Objective To survey the value of risperidone in the treatment of depression without psychotic symptoms. Methods 205 depressive patients were randomly divided into 3 groups: risperidone group, fluoxetine group and combined group (risperidone+fluoxetine). They were assessed with Hamilton Depression Scale (HAMD), Treatment Emergent Symptom Scale (TESS) before treatment and in 2nd week, 4th week, 6th week and 8th week after treatment. Results The reduction rate of HAMD of combined group was the best among three groups(P<0.05), and that of fluoxetine group was better than that of risperidone(P<0.01). The significant difference in reduction rate of HAMD between combined group and other two groups was observed since 2nd week after treatment(P<0.05). No difference in scores of TESS has been observed in any time among three groups. Conclusion Risperidone can improve the efficacy of fluoxetine on depression without psychotic symptoms without increasing side effects, but itself is less effective than fluoxetine.

The preparation and certification of five China Sea and continental shelf sediment reference materials MSCS-1- 5 are reported. The raw samples were collected separately from the East China Sea and the South China Sea. First,they were ground by a ball mill to a homogenous powder of less than 74μm,then these samples was further processed by an ultra-fine processing technique,a jet mill,to form an ultra-fine powder. The particle size distribution of the samples was determined with a laser particle-analyser,their average particle size is < 4μm. The homogeneity was tested by high-precision WD-XRF and the minimum sampling mass is 5 mg,which was confirmed by XRF,ICP-AES and ICP-MS. Twelve laboratories participated in the cooperative study and 60 constituents were determined. 50 and 51 components were certified as certified values and 1 and 2 components as reference values respectively for MSCS-1 and MSCS-2,52 constituents were certified as certified values for MSCS-3,4 and 5. The sum of the major and minor components in the five reference materials MSCS-1 -5 is 99.9% ,99.6% ,100.4% ,100.1% and 99.7% ,respectively.

Melanocytic lesions occur on the surface of the skin, in which the malignant type is melanoma with a high fatality rate, seriously endangering human health. The histopathological analysis is the gold standard for diagnosis of melanocytic lesions. In this study, a fully automated intelligent diagnosis method based on deep learning was proposed to classify the pathological whole slide images (WSI) of melanocytic lesions. Firstly, the color normalization based on CycleGAN neural network was performed on multi-center pathological WSI; Secondly, ResNet-152 neural network-based deep convolutional network prediction model was built using 745 WSI; Then, a decision fusion model was cascaded, which calculates the average prediction probability of each WSI; Finally, the diagnostic performance of the proposed method was verified by internal and external test sets containing 182 and 54 WSI, respectively. Experimental results showed that the overall diagnostic accuracy of the proposed method reached 94.12% in the internal test set and exceeded 90% in the external test set. Furthermore, the color normalization method adopted was superior to the traditional color statistics-based and staining separation-based methods in terms of structure preservation and artifact suppression. The results demonstrate that the proposed method can achieve high precision and strong robustness in pathological WSI classification of melanocytic lesions, which has the potential in promoting the clinical application of computer-aided pathological diagnosis.
Humans ; Deep Learning ; Melanoma/pathology* ; Diagnosis, Computer-Assisted ; Neural Networks, Computer ; Skin/pathology*