AIM:To observe the effects of the combination of berberin and epirubicin on the cell cycle of T24 bladder cancer cells and the underlying mechanisms.METHODS:The cancer cells were exposed to epirubicin in the presence or absence of different concentrations of berberin.The viability of the cancer cells was determined by MTT assay.The cell cycle distribution was detected by flow cytometry, and the protein levels of cyclin D1, CDK2, CDK4, P21 and P27 were detected by Western blot.RESULTS:Berberine markedly enhanced the inhibitory effect of epirubicin on the viability of T24 cells and promoted epirubicin-induced cell cycle arrest at G0/G1 phase as compared with the negative control cells.Epirubicin increased the protein expression of P27 and P21, both of which were enhanced by treatment with berberin.In contrast, berberin exposure further decreased the protein expression of cyclin D1, CDK2 and CDK4 in epirubicin-treated T24 cells.CONCLUSION:Berberine significantly promotes epirubicin-induced G0 /G1 phase arrest in human bladder cancer cells by up-regulating P27 and P21 expression and inhibiting the expression of cyclin D1, CDK2 and CDK4.

Objective To explore the risk factors affecting T-tube sinus tract formation after common bile duct exploration and T-tube drainage by spiral computed tomography (SCT)examination.Methods The clinical data of 465 patients undergoing common bile duct exploration and T-tube drainage at the Affiliated Hospital of Youjiang Medical College for Nationalities from May 2011 to December 2013 were retrospectively analyzed.The residual stones and biliary stricture were detected by T-tube cholangiography,and the T-tube sinus tract formation in all the patients was detected by SCT examination at postoperative week 2.The factors affecting sinus tract formation were analyzed,including gender,age,albumin (Alb),C-reactive protein,alanine transaminase (ALT),total bilirubin (TBil),hemoglobin (Hb),surgical method,effusion around T tube,reoperation,diabetes.Univariate analysis was done using the chi-square test.Multivariate analysis was done using the Logistic regression.Results T-tubes of 465 patients were clear without residual stones.T-tube in the 397 patients was removed when the sinus tract formation was confirmed by CT examination at postoperative week 2.T-tubes in other patients were removed when the sinus tract formation was detected by CT reexamination at postoperative week 4.In univariate analysis,Alb,surgery method,effusion around T-tube and diabetes were important factors affecting T-tube sinus tract formation (x2 =50.750,7.671,19.022,15.373,P ＜ 0.05).Alb ＜ 30 g/L,laparoscopic surgery,effusion around T-tube and diabetes were independent risk factors affecting T-tube sinus tract formation in multivariate analysis [Odds ratio =1.135,0.493,0.262,0.363; 95％ confidence interval:1.061-1.214,0.280-0.865,0.104-0.658,0.156-0.843,P ＜ 0.05].Conclusions The T-tube removal is determined according to the sinus tract formation by CT examination at week 2 after common bile duct exploration and T-tube drainage.Alb ＜ 30 g/L,laparoscopic surgery,effusion around T-tube and diabetes are independent risk factors affecting T-tube sinus tract formation.

Neuroendocrine prostate cancer(NEPC) is a prostate cancer subtype with a very high degree of malignancy and a special molecular phenotype.NEPC is not sensitive to endocrine therapy, and there are currently no specific drugs, so there is a lack of effective clinical treatment.New advances in NEPC therapeutic include chemical therapy, targeted drug therapy based on molecular phenotype and other non-targeted drug therapy. This article summarizes the current treatment methods, pharmaceutical, and clinical research results for NEPC, aiming to deepen clinicians' more comprehensive understanding of NEPC patients' treatment strategies.

Objective:To explore the application value of transrectal ultrasound images classification network model of prostate cancer based on deep learning in the classification of benign and malignant prostate tissue in transrectal ultrasound images.Methods:A total of 1 462 two-dimensional images of transrectal prostate biopsy with clear pathologic results(including 658 images of malignant tumor, 804 images of benign tumor) from 203 patients with suspicious prostate cancer(including 89 cases of malignant tumor, 114 cases of benign tumor) were collected from May 2018 to May 2021 in the First Affiliated Hospital of Jinan University. They were divided into the training database, validation database, and test database. And the training and validation database were used to train and obtain the intelligence-assisted diagnosis network model, and then the test database was used to test the network model and two ultrasound doctors of different ages. With pathologic diagnosis as the gold standard, the diagnostic performance among them was evaluated.Results:①The sensitivity of network model was 66.7% the specificity was 91.9%, the accuracy was 80.5%, the precision(positive predictive value) was 87.1%. The area under the ROC curve was 0.922. ②The accuracy of the junior and senior ultrasound doctors was 57.5%, 62.0%; the specificity was 62.0%, 66.3%; the sensitivity was 51.5%, 56.8%; the precision was 53.1%, 58.1%, respectively. ③The accuracy, sensitivity, specificity, precision of classification: the network model > the ultrasound doctors, the differences were significant( P<0.05); the senior ultrasound doctor>the junior ultrasound doctor, the differences were not significant( P>0.05). Conclusions:The intelligence-assisted diagnosis network model based on deep learning can classify benign and malignant prostate tissue in transrectal ultrasound images, improve the accuracy of ultrasound doctors in diagnosing prostate cancer. It is of great significance to improve the efficiency of screening for patients with high clinical suspicion of prostate cancer.