Objective To study the effect of cognitive treatment on the compliance for medication and recrudescence of patients with schizophrenia.Method 106 schizophrenic patients were randomly allocated to the study and the control; both groups treated with antipsychotic medical treatment and health education, while the study group also accepted cognitive psychotherapy. Before treatment and at the ends of 0, 6, 12 months of treatment, the efficacy were investigated using the Brief Psychiatric Rating Scale (BPRS), Insight and Treatment Attitude Questionnaire (ITAQ) and Self-designed Compliance Survey in order to assess the compliance and the recrudescence. Results All the indexes after cognitive psychotherapy were significantly better than those before cognitive psychotherapy. Conclusion The cognitive psychotherapy can effectively reinforce the compliance of patients with schizophrenia, and then improve the quality of life for them.

Objective To investigate the expression of MMP-2 and TIMP-2 during the course of traumatic PVR treated with GM6001 and without GM6001,and to explore the potential role of MMP-2 and TIMP-2 during the course of traumatic PVR and to evaluate the effect of GM6001 on traumatic PVR prevention and treatment.Methods 360 SD rats were divided randomly into three groups: normal control group,the traumatic PVR group,the traumatic PVR treated with GM6001 group.The normal control group was intravitreous injected with normal saline.The traumatic PVR group was intravitreous injected with the PRP.The traumatic PVR treated with GM6001 group was intravitreous injected with the PRP and GM6001.The expression of MMP-2 and TIMP-2 were qualitativly and semiquantitativly analyzed with immunohistochemistry on day 1,3,7,14,21 and 28.Results 1.The results of immunohistochemistry showed that the expression of MMP-2,TIMP-2 was mainly located in the photoreceptor cells layer,out plexiform layer,inner plexiform layer and nerve fiber layer.2.The expression of MMP-2 in the normal group and the traumatic PVR treated with GM6001 group was weak at all time.The differences were statistical significance as compared with the normal group and the traumatic PVR treated with GM6001 group(P

Objective To investigate effect of carnosine on oxygen glucose deprivation/reperfusion ( OGD/RP) induced injury in rat brain slices. Methods Injury of brain slices was determined by TTC methods.The contents of ATP, ADP and AMP were determined by high performance liquid chromatography.Reactive Oxygen species ( ROS) were determined by fluorescence methods.Results Compared with control group, rat hippocampal slices were significantly damaged by OGD/RP, indicated by light color and decreased A490 nm value of TTC staining.Meanwhile the contents of ATP and ADP were significantly decreased, and the content of AMP and ROS were significantly increased, the difference between two group was significant ( P<0.01).Pre-incubation with Carnosine (1000, 200, 40 μg/mL) significantly inhibited the light color and decreased A490 nm value of TTC staining, increased the contents of ATP, ADP and AMP, and decreased the content of ROS, the difference between two group was significant ( P <0.01 ) . Conclusion Carnosine can protect rat hippocampal slices against injury induced by OGD/RP, which may relate to improve the energy metabolism and strengthen the ability of anti-oxidative stress.

Objective:To establish a disease risk prediction model for the newborn screening system of inherited metabolic diseases by artificial intelligence technology.Methods:This was a retrospectively study. Newborn screening data ( n=5 907 547) from February 2010 to May 2019 from 31 hospitals in China and verified data ( n=3 028) from 34 hospitals of the same period were collected to establish the artificial intelligence model for the prediction of inherited metabolic diseases in neonates. The validity of the artificial intelligence disease risk prediction model was verified by 360 814 newborns ' screening data from January 2018 to September 2018 through a single-blind experiment. The effectiveness of the artificial intelligence disease risk prediction model was verified by comparing the detection rate of clinically confirmed cases, the positive rate of initial screening and the positive predictive value between the clinicians and the artificial intelligence prediction model of inherited metabolic diseases. Results:A total of 3 665 697 newborns ' screening data were collected including 3 019 cases ' positive data to establish the 16 artificial intelligence models for 32 inherited metabolic diseases. The single-blind experiment ( n=360 814) showed that 45 clinically diagnosed infants were detected by both artificial intelligence model and clinicians. A total of 2 684 cases were positive in tandem mass spectrometry screening and 1 694 cases were with high risk in artificial intelligence prediction model of inherited metabolic diseases, with the positive rates of tandem 0.74% (2 684/360 814)and 0.46% (1 694/360 814), respectively. Compared to clinicians, the positive rate of newborns was reduced by 36.89% (990/2 684) after the application of the artificial intelligence model, and the positive predictive values of clinicians and artificial intelligence prediction model of inherited metabolic diseases were 1.68% (45/2 684) and 2.66% (45/1 694) respectively. Conclusion:An accurate, fast, and the lower false positive rate auxiliary diagnosis system for neonatal inherited metabolic diseases by artificial intelligence technology has been established, which may have an important clinical value.