BACKGROUND: Glucose metabolism disturbance, one of clozapine's adverse effects, has received increasing attention from endocrinologists. Insulin resistance is believed to be associated with clozapine-induced glucose metabolism disturbance. Does it have direct effects on secretion function of islets?OBJECTIVE: To investigate the effects of different concentrations of clozapine on the secretion function of pancreatic islets in vitro.DESIGN: A completely randomized controlled design for the experimental study.SETTING: Department ofPsychiatry, People's Hospital of Wuhan University.MATERIALS: The experiment was conducted in the Laboratory Center of Stomatology Hospital of Wuhan University from September 2003 to January2004. Three healthy male Wistar rats of clean grade were used.METHODS: [1] Classical collagenase digestion method was used to isolate and purify the rat islets of Langerhans. [2] Hank's solution containing 2 g/L bovine serum albumin and 3.3 mmol/L glucose was added for preincubation for 30 minutes. The supernatant was removed. The wells were divided into five groups with 12 wells in each group. The control group was added with 1 g/L dimethyl sulphoxide (DMSO) and 3.3 mmol/L or 16.7 mmol/L glucose, 1 mL per well. The four experimental groups were added with 1 mL 0.2 μmol/L, 1 μmol/L, 5 μmol/L, or 10 μmol/L clozapine apart from the above DMSO and glucose. Six wells in each group were incubated for 1 hour, and another six wells were incubated for 4hours. The supernatants of the different groups were collected and stored at-20℃ in the refrigerator for later testing. The above procedures were repeated three times. The insulin released into the medium supernatant was examined by radioimmunoassay. [3] One-way ANOVA was used to compare the differences between the experimental groups and control group; LSD-t test was used for multiple comparison.MAIN OUTCOME MEASURES: The level of insulin secretion in the supernatants in culture solution containing 3.3 mmol/L or 16.7 mmol/L glucose which was incubated for 1 hour or 4 hours.RESULTS: [1] At 3.3 mmol/L glucose, no difference in insulin secretion was found between the four clozapine groups and control group after 1-hour incubation (P ＞ 0.05). After 4-hour incubation, the level of insulin in clozapine groups decreased significantly [(0.92±0.4), (1.02±0.3),(1.06±0.4), (0.74±0.2), (1.66±0.4) mU/IEQ, P ＜ 0.05]. There was no significant difference in the volume of insulin among the four clozapine groups with different concentrations (P ＞ 0.05). [2] At 16.7 mmol/L glucose, the level of insulin at the four concentrations of clozapine did not differ from that of control group either after 1 hour or 4 hours of incubation (P ＞ 0.05).CONCLUSION: Clozapine inhibits basal insulin secretion, but the effect is not correlated with its dosage.

Objective:To investigate the associations of brain white matter integrity deficits, and to explore the association of fractional anisotropy (FA) abnormality with clinical symptoms and cognitive impairments, as well as the prediction effect of the FA alterations on symptoms and cognitive function outcomes in the acute stage of schizophrenia from the whole brain level based on magnetic resonance diffusion tensor imaging (DTI).Methods:From November 2019 to December 2020, thirty-eight patients with first-episode schizophrenia and 38 healthy controls were recruited in this study. Wisconsin card classification test (WCST), digit span test (DST forward/backward), verbal fluency test, Stroop (A/B/C), trail making test (TMT-A/B), as well as positive and negative syndrome scale (PANSS) were utilized to evaluate patients' cognitive function and clinical symptoms both before and after 8 weeks of risperidone monotherapy. T1-weighted images and DTI data of all the subjects were collected . FSL and SPM8 were used to preprocess MRI data and compare the between-group differences of FA. Support vector machine (SVM) analysis was used to evaluate the accuracy of abnormal FA values in differentiating schizophrenic patients from healthy controls. Finally, stepwise multiple regression analysis or generalized linear models were used to explore the associations between white matter integrity and symptoms or cognition.Results:Before treatment, patients' FA values of right medial temporal lobe (mTL), cuneus, anterior cingulate gyrus (ACG) and inferior parietal lobe (IPL) were significantly lower than those in healthy controls ( P<0.01, GRF corrected), and patients' FA values of bilateral middle cingulate gyrus (mCG) were significantly higher than those in the control group ( P<0.01, GRF corrected). SVM analysis showed that four combinations could distinguish the patients from the control with the most accurate rate of 89.47%. Patients' baseline decreased FA values in the right IPL were positively associated with their increased total response time in WCST ( β=0.489, P=0.003, FDR corrected), correct response time in WCST ( β=0.450, P=0.008, FDR corrected), error response time in WCST ( β=0.435, P=0.008, FDR corrected), TMT-B ( β=0.296, P=0.042, FDR corrected), Stroop-C ( β=0.345, P=0.035, FDR corrected), and PANSS-P ( β=0.321, P=0.042, FDR corrected). Reduced FA values in the right mTL in patient group were significantly negatively related to the total time spent on the TMT-A ( β=-0.425, P=0.009, FDR corrected) and TMT-B ( β=-0.325, P=0.026 with FDR correction). Increased FA values in right mCG in patient group demonstrated positive associations with total response time in the WCST ( β=0.585, P=0.002, FDR corrected), correct response time in WCST ( β=0.524, P=0.003, FDR corrected), error response time in WCST ( β=0.536, P=0.003, FDR corrected) and total time consuming in TMT-B ( β=0.484, P=0.004, FDR corrected), as well as negative associations with DST-forward ( β=-0.319, P=0.042, FDR corrected). After treatment, patients' percentage changes in total response time of WCST ( β=0.715, P<0.001, FDR corrected), correct response time of WCST ( β=0.752, P<0.001, FDR corrected), as well as total time-consuming of TMT-A ( β=1.333, P=0.001, FDR corrected) showed positive correlations with baseline increased FA values in the left mCG. Percentage alteration of Stroop-B was negatively correlated with baseline FA values in the right cuneus ( β=-0.745, P=0.015, FDR corrected). Conclusions:The combination of abnormal FA values in multiple brain regions may be potential biomarkers to distinguish schizophrenic patients from healthy volunteers. There was regional dependence in the associations of the impairment of white matter integrity with the cognitive impairment, the severity of psychopathological symptoms as well as the prognosis of patients in the acute stage.

Objective:To evaluate the diagnostic accuracy of the convolutional neural network（CNN） in diagnosing nasopharyngeal carcinoma using endoscopic narrowband imaging. Methods:A total of 834 cases with nasopharyngeal lesions were collected from the People's Hospital of Guangxi Zhuang Autonomous Region between 2014 and 2016. We trained the DenseNet201 model to classify the endoscopic images, evaluated its performance using the test dataset, and compared the results with those of two independent endoscopic experts. Results:The area under the ROC curve of the CNN in diagnosing nasopharyngeal carcinoma was 0.98. The sensitivity and specificity of the CNN were 91.90% and 94.69%, respectively. The sensitivity of the two expert-based assessment was 92.08% and 91.06%, respectively, and the specificity was 95.58% and 92.79%, respectively. There was no significant difference between the diagnostic accuracy of CNN and the expert-based assessment （P=0.282, P=0.085）. Moreover, there was no significant difference in the accuracy in discriminating early-stage and late-stage nasopharyngeal carcinoma（P=0.382）. The CNN model could rapidly distinguish nasopharyngeal carcinoma from benign lesions, with an image recognition time of 0.1 s/piece. Conclusion:The CNN model can quickly distinguish nasopharyngeal carcinoma from benign nasopharyngeal lesions, which can aid endoscopists in diagnosing nasopharyngeal lesions and reduce the rate of nasopharyngeal biopsy.
Humans ; Nasopharyngeal Carcinoma ; Narrow Band Imaging ; China ; Neural Networks, Computer ; Nasopharyngeal Neoplasms/diagnostic imaging*