ABSTRACT
Silk fibroin is a novel biomaterial for enhancing transplanted islet cell function and survival. This study investigated whether silk fibroin may have unique properties that improve islet function in the face of inflammatory-mediated stress during transplantation. Murine islet function was tested in vitro with either silk fibroin or alginate and challenged with inflammatory cytokines. The glucose-stimulated insulin secretion index for all conditions decreased with inflammatory cytokines, but was better preserved for islets exposed to silk compared to those exposed to alginate or medium. GLUT2 transporter expression on the cell surface of islets exposed to silk was increased compared to alginate or medium alone. Upon cytokine stress, a greater percentage of islet cells exposed to silk expressed GLUT2 on their surface. We conclude that preconditioning islets with silk fibroin stimulates islet cell surface GLUT2 expression, an increase, which persists under inflammatory stress, and may improve islet engraftment and function after transplantation.
Subject(s)
Fibroins/metabolism , Fibroins/pharmacology , Islets of Langerhans/drug effects , Alginates/pharmacology , Animals , Fibroins/physiology , Glucose Transporter Type 2/genetics , Glucose Transporter Type 2/metabolism , Inflammation , Insulin-Secreting Cells/drug effects , Islets of Langerhans/physiology , Islets of Langerhans Transplantation/methods , Islets of Langerhans Transplantation/physiology , Male , Mice , Mice, Inbred C57BL , Silk/physiology , Stress, Physiological/drug effectsABSTRACT
OBJECTIVE: A neural network method was used to establish a dose prediction model for organs at risk (OARs) during intensity-modulated radiotherapy (IMRT) for nasopharyngeal carcinoma (NPC). MATERIALS AND METHODS: In total, 103 patients with NPC were randomly selected for IMRT. Suborgans were automatically generated for OARs using ring structures based on distance to the target using a MATLAB program and the corresponding volume of each suborgan was determined. The correlation between the volume of each suborgan and the dose to each OAR was analysed and neural network prediction models of the OAR dose were established using the MATLAB Neural Net Fitting application. The R-value and mean square error in the regression analysis were used to evaluate the prediction model. RESULTS: The OAR dose was related to the volume of the corresponding sub-OAR. The average R-values for the normalised mean dose (Dnmean) to parallel organs and serial organs and the normalised maximum dose (Dn0) to serial organs in the training set were 0.880, 0.927 and 0.905, respectively. The mean square error for each OAR in the prediction model was low (ranging from 1.72 × 10-4 to 7.06 × 10-3). CONCLUSION: The neural network-based model for predicting OAR dose during IMRT for NPC is simple, reliable and worth further investigation and application.