Objective To investigate the effect and mechanism of miglitol on regulating the energy metabolism of brown adipocytes by activating UCP1 and preventing cold injury in mice after cold exposure. Methods Primary brown adipocytes were induced into mature adipocytes, the effect of miglitol on the viability of brown adipocytes was investigated by MTT method, the lipid droplet consumption level of cells after drug administration was investigated by Oil Red O staining technology, and the level of UCP1, a key protein of thermogenesis in brown adipocytes, was detected by Western blotting. The activity of anti-frostbite was investigated in cold exposure at 4 ℃ and −20 ℃. KM mice, which were randomly divided into control group, cold exposure group, miglitol group and all-trans retinoic acid group, and after 7 days of repeated administration, the body surface temperature of mice was detected by infrared thermal imaging system, the anal temperature change was detected by anal thermometer, and the expression levels of UCP1 and PGC1-α in adipose tissue were detected by immunoblotting. Results Compared with the control group, the lipid droplet consumption and UCP1 expression levels in brown adipocytes in the miglitol group were significantly increased. The levels of body surface temperature and rectal temperature increased significantly after cold exposure, and the levels of UCP1 and PGC1α in the brown adipose tissue of mice increased significantly, which indicated that the miglitol could activate the critical proteins UCP1 and PGC1α of the thermogenesis pathway, increase the thermogenesis of mice after cold exposure, and thus improve the effect of cold injury for toe swelling. Conclusion Miglitol could play a role in improving cold injury and body temperature in mice by increasing the level of UCP1 and PGC1α, which are key targets of the thermogenesis pathway to promote the thermogenesis of brown fat.

Objective To screen the prescription,preparation,quality evaluation of Ganoderma lucidum extract nano-emulsion(GLEN)and study its anti-skin aging effect.Methods The emulsifier and oil phase of the nano-emulsion were preliminarily screened by solubility method.The pseudo-ternary phase diagram method was used to screen the nano-emulsions,and the prepared GLEN were evaluated for quality in terms of the appearance,particle size,structure,drug loading,stability and skin safety.A mouse skin aging model was constructed by injecting D-galactose combined with ultraviolet radiation.GLEN was applied to the skin at the modeling site and investigated for their anti-skin aging efficacy.Results The optimal GLEN prescription was 30%polyoxymethylene castor oil,14.4%glycerol,11.1%medium chain triglycerides,and 44.5%water.GLEN was a dark brown and transparent liquid at room temperature,O/W type nano-emulsion with an average particle size(32.20±2.89)nm,polydispersity coefficient 0.38±0.03,zeta potential value(-4.05±0.27)mV.The content of Ganoderma lucidum polysaccharides was(36.35±0.55)mg·mL-1,and the content of Ganoderma lucidum triterpenes was(5.99±0.46)mg·mL-1,which possessed good stability.Pharmacodynamic experiments showed compared with the model group,the skin scores of mice in the GLEN group significantly improved(P﹤0.05),the water content increased(P﹤0.01),and the contents of SOD,GSH-PX and Hyp increased(P﹤0.05),while the content of MDA was decreased significantly(P﹤0.01).HE,Masson and Sirius crimson staining further indicated that the pathological changes in mice were alleviated to varying degrees in GLEN group.And GLEN had high safety for skin,could be applied to human body.Conclusion In this study,the prepared GLENs were homogeneous in appearance,stable in nature,and exhibited significant anti-aging effects on mouse skin.

Objective To screen the prescription,preparation,quality evaluation of Ganoderma lucidum extract nano-emulsion(GLEN)and study its anti-skin aging effect.Methods The emulsifier and oil phase of the nano-emulsion were preliminarily screened by solubility method.The pseudo-ternary phase diagram method was used to screen the nano-emulsions,and the prepared GLEN were evaluated for quality in terms of the appearance,particle size,structure,drug loading,stability and skin safety.A mouse skin aging model was constructed by injecting D-galactose combined with ultraviolet radiation.GLEN was applied to the skin at the modeling site and investigated for their anti-skin aging efficacy.Results The optimal GLEN prescription was 30%polyoxymethylene castor oil,14.4%glycerol,11.1%medium chain triglycerides,and 44.5%water.GLEN was a dark brown and transparent liquid at room temperature,O/W type nano-emulsion with an average particle size(32.20±2.89)nm,polydispersity coefficient 0.38±0.03,zeta potential value(-4.05±0.27)mV.The content of Ganoderma lucidum polysaccharides was(36.35±0.55)mg·mL-1,and the content of Ganoderma lucidum triterpenes was(5.99±0.46)mg·mL-1,which possessed good stability.Pharmacodynamic experiments showed compared with the model group,the skin scores of mice in the GLEN group significantly improved(P﹤0.05),the water content increased(P﹤0.01),and the contents of SOD,GSH-PX and Hyp increased(P﹤0.05),while the content of MDA was decreased significantly(P﹤0.01).HE,Masson and Sirius crimson staining further indicated that the pathological changes in mice were alleviated to varying degrees in GLEN group.And GLEN had high safety for skin,could be applied to human body.Conclusion In this study,the prepared GLENs were homogeneous in appearance,stable in nature,and exhibited significant anti-aging effects on mouse skin.

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 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 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.

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 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.