Objective To investigate the expression of CD44v3 and CD44v6 in the polymorphic adenoma of salivary glands. Methods Using immunohistochemistry technology to detect the expression of CD44v3 and CD44v6 in 30 cases of polymorphic adenoma of salivary glands. Results The expression of CD44v3 was positive in 66.7 %cases within 30 paraffin-embedded sections, while CD44v6,86.7 % was positive. Conclusion The high expression of CD44v3 and CD44v6 maybe the reason of the low invasive ability and migration of polymorphic adenoma of salivary glands.

Objective To evaluate the long-term efficacy of transjugular intrahepatic portosystemic shunt(TIPS)with bare stents and Fluency covered stents in the treatment of portal hypertension,and to discuss its clinical value.Methods The clinical data of 29 patients with intractable ascites or esophagogastric fundus varices rupture and hemorrhage caused by cirrhotic portal hypertension,who received TIPS with bare stents and covered stents at the First Affiliated Hospital of Xinjiang Medical University of China(25 patients)and the Lishui Municipal Central Hospital of China(4 patients)between August 2012 and December 2017,were retrospectively analyzed.The patients were regularly followed up to check the survival status.The postoperative cumulative shunt patency rate and cumulative survival rate of the patients were analyzed by Kaplan-Meier method.Results The technical success rate of TIPS was 100％.The mean portal vein pressure was decreased from preoperative(40.21±3.24)cmH2O to postoperative(24.55±3.55)cmH2O(P＜0.05).The patients were followed up for 5.1-10.5 years.The postoperative 1-,3-,5-,7-year primary cumulative patency rates of the shunt were 89.7％,75.9％,75.9％ and 52.5％,respectively.The postoperative 5-,7-,9-and 10-year cumulative survival rates were 100％,66.9％,66.9％ and 33.4％,respectively.The incidence of hepatic encephalopathy was 13.8％(4/29).Conclusion Using bare stents combined with Fluency covered stents for TIPS is clinically safe and effective in the treatment of portal hypertension.This technique carries higher long-term shunt patency rate and low incidence of hepatic encephalopathy.Therefore,it can be used as a substitute for Viatorr stent when necessary.(J Intervent Radiol,2024,33:295-299)

The clinical manifestations of patients with schizophrenia and patients with depression not only have a certain similarity, but also change with the patient's mood, and thus lead to misdiagnosis in clinical diagnosis. Electroencephalogram (EEG) analysis provides an important reference and objective basis for accurate differentiation and diagnosis between patients with schizophrenia and patients with depression. In order to solve the problem of misdiagnosis between patients with schizophrenia and patients with depression, and to improve the accuracy of the classification and diagnosis of these two diseases, in this study we extracted the resting-state EEG features from 100 patients with depression and 100 patients with schizophrenia, including information entropy, sample entropy and approximate entropy, statistical properties feature and relative power spectral density (rPSD) of each EEG rhythm (δ, θ, α, β). Then feature vectors were formed to classify these two types of patients using the support vector machine (SVM) and the naive Bayes (NB) classifier. Experimental results indicate that: ① The rPSD feature vector performs the best in classification, achieving an average accuracy of 84.2% and a highest accuracy of 86.3%; ② The accuracy of SVM is obviously better than that of NB; ③ For the rPSD of each rhythm, the β rhythm performs the best with the highest accuracy of 76%; ④ Electrodes with large feature weight are mainly concentrated in the frontal lobe and parietal lobe. The results of this study indicate that the rPSD feature vector in conjunction with SVM can effectively distinguish depression and schizophrenia, and can also play an auxiliary role in the relevant clinical diagnosis.
Bayes Theorem ; Depression ; Electroencephalography ; Humans ; Schizophrenia ; Signal Processing, Computer-Assisted ; Support Vector Machine