Aim To investigate the protective effects of beraprost sodium on cerebral cortical neuron injury in chronic aluminum-overload rats and its effects on PGIS-IP signaling pathway. Methods 75 SD rats were randomized into five groups: normal control group, chronic aluminum-overload group ( model group) and beraprost sodium groups-low dose (6 μg· kg-1 ), medium dose ( 12 μg · kg-1 ) and high dose (24 μg·kg-1). Aluminum gluconate (Al3+ 200 mg ·kg-1 d-1, once a day, 5d a week, for 20 weeks, p. o. ) was administered to rats of cerebral damage model. The rats of experimental groups were concomi-tantly treated with beraprost sodium ( p. o. ) daily for 20 weeks. After the model was built successfully, the spatial learning and memory( SLM) function was done by Morris water maze. The cortical neurons damage was detected by HE staining, SOD activities and MDA contents. The 6-k-PGF1α levels in cortex were meas-ured by ELISA. The expressions of PGIS, IP mRNA and IP protein were also studied. Results Compared with the rats of normal control group, the SLM function was significantly impaired ( P<0. 01 ) and considera-ble karyopycnosis was observed in model group rats. The SOD activities were weakened ( P <0. 01 ), the MDA contents increased ( P<0. 05 ) and the levels of 6-k-PGF1α raised significantly ( P <0. 01). The ex-pressions of PGIS and IP mRNA in the rats cortex obvi-ously increased ( P<0. 01 ), so did the expression of IP protein(P<0. 05). Compared with the rats of mod-el group, the SLM function of rats in experimental groups decreased significantly ( P<0. 01 ) and damage of cortical neurons reduced remarkably. The SOD ac-tivities increased ( P <0. 01 ) and the MDA contents decreased ( P <0. 01). Besides, the content of 6-k-PGF1α, the expressions of PGIS mRNA and IP protein in the rats cortex decreased significantly ( P<0. 05 ) as well as IP mRNA ( P<0. 01). Conclusion Our re-sults demonstrate that in cerebral cortical neuron of chronic aluminum-overload rats, beraprost sodium has notably protective effects and the mechanism might be related to PGIS-IP signaling pathway.

Objective To survey the situation of spinal cord injuries (SCI) in Beijing.Methods China Rehabilitation Research Center and Information Center of Beijing Health Bureau sponsored the surveillance of 86 hospitals in Beijing which had hospitalized SCI patients in 2002. The faculty of surveillance was composed of trained professionals. The number of registered SCI patients in 2002 was 1077, and 264 patients in 11 hospitals were chosen to be investigated in detail according to stratified sampling result.Results There were 1077 registered patients with a neurological deficit and the annual SCI incidence was 60 per million. The ratio of male to female was 3:1 and the ratio of cervical, thoracic, lumbar injuries and others is 4.9%, 28%, 66.7%, 0.4% respectively. The mean age at the time of injury was 41 years. The causes most frequently seen were falls from a height and traffic accidents. The mean time of hospitalization was 18.9 days and the mean expenditure of hospitalization was 27819.3 RMB. Four patients were transferred to rehabilitation hospitals, and others went homes directly after discharge.Conclusion There are many reasons for the high annual SCI incidence in Beijing and the first SCI cause was falling from a height, which should be pay special attention when the prevention measures are taken into account. The rate of SCI patients who received systemic medical rehabilitation was low.

Objective To explore the program of convolutional neural networks for the diagnosis of schizophrenia and evaluate its effects. Methods Using the convolutional neural network,the training model was trained in the lead data of 138 normal people and 183 schizophrenic patients,and the model was valida-ted by 20-fold cross-validation. Results The true positive rate of schizophrenia prediction using the convolu-tional neural network training model was 0. 749, the false positive rate was 0. 275, and the accuracy was 0. 738. Conclusion This model can achieve a strong diagnostic ability for patients with schizophrenia. Therefore,convolutional neural network for the diagnosis of schizophrenia will become an important research direction in the future.

Objective:To establish a radiomics-based biomarker for predicting pathological response after preoperative neoadjuvant chemoradiotherapy (nCRT) in locally advanced esophageal cancer.Methods:From 2008 to 2018, 112 patients with locally advanced esophageal cancer who received nCRT were enrolled. All patients were treated with preoperative nCRT combined with surgery. Enhanced CT images and clinical information before nCRT were collected. A lesion volume of interest was manually delineated. In total, 670 radiomics features (including tumor intensity, shape and size, texture and wavelet characteristics) were extracted using the pyradiomics package in PYTHON. The stepwise regression combined with the best subset were employed to select the features, and finally the Logistic regression model was adopted to establish the prediction model. The performance of the classifier was evaluated by the area under the ROC curve (AUC). Results:The pathological complete remission (pCR) rate was 58.0%(65/112). 10 radiomics features were included in the final model, The most relevant radiomics feature was the gray feature (the texture information of the image), followed by the shape and voxel intensity-related features. In the training set, the AUC was 0.750 with a sensitivity of 0.711 and a specificity of 0.778, the corresponding values in the testing set were 0.870, 0.757 and 0.900, respectively.Conclusions:Models based on radiomics features from CT images can be utilized to predict the pathological response to nCRT in esophageal cancer. As it is efficient, non-invasive and economic model, it could serve as a promising tool for individualized treatment when validated by further prospective trials in the future.