Objective To compare the effect of transplant islets between the subcutaneous inguinal white adipose tissues and renal capsule in the treatment of type 1 diabetes mellitus in mouse models. Methods The mice with type 1 diabetes mellitus undergoing islet transplantation were divided into the white adipose group (n=10) and renal capsule group (n=10). The islets were isolated, purified and transplanted to the subcutaneous white adipose tissues of inguinal region and renal capsule. The random blood glucose level and glucose tolerance function of the recipient mice in two groups were continuously monitored after operation. Islet grafts of the surviving recipient mice were harvested at postoperative 100 d for histopathological examination. Results In the white adipose group, the blood glucose levels of 6 recipient mice were restored to normal at 1 month after transplantation, whereas the blood glucose levels of the other 4 recipient mice were high, which died before the end of monitoring. In the renal capsule group, the blood glucose levels of 10 recipient mice returned to normal within 10 d after transplantation. Islet grafts of the recipient mice in two groups could lower the blood glucose levels, whereas the islet grafts in the white adipose group required a longer time to exert the effect. The glucose tolerance function of the mice in the renal capsule group was significantly better than that of those in white adipose group (P < 0.05). Histopathological examination demonstrated that the insulin of the islet grafts was normally expressed in two groups. Conclusions The islets transplanted into the subcutaneous white adipose tissues of inguinal region can play an effective role in regulating the changes of blood glucose level. Although the blood glucose-lowering function is slightly weaker than that of the islets graft in the renal capsule, it has multiple advantages resembling the ideal islet transplantation sites, which is a promising replacement site for islet transplantation.

To establish a platform to monitor the immune rejection after abdominal aortic patch suture in a xenotransplantation model.Methods The carotid was excised from wild-type Bama pigs,cut into 2.5 cmx 1.0 cm pieces in shuttle shape and subsequently sutured to the abdominal aorta of cynomolgus monkeys.No immunosuppressive agent was administered.General conditions of the recipient monkeys were observed.The morphological changes of the graft artery were assessed by pathological examination at postoperative 1 year.Before and 7,14,28 and 49 d after surgery,the blood samples were collected from the recipient monkeys.The serum levels of IgM and IgG antibodies were quantitatively measured by the red blood cell and peripheral blood mononuclear cell (PBMC) from Bama pigs.The quantity of lymphocytes in the recipient monkeys was detected by routine blood test and flow cytometry.Results All 3 monkeys undergoing transplantation survived well.At postoperative 1 year,the lateral tissues of the vascular wall at the artery graft were seen in dark red color.Hematoxylin-eosin (HE) staining revealed a large quantity of red blood cell and platelet deposition,accompanied with lymphocyte infiltration.Using porcine red blood cell and PBMC as target cells,the serum levels of anti-pig IgM and IgG antibodies peaked at postoperative 28 d,and slightly declined at postoperative 49 d.The quantity of lymphocytes and T cell subset also peaked at postoperative 28 d and began to decrease at postoperative 49 d.Conclusions Artery patch suture is a simple and reliable xenotransplantation model.The recipients can maintain normal physiological state without the use of immunosuppressive agents.The grafts can effectively activate the immune system of the recipients,induce the production of anti-pig antibodies and provoke cellular immune rejection.Therefore,this model can be utilized to monitor the immune rejection throughout the xenotransplantation process.

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.