Objective To evaluate the indication,operation pattern and therapeutic effect of ano-saving surgery for lower rectal carcinoma. Methods Retrospective analysis on the clinical feature of 320 patients with lower rectal carcinoma (postoperative time ≥5 years)treated by ano-saving surgery, the 5-year survival rate, local recurrence rate, and mortality were compared in the various operations. Results The success performed rate of ano-saving operation for lower rectal cancer was 58.5%(320/547).Among them, anastomotic leakage after surgery occurred in 4 cases (1.25%), and 26 cases had anastomostic narrowness (8.13%) within 1 year after surgery.The defecation function after surgery, in patients received colonic J pouch or transverse coloplasty pouch was much better than that in patients received coloanal or colorectal anastomosis. 5-year survival rates, and anastomostic recurrence rates were as follows:In ultra-low anastomosis were 63.24% and 10.27%. Park′s operation 66.67% and 5.13%, local resection 89.46% and 10.71%, respectively. 5-year recurrence rate in the pelvic soft tissue was 3.44%(11/320).Two cases died after operation. Conclusions Lower or ultra-low colon-rectum anastomosis becomes the main operative pattern in preserving anal sphincter in lower rectal cancer.Local resection of lower rectal tumor might be considered if the indecation is selected strictly. Colonic-J-pouch or transverse coloplasty pouch is good for improving the defecation function after ano-saving surgery for lower rectal cancer.

Objective To investigate the principles of treatment for severe acute pancreatitis(SAP). Methods A retrospective analysis of the data of 217 cases of SAP with regards to clinical features,mortality rate and conversion to surgical operation.In this group,66 cases received early operation and 24 cases were converted to operation after initial conservative treatment.Results The overall cure rate was 80.2%(174/217). Among them,90 cases underwent operative treatment,with a cure rate of 72.2%(65/90),and 127 cases underwent conservative treatment with a cure rate of 85.8%(109/127).Conclusions The initial treatment of SAP should be conservative. Operation should be performed if there is a specific indication for early surgical intervention,or for conversion to operation.

Objective To evaluate the effects of sitagliptin on blood glucose,blood pressure,blood lip and carotid artery intima media thickness (IMT) in metabolic syndrome patients with type 2 diabetes.Methods The clinical data were collected for 64 cases of inpatient and outpatient patients with metabolic syndrome with type 2 diabetes.Those patients included anti-diabetes native patients and patients only used the stable metformin dose.After signed off the informed consent form,those patients were randomized to the sitagliptin treatment group or original treatment group,and the metabolic index and carotid artery intima-media thickness were evaluated after 24 weeks treatment.Results The body mass index (BMI),waist circumference (WC),fasting plasma glucose (FPG),triglycerides (TG),high density lipoprotein cholesterol (HDL-C),systolic blood pressure (SBP),diastolic blood pressure (DBP),glycated hemoglobin a1c (HbA1c),and carotid artery IMT in two groups were comparable at baseline.After 12 weeks treatment,the FPG,TG,DBP,and HbA1c in the sitagliptin group were significantly better than original treatment group and the baseline,while there was no different between two groups in other index.After 24 weeks treatment,the FPG,TG,HDL-C,DBP,HbA1c,and carotid artery IMT in the sitagliptin group were significantly better than original treatment group and the baseline.Conclusions Sitagliptin presents the functions of lowering blood pressure,adjusting blood lipid,and protecting vascular endothelial in addition to lowering blood glucose.

Objective To compare the curative effect of Epalrestat and mecobalamine .Methods Epalrestat to treat 48 pa-tionts in DPN and mecobalamine to treat 42,measuring blood sugar ,blood pressure, blood fat and body mass index (BMI) prior and post treatment ,and measuring the MCV and SCV of nervus medianus ,nervus peronaeus connunis and nervus tibialis with EMG .Re-sults The symptom of the two sets have all been improved after the treatment ,and the effective power of Epalrestat and mecobalamine is 92.7% and 80.5% respectively.mean while there is improvement in MCV and SCV of nervus medianus ,nervus peronaeus connunis and nervus tibialis,and is more obvious in the set of Epalrestat ( P <0.01).In the whole process of the treat of the two sets ,no one appear to have adverse reactions .Conclusions Epalrestat has significant curative effect with less adverse reactions , and deserves to be spreaded in clinic.

Objective:To construct a deep learning-based diagnostic system for gastrointestinal submucosal tumor (SMT) under endoscopic ultrasonography (EUS), so as to help endoscopists diagnose SMT.Methods:From January 1, 2019 to December 15, 2021, at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University, 245 patients with SMT confirmed by pathological diagnosis who underwent EUS and endoscopic submucosal dissection were enrolled. A total of 3 400 EUS images were collected. Among the images, 2 722 EUS images were used for training of lesion segmentation model, while 2 209 EUS images were used for training of stromal tumor and leiomyoma classification model; 283 and 191 images were selected as independent test sets to evaluate lesion segmentation model and classification model, respectively. Thirty images were selected as an independent data set for human-machine competition to compare the lesion classification accuracy between lesion classification models and 6 endoscopists. The performance of the segmentation model was evaluated by indexes such as Intersection-over-Union and Dice coefficient. The performance of the classification model was evaluated by accuracy. Chi-square test was used for statistical analysis.Results:The average Intersection-over-Union and Dice coefficient of lesion segmentation model were 0.754 and 0.835, respectively, and the accuracy, recall and F1 score were 95.2%, 98.9% and 97.0%, respectively. Based on the lesion segmentation, the accuracy of classification model increased from 70.2% to 92.1%. The results of human-machine competition showed that the accuracy of classification model in differential diagnosis of stromal tumor and leiomyoma was 86.7% (26/30), which was superior to that of 4 out of the 6 endoscopists(56.7%, 17/30; 56.7%, 17/30; 53.3%, 16/30; 60.0%, 18/30; respectively), and the differences were statistically significant( χ2=7.11, 7.36, 8.10, 6.13; all P<0.05). There was no significant difference between the accuracy of the other 2 endoscopists(76.7%, 23/30; 73.3%, 22/30; respectively) and model(both P<0.05). Conclusion:This system could be used for the auxiliary diagnosis of SMT under ultrasonic endoscope in the future, and to provide a powerful evidence for the selection of subsequent treatment decisions.

Objective:To evaluate deep learning for differentiating invasion depth of colorectal adenomas under image enhanced endoscopy (IEE).Methods:A total of 13 246 IEE images from 3 714 lesions acquired from November 2016 to June 2021 were retrospectively collected in Renmin Hospital of Wuhan University, Shenzhen Hospital of Southern Medical University and the First Hospital of Yichang to construct a deep learning model to differentiate submucosal deep invasion and non-submucosal deep invasion lesions of colorectal adenomas. The performance of the deep learning model was validated in an independent test and an external test. The full test was used to compare the diagnostic performance between 5 endoscopists and the deep learning model. A total of 35 videos were collected from January to June 2021 in Renmin Hospital of Wuhan University to validate the diagnostic performance of the endoscopists with the assistance of deep learning model.Results:The accuracy and Youden index of the deep learning model in image test set were 93.08% (821/882) and 0.86, which were better than those of endoscopists [the highest were 91.72% (809/882) and 0.78]. In video test set, the accuracy and Youden index of the model were 97.14% (34/35) and 0.94. With the assistance of the model, the accuracy of endoscopists was significantly improved [the highest was 97.14% (34/35)].Conclusion:The deep learning model obtained in this study could identify submucosal lesions with deep invasion accurately for colorectal adenomas, and could improve the diagnostic accuracy of endoscopists.

Objective:To explore the effectiveness of the artificial intelligence-endoscopic ultrasound (AI-EUS) biliary and pancreatic recognition system in assisting the recognition of EUS images.Methods:Subjects who received EUS due to suspicious biliary and pancreatic diseases from December 2019 to August 2020 were prospectively collected from the database of Department of Gastroenterology, Renmin Hospital of Wuhan University. Pancreatic EUS images of 28 subjects were included for recognition of pancreas standard station. EUS images of bile duct of 29 subjects were included for recognition of bile duct standard station. Eight new endoscopists from the Gastroenterology Department of Renmin Hospital of Wuhan University read the 57 EUS videos with and without the assistance of AI-EUS biliary and pancreatic recognition system. Accuracy of endoscopists' identification of biliary and pancreatic standard sites with and without the assistance of AI-EUS was compared.Results:The accuracy of pancreas standard station identification of the new endoscopists increased from 67.2% (903/1 344) to 78.4% (1 054/1 344) with the assistance of AI-EUS. The accuracy of bile duct standard station identification increased from 56.4% (523/928) to 73.8% (685/928).Conclusion:AI-EUS biliary and pancreatic recognition system can improve the accuracy of EUS images recognition of biliary and pancreatic system, which can assist diagnosis in clinical work.

Objective:To develop an endoscopic ultrasonography (EUS) station recognition and pancreatic segmentation system based on deep learning and to validate its efficacy.Methods:Data of 269 EUS procedures were retrospectively collected from Renmin Hospital of Wuhan University between December 2016 and December 2019, and were divided into 3 datasets: (1)Dataset A of 205 procedures for model training containing 16 305 images for classification training and 1 953 images for segmentation training; (2)Dataset B of 44 procedures for model testing containing 1 606 images for classification testing and 480 images for segmentation testing; (3) Dataset C of 20 procedures with 150 images for comparing the performance between models and endoscopists. EUS experts (with more than 10 years of experience) A and B classified and labeled all images of dataset A, B and C through discussion, and the results were used as the gold standard. EUS expert C and senior EUS endoscopists (with more than 5 years of experience) D and E classified and labeled the images in dataset C, and the results were used for comparison with model. The main outcomes included accuracy of classification, Dice (F1 score) of segmentation and Cohen Kappa coefficient of consistency analysis.Results:In test dataset B, the model achieved a mean accuracy of 94.1% in classification. The mean Dice of pancreatic and vascular segmentation were 0.826 and 0.841 respectively. In dataset C, the classification accuracy of the model reached 90.0%. The classification accuracy of expert C, senior endoscopist D and E were 89.3%, 88.7% and 87.3%, respectively. The Dice of pancreatic and vascular segmentation in the model were 0.740 and 0.859, 0.708 and 0.778 for expert C, 0.747 and 0.875 for senior endoscopist D, and 0.774 and 0.789 for senior endoscopist E. The model was comparable to the expert level.Consistency analysis showed that there was high consistency between the model and endoscopists (the Kappa coefficient was 0.823 between model and expert C, 0.840 between model and senior endoscopist D, and 0.799 between model and senior endoscopist E).Conclusion:EUS station classification and pancreatic segmentation system based on deep learning can be used for quality control of pancreatic EUS, with a comparable performance of classification and segmentation to that of EUS experts.