Objective To assess the effectiveness and safety of mycophenolate mofetil (MMF) in the treatment of proliferative lupus nephritis. Methods We searched CBM (November 1979 to February 2006), Chinese Cochrane Centre Database (2005), The Cochrane Library (Issue 4, 2005), MEDLINE (November 1966 to February 2006) and EMBASE (1975 to February 2006) for randomize controlled trials. Data were extracted and analyzed using The Cochrane Collaboration’s RevMan 4.2.7. Results Nine randomize controlled trials involving 512 patients met the inclusion criteria. The meta-analysis showed that the total clinical effective rate and complete remission rate were not significantly higher for MMF than for cyclophosphamide, azathioprine, or both. Renal survival rate and relapse rate of MMF were not significantly different from those for cyclophosphamide, azathioprine, or both. Patient survival rate and safety of MMF were significantly improved compared with cyclophosphamide, azathioprine, or both. Conclusion More large-scale multi-center randomized trials are needed to investigate the role of MMF in the treatment of proliferative lupus nephritis.
Cognitive impairment is one of the three primary symptoms of schizophrenic patients and shows important value in early detection and warning for high-risk individuals. To study the specifics of electroencephalogram (EEG) in patients with schizophrenia under the cognitive load, we collected EEG signals from 17 schizophrenic patients and 19 healthy controls, extracted signals of each band based on wavelet transform, calculated the characteristics of nonlinear dynamic and functional brain networks, and automatically classified the two groups of people by using a machine learning algorithm. Experimental results indicated that the correlation dimension and sample entropy showed significant differences in α, β, θ, and γ rhythm of the Fp1 and Fp2 electrodes between groups under the cognitive load. These results implied that the functional disruptions in the frontal lobe might be the important factors of cognitive impairments in schizophrenic patients. Further results of the automatic classification analysis indicated that the combination of nonlinear dynamics and functional brain network properties as the input characteristics of the classifier showed the best performance, with the accuracy of 76.77%, sensitivity of 72.09%, and specificity of 80.36%. The results of this study demonstrated that the combination of nonlinear dynamics and function brain network properties may be potential biomarkers for early screening and auxiliary diagnosis of schizophrenia.
A great number of studies have demonstrated the structural and functional abnormalities in chronic schizophrenia (SZ) patients. However, few studies analyzed the differences between first-episode, drug-naive SZ (FESZ) patients and normal controls (NCs). In this study, we recruited 44 FESZ patients and 56 NCs, and acquired their multi-modal magnetic resonance imaging (MRI) data, including structural and resting-state functional MRI data. We calculated gray matter volume (GMV), regional homogeneity (ReHo), amplitude of low frequency fluctuation (ALFF), and degree centrality (DC) of 90 brain regions, basing on an automated anatomical labeling (AAL) atlas. We then applied these features into support vector machine (SVM) combined with recursive feature elimination (RFE) to discriminate FESZ patients from NCs. Our results showed that the classifier using the combination of ReHo and ALFF as input features achieved the best performance (an accuracy of 96.97%). Moreover, the most discriminative features for classification were predominantly located in the frontal lobe. Our findings may provide potential information for understanding the neuropathological mechanism of SZ and facilitate the development of biomarkers for computer-aided diagnosis of SZ patients.