The prevalence of molecular and immunologic infective markers of hepatitis viruses in patients with hematological malignancies.

Acute and chronic viral hepatitis infections are corresponding to increase the risk of different types of hematological malignancies especially with leukemia. In this study the serological and molecular markers of hepatitis viruses were evaluated in patients with different types of leukemia in comparing with control group. In this cross sectional study, 100 EDTA-treated blood samples were collected from leukemia patients and also from healthy control group, respectively. Serological and molecular markers of HBV, HCV and HDV viruses were analyzed for determination of the role of these hepatitis viruses in clinical outcomes of leukemia disorders. Increasing risk factors of leukemia were evaluated statistically in two studied groups by SPSS software. One of molecular and immunological markers of HBV, HDV and HCV was found in 24 of 100 (24%), 22 of 100 (22%), and 1 of 100 (1%) patients with leukemia and in 12 of 100 (12%), 6 of 100 (6%), and 2 of 100 (2%) control patients. Significant differences were detected in detection of HBsAg (P = 0.02), HBeAb (P = 0.009), and HCV-RNA (P = 0.05) between leukemia patients and control group, respectively. The high prevalence of HBV and HCV infective markers were detected in ALL and AML patients. Identification of high prevalence of HBV and HCV infective markers in leukemia patients proposed strong association between hepatitis viral infections and leukemia. Therefore, evaluation of the prevalence of viral hepatitis infections in larger groups of patients with long lasting follow up is suggesting.

Detection of reaching intention using EEG signals and nonlinear dynamic system identification.

BACKGROUND AND OBJECTIVES: Low frequency electroencephalography (EEG) signals are associated with preparation of movement and thus provide valuable information for brain-machine interface applications. The purpose of this study was to detect movement intention from EEG signals before execution of self-paced arm reaching movements. METHODS: Ten healthy individuals were recruited. Movement onset was determined from surface electromyography recordings time-locked with EEG signals. Unlike previous studies, which employed feature extraction and classification for decoding, a nonlinear dynamic multiple-input/single output (MISO) model was developed. The MISO model consisted of a cascade of Volterra structures and a threshold block, generating the binary output corresponding to intention/no-intention. The modeling process included input selection from a pool of different EEG channels. The predictive performance of the model was evaluated using the receiver operating characteristics curve, from which the optimum threshold was also selected. The Mann-Whitney statistics was employed to select the significant EEG channels for the output by examining the statistical significance of improvement in the predictive capability of the model when the respective channels were included. RESULTS: With the proposed approach, movement intention was detected approximately 500 ms before the movement onset and on average, with an accuracy of 96.37 ± 0.94%, a sensitivity of 77.93 ± 4.40% and a specificity of 98.52 ± 1.19%. CONCLUSIONS: The model output can be converted to motion commands for neuroprosthetic devices and exoskeletons in future applications.