Objective To study the expression of regulator of cullins-1(ROC1)and ubiquitin binding enzyme 9(UBC9)in non-muscle invasive bladder cancer(NMIBC)and their relationship with clinicopathological features and prognostic value of transurethral resection of bladder tumor(TURBT).Methods Retrospective analysis was conducted on 104 patients with NMIBC who underwent TURBT at Northwest University First Hospital from April 2018 to February 2021.Immunohistochemistry was used to detect the expression of ROC1 and UBC9 in tissues.Follow-up was conducted for 3 years,and Kaplan-Meier curve analysis and COX regression analysis were used to identify factors affecting the prognosis of NMIBC patients.Results The positive rates of ROC1(67.31%)and UBC9(69.23%)in NMIBC cancer tissues were higher than those in adjacent tissues(9.62%,7.69%),and the differences were statistically significant(χ2=73.125,83.200,all P<0.001).There was a positive correlation between the expression of ROC1 and UBC9 in NMIBC cancer(r=0.719,P<0.001).The positivity rates of ROC1(87.23%,90.00%)and UBC9(89.36%,88.33%)in cancer tissues with a maximum tumor diameter of≥2cm,T1 stage were higher than those in cancer tissues with a maximum tumor diameter of<2cm(50.88%,52.63%),Ta/Tis stage(36.36%,43.18%),and the difference were statistically significant(χ2=15.474～33.188,all P<0.05).After TURBT surgery,there were 29 cases of local recurrence,10 cases of metastasis,and 2 deaths of bladder cancer,the 3-year progression-free survival(PFS)rate was 60.58%(63/104).The 3-year PFS of NMIBC patients in the ROC1 positive and negative groups was 47.14%(33/70)and 88.24%(30/34),and the 3-year PFS of patients in the UBC9 positive and negative groups was 47.22%(34/72)and 90.63%(29/32),the differences were statistically significant(Log-rank χ2=15.341,15.931,all P<0.001).Multivariate COX regression analysis showed that ROC1 positivity,UBC9 positivity,maximum tumor diameter≥2cm and T1 phase were risk factors affecting the prognosis of NMIBC patients(all P<0.001).Conclusion The expression of ROC1 and UBC9 is elevated in NMIBC,which is related to the maximum diameter and stage of the tumor.Combined detection of ROC1 and UBC9 can help evaluate the prognosis of NMIBC patients.

Objective:To develop a deep learning-based system for real-time recognition and classification of portal hypertensive gastropathy (PHG) and evaluate its ability to assist junior endoscopists.Methods:A total of 2 848 gastroscopy images from 832 patients with liver cirrhosis were selected from Digestive Endoscopy Center databases of Renmin Hospital of Wuhan University, Wuhan Hospital of Traditional Chinese and Western Medicine, and the Second Hospital of Jingzhou from January 2015 to October 2023. This system referred to 3 endoscopic features of Baveno Ⅱ scoring system. Three models were developed respectively for gastric antral vascular ectasia (GAVE), mosaic-like pattern (MLP), and red marks (RM). The specific classification references were as follows: (1) GAVE model: 0 no, 1 yes; (2) MLP model: 0 no, 1 mild, 2 severe; (3) RM model: 0 no, 1 isolated, 2 fused. The classification results for endoscopic characteristics of PHG of 3 endoscopy experts were taken as the gold standard. The yolov8-m model was used for training. The training dataset, validation dataset, and test dataset were allocated at a ratio of 8∶1∶1. The test dataset was used to evaluate the performance of models and their auxiliary effects on endoscopists. The accuracy, recall, precision, specificity and Kappa coefficient were calculated. Results:The accuracy, recall, specificity of GAVE model were 96.0% (48/50), 87.5% (7/8) and 97.6% (41/42). There was no significant difference between its accuracy and the gold standard ( χ2=316.226, P=1.000). The precision of GAVE1 and GAVE0 were 87.5% (7/8) and 97.6% (41/42) respectively. The accuracy of MLP model was 84.1% (132/157), and there was no significant difference compared with the gold standard ( χ2=3.286, P=0.193). The precision and recall of MLP2 were 88.2% (15/17) and 75.0% (15/20). The precision and recall of MLP1 were 77.9% (60/77) and 88.2% (60/68). The precision and recall of MLP0 were 90.5% (57/63) and 82.6% (57/69). The accuracy of RM model was 87.9% (123/140), and there was no significant difference compared with the gold standard ( χ2=2.891, P=0.409). The precision and recall of RM2 were 94.7% (18/19) and 78.3% (18/23). The precision and recall of RM1 were 72.2% (26/36) and 81.3% (26/32). The precision and recall of RM0 were 92.9% (79/85) and 92.9% (79/85). The mean accuracy of the three junior endoscopists, with and without the assistance of the GAVE model, MLP model, and RM model, respectively increased from 95.3% to 99.3%, from 83.9% to 91.9%, and from 81.9% to 83.1%. The overall consistency analysis of the 3 junior endoscopists with the gold standard indicated that the consistency of the GAVE model before and after assistance was extremely strong (both an overall Kappa of 1.000); the consistency before assistance of the MLP model was moderate (with an overall Kappa of 0.601), which increased to extremely strong after assistance (with an overall Kappa of 0.964); and the consistency of the RM model before and after assistance was also relatively strong (with an overall Kappa of 0.792 before and 0.798 after). Conclusion:The deep learning system accurately identifies and classifies PHG features and significantly enhances diagnostic performance of junior endoscopists.

Objective To study the expression of regulator of cullins-1(ROC1)and ubiquitin binding enzyme 9(UBC9)in non-muscle invasive bladder cancer(NMIBC)and their relationship with clinicopathological features and prognostic value of transurethral resection of bladder tumor(TURBT).Methods Retrospective analysis was conducted on 104 patients with NMIBC who underwent TURBT at Northwest University First Hospital from April 2018 to February 2021.Immunohistochemistry was used to detect the expression of ROC1 and UBC9 in tissues.Follow-up was conducted for 3 years,and Kaplan-Meier curve analysis and COX regression analysis were used to identify factors affecting the prognosis of NMIBC patients.Results The positive rates of ROC1(67.31%)and UBC9(69.23%)in NMIBC cancer tissues were higher than those in adjacent tissues(9.62%,7.69%),and the differences were statistically significant(χ2=73.125,83.200,all P<0.001).There was a positive correlation between the expression of ROC1 and UBC9 in NMIBC cancer(r=0.719,P<0.001).The positivity rates of ROC1(87.23%,90.00%)and UBC9(89.36%,88.33%)in cancer tissues with a maximum tumor diameter of≥2cm,T1 stage were higher than those in cancer tissues with a maximum tumor diameter of<2cm(50.88%,52.63%),Ta/Tis stage(36.36%,43.18%),and the difference were statistically significant(χ2=15.474～33.188,all P<0.05).After TURBT surgery,there were 29 cases of local recurrence,10 cases of metastasis,and 2 deaths of bladder cancer,the 3-year progression-free survival(PFS)rate was 60.58%(63/104).The 3-year PFS of NMIBC patients in the ROC1 positive and negative groups was 47.14%(33/70)and 88.24%(30/34),and the 3-year PFS of patients in the UBC9 positive and negative groups was 47.22%(34/72)and 90.63%(29/32),the differences were statistically significant(Log-rank χ2=15.341,15.931,all P<0.001).Multivariate COX regression analysis showed that ROC1 positivity,UBC9 positivity,maximum tumor diameter≥2cm and T1 phase were risk factors affecting the prognosis of NMIBC patients(all P<0.001).Conclusion The expression of ROC1 and UBC9 is elevated in NMIBC,which is related to the maximum diameter and stage of the tumor.Combined detection of ROC1 and UBC9 can help evaluate the prognosis of NMIBC patients.

Objective:To develop a deep learning-based system for real-time recognition and classification of portal hypertensive gastropathy (PHG) and evaluate its ability to assist junior endoscopists.Methods:A total of 2 848 gastroscopy images from 832 patients with liver cirrhosis were selected from Digestive Endoscopy Center databases of Renmin Hospital of Wuhan University, Wuhan Hospital of Traditional Chinese and Western Medicine, and the Second Hospital of Jingzhou from January 2015 to October 2023. This system referred to 3 endoscopic features of Baveno Ⅱ scoring system. Three models were developed respectively for gastric antral vascular ectasia (GAVE), mosaic-like pattern (MLP), and red marks (RM). The specific classification references were as follows: (1) GAVE model: 0 no, 1 yes; (2) MLP model: 0 no, 1 mild, 2 severe; (3) RM model: 0 no, 1 isolated, 2 fused. The classification results for endoscopic characteristics of PHG of 3 endoscopy experts were taken as the gold standard. The yolov8-m model was used for training. The training dataset, validation dataset, and test dataset were allocated at a ratio of 8∶1∶1. The test dataset was used to evaluate the performance of models and their auxiliary effects on endoscopists. The accuracy, recall, precision, specificity and Kappa coefficient were calculated. Results:The accuracy, recall, specificity of GAVE model were 96.0% (48/50), 87.5% (7/8) and 97.6% (41/42). There was no significant difference between its accuracy and the gold standard ( χ2=316.226, P=1.000). The precision of GAVE1 and GAVE0 were 87.5% (7/8) and 97.6% (41/42) respectively. The accuracy of MLP model was 84.1% (132/157), and there was no significant difference compared with the gold standard ( χ2=3.286, P=0.193). The precision and recall of MLP2 were 88.2% (15/17) and 75.0% (15/20). The precision and recall of MLP1 were 77.9% (60/77) and 88.2% (60/68). The precision and recall of MLP0 were 90.5% (57/63) and 82.6% (57/69). The accuracy of RM model was 87.9% (123/140), and there was no significant difference compared with the gold standard ( χ2=2.891, P=0.409). The precision and recall of RM2 were 94.7% (18/19) and 78.3% (18/23). The precision and recall of RM1 were 72.2% (26/36) and 81.3% (26/32). The precision and recall of RM0 were 92.9% (79/85) and 92.9% (79/85). The mean accuracy of the three junior endoscopists, with and without the assistance of the GAVE model, MLP model, and RM model, respectively increased from 95.3% to 99.3%, from 83.9% to 91.9%, and from 81.9% to 83.1%. The overall consistency analysis of the 3 junior endoscopists with the gold standard indicated that the consistency of the GAVE model before and after assistance was extremely strong (both an overall Kappa of 1.000); the consistency before assistance of the MLP model was moderate (with an overall Kappa of 0.601), which increased to extremely strong after assistance (with an overall Kappa of 0.964); and the consistency of the RM model before and after assistance was also relatively strong (with an overall Kappa of 0.792 before and 0.798 after). Conclusion:The deep learning system accurately identifies and classifies PHG features and significantly enhances diagnostic performance of junior endoscopists.