[Higher dose of erythropoietin for anemia correction in balkan endemic nephropathy patients].

INTRODUCTION: Balkan endemic nephropathy (BEN) patients maintained with hemodialysis have more severe anemia than patients with other kidney diseases. OBJECTIVE: The aim of the study was to compare the improvement of anemia in BEN patients and those with other kidney diseases during treatment with human recombinant erythropoietin (rHuEpo). METHODS: The study involved 240 patients on regular hemodialysis for more than one year. Out of them 146 had BEN and 94 other kidney diseases (21 glomerulonephritis, 20 hypertension, 18 diabetes, 10 policystic kidney disease, 5 obstructive nephropathy, 18 other diseases). Treatment with rHuEpo was carried out according to European guidelines for the management of anemia. RESULTS: Patients with BEN were older and were less frequently treated with ACEi than patients with other kidney diseases. At the onset of the study mean hemoglobin level (109.6 +/- 22.3 vs. 112.7 +/- 11.3 g/l) was significantly lower, but serum ferritin level and rHuEpo dose (65.4 +/- 22.3 vs. 57.5 +/- 22.5 U/kg/week) were significantly higher in BEN patients than in others. In prospective four months study these differences in hemoglobin levels and rHuEpo doses maintained.The rate of anemia improvement was examined in 15 BEN patients and 10 patients with other kidney diseases at the beginning of rHuEpo treatment. No difference in the rate of anemia improvement was found between the two groups but higher rHuEpo doses were used in BEN patients. CONCLUSION: Patients with BEN on regular hemodialysis had more severe anemia and required higher rHuEpo doses for maintaining target hemoglobin level.

A computational model of limb impedance control based on principles of internal model uncertainty.

Efficient human motor control is characterized by an extensive use of joint impedance modulation, which is achieved by co-contracting antagonistic muscles in a way that is beneficial to the specific task. While there is much experimental evidence available that the nervous system employs such strategies, no generally-valid computational model of impedance control derived from first principles has been proposed so far. Here we develop a new impedance control model for antagonistic limb systems which is based on a minimization of uncertainties in the internal model predictions. In contrast to previously proposed models, our framework predicts a wide range of impedance control patterns, during stationary and adaptive tasks. This indicates that many well-known impedance control phenomena naturally emerge from the first principles of a stochastic optimization process that minimizes for internal model prediction uncertainties, along with energy and accuracy demands. The insights from this computational model could be used to interpret existing experimental impedance control data from the viewpoint of optimality or could even govern the design of future experiments based on principles of internal model uncertainty.