Objective To establish the model of pancreatoduodenal allotransplantation in pigs with enteric drainage (ED) and portal venous drainage (PVD). Methods Forty-six hybrid landraces were divided into two groups (donor and recipient groups) randomly, for pancreatoduodenal allotransplantation. Donors were perfused via abdomial aorta without clamping the portal venous outflow with UW solution after heparinization. Whole pancreatoduodenal graft was harvested with segments of abdomial aorta and portal vein and shaped under cold UW solution. Then, the end-to-end anastomosis was performed with the donor iliac artery bifurcation “Y” graft to the recipient superior mesenteric arteries and celiac artery. Furthermore, type Ⅰdiabete model was made by removal of the recipient pancreas. The venous anastomosis was reconstructed between the donor portal vein and the recipient superior mesenteric vein. Meanwhile, the end-to-side anastomosis was performed with the donor common iliac artery bifurcation “Y” graft to the recipient abdomial aorta and the side-to-side intestinal anastomosis was performed between the donor duodenum and the recipient jejunum. External jugular vein was intubated for transfusion. The levels of blood glucose, insulin and glucagon in blood were measured before and during the operation and 1, 3, 5, 7 d after operation. Results Twenty-three cases of pancreatoduodenal allotranplantations were performed on pigs. One died from complication of anesthesia. Success rate of operation was 95.7%.Complications of operation happened in 2 cases in which one was phlebothrombosis, incidence 4.5% and the other was duodenojejunal anastomotic leak, incidence 4.5%. The level of blood glucose increased within 30 min and recovered on the 2nd day after removal of pancreas. The levels of insulin and glucagon decreased within 30 min and recovered on the 2nd day after removal of pancreas. Rejection curred at the 1st day and reached the worst level on the 9th day after transplantation without the change of insulin and glucagon in blood and clinical symptoms of rejection. Conclusion Pancreatoduodenal transplantation in pigs can treat type Ⅰ diabete. ED and PVD can keep the function of endocrine in normal. The techni- que of duodenal transplantation with ED and PVD may pave the way for the further development of pancreas transplantation in clinic.

OBJECTIVE: To evaluate the quality of reports published in recent 10 years in China about quantitative analysis of syndrome differentiation for diabetes mellitus (DM) in order to explore the methodological problems in these reports and find possible solutions. METHODS: The main medical literature databases in China were searched. Thirty-one articles were included and evaluated by the principles of clinical epidemiology. RESULTS: There were many mistakes and deficiencies in these articles, such as clinical trial designs, diagnosis criteria for DM, standards of syndrome differentiation of DM, case inclusive and exclusive criteria, sample size and estimation, data comparability and statistical methods. CONCLUSION: It is necessary and important to improve the quality of reports concerning quantitative analysis of syndrome differentiation of DM in light of the principles of clinical epidemiology.

Objective To explore the feasibility and reasonable of low-dose scan on abdominal angiography in 64-slice spiral helical CT. Methods Phantom test: at 120 KV and from 200 mAs to 30 mAs at an interval of 10 mAs in each image acquisition, it was measured standard deviation (SD) of CT number, high contrast resolution and low contrast resolution, and then analyzed the relationship between the three parameters and the mAs values. Three mAs values were chosen to undertake clinical analysis.Clinical analysis: 90 randomly selected objects with abdominal angiography were divided into three groups,scanning with above three mAs values.Measurement of the SD value at the plain scan images was performed and the enhanced low-dose scan images were used post-processing with three-dimensional volume reconstruction (VR). The VR images were classified into three grades (excellent, moderate, bad) with the blind evaluation of three CT radiologists. The quality-correlation analysis was used between the standard deviation (SD) values of plain scan image and abdominal angiography VR image. Results According to the quality-correlation analysis between the standard deviation (SD) value of plain scan image and abdominal angiography VR image, the area under curve in receiver-operated characteristic (ROC) analysis was 0.921, 0.906 and 0.893 in each three group, respectively. Conclusions The low-dose scan of abdominal angiography is feasible. 80mAs can ensure better image quality. The enhanced scan probably can use 60mAs when the SD value is less than 5.78 in the plain scan; but when the SD value of plain scan is greater than 11.8, the enhanced scan is used best 100 mAs or higher.

Objective:To develop a deep learning model based on multimodal data for glaucoma diagnosis and severity assessment.Methods:A diagnostic test was conducted.A total of 145 normal eyes from 86 participants and 507 eyes with primary open-angle glaucoma from 314 participants were collected at the First Affiliated Hospital of Kunming Medical University from June to December in 2023.Fundus photographs and visual field data were obtained, and glaucoma eyes were divided into three groups based on the mean deviation value of the visual field, namely mild group (154 eyes), moderate group (113 eyes), and severe group (240 eyes).Three convolutional neural network (CNN) models, including DenseNet 121, ResNet 50 and VGG 19, were used to build an artificial intelligence (AI) model.The impact of single-modal and multimodal data on the classification results was evaluated, and the most appropriate CNN network architecture for multimodal data was identified.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of The First Affiliated Hospital of Kunming Medical University (No.2023L93).Written informed consent was obtained from each subject.Results:A total of 652 eyes had both fundus photographs and visual field test results.Images were randomly assigned to training and test datasets in a 4∶1 ratio by using computer random number method.AI models built with different CNN models showed high accuracy, with DenseNet 121 outperforming ResNet 50 and VGG 19 on various effectiveness measures.In the single-modal algorithm using fundus photographs, single-modal algorithm using visual field tests, and multimodal algorithm combining fundus photographs and visual field data, the area under the curve for early glaucoma detection was 0.87, 0.93 and 0.95, respectively.Conclusions:The use of multimodal data enables the development of a highly accurate tool for the glaucoma diagnosis and severity grading.