control group includes 29 patients with standard treatment (skin care, exercise and compression measures, bandages for one month and, subsequently, compression garnments). The experimental group includes 29 patients with standard treatment plus Manual Lymphatic Drainage. The therapy will be administered daily for four weeks and the patients condition will be assessed one, three and six months after treatment.The primary outcome parameter is volume reduction of the affected arm after treatment, expressed as a percentage. Secondary outcome parameters include: duration of lymphoedema reduction and improvement of the concomitant symptomatology (degree of pain, sensation of swelling and functional limitation in the affected extremity, subjective feeling of being physically less atractive and less feminine, difficulty looking at oneself naked and dissatisfaction with the corporal image).DiscussionThe results of this study will provide information on the effectiveness of Manual Lymphatic Drainage and its impact on the quality of life and physical limitations of these patients.Trial registrationClinicalTrials (NCT): NCT01152099

Adaptive output-feedback decentralized control of a class of second order nonlinear systems using recurrent fuzzy neural networks

In this paper the design of an adaptive output-feedback decentralized control for the class of second order nonlinear affine interconnected systems based on recurrent fuzzy neural networks (RFNN) is addressed. First, a centralized control that needs the state measurements of all subsystems is designed. Then a decentralized control using the local state measurements is obtained by adding a control component aimed at compensating for the interconnections. Finally, an adaptive output-feedback decentralized control based on an RFNN is designed. In design of such controller, no separated state estimator is needed, since the controller dynamics is embedded in the recurrent network. Practical tracking is established by invoking Lyapunov stability analysis. Simulation and experimental results are presented to evaluate the performance of the proposed control law.