To study the mechanism of injection of sinomenine (Sinomenine,SIN)into Jiaji point in the treatment of L1-L5 segment Compression of fracture spine (VCF) in rabbits.Methods:54 rabbits were randomly divided into blank control group,model group and sinomenine group (group SIN)by the random number table.All the 2 groups established L1-L5 segment VCF model except the blank control group.After molding,the SIN group was injected SIN at both sides of the L1-L5 section of the vertebral column,the model group and the blank control group were injected the same volume of physiological saline as comparison.The 3 groups were treated for 4 weeks.Fracture of vertebral bone growth differentiation factor 2 (Growth differentiation,FACTOR2,GDF2),vascular endothelial growth factor (Vascular endothelial,growth factor,VEGF) and the expression of VEGF-mRNA were determined by immunohistochemical staining method;in venous blood,activities of nitric oxide (Nitric oxide NO),super oxide dissuade (Super oxide dims,unties,SOD) and bone protection Su (OPG) were detected by ELISA;immunofluorescence was used to measure two chitosan,image analysis method was used to detect spinal bone density in L1-L5 segment,the ELISA was used to detect inflammatory factor interleukin 1 beta (IL-1 beta),IL-6 beta,beta IL-17 in the spinal disc.Results:compared with the model group,in the SIN group,VEGF,VEGFmRNA and GDF2 in L1-L5 segment of spine were highly expressed,and the bone density increased (P＜0.05);two chitosan in intervertebral disc increased,IL-1beta,IL-6 beta,IL-17 beta,(P＜ 0.05);NO in venous blood decreased OPG,SOD increased (P＜0.05),the differences were statistically significant.Conclusion:injection of SIN into Jiaji can promote the healing of VCF in L1-L5 segment in rabbits.

Objective To explore the clinical value of models based on clinical features and enhanced MRI radiomics for predicting the occurrence of post-acute pancreatitis diabetes mellitus(PPDM-A).Methods A retrospective selection of 161 acute pancreatitis(AP)patients was conducted,comprising 99 in the non-PPDM-A group and 62 in the PPDM-A group.They were randomly divided into training set and test set in a ratio of 7∶3.Region of interest(ROI)were delineated and radiomics features were extracted on the late arterial phase MRI images.Optimal radiomics features were selected by maximum relevance and minimum redundancy(mRMR)and least absolute shrinkage and selection operator(LASSO).Support vector machine(SVM)was used to develop three predictive models.The efficacy of the models in predicting PPDM-A was evaluated,the receiver operating characteristic(ROC)curve was drawn,and the DeLong test was employed to assess the difference in predictive capability among the models.Results In the training set,the area under the curve(AUC)of the clinical model,radiomics model,and combined model were 0.702,0.810 and 0.901,respectively,and in the test set were 0.678,0.797 and 0.830,respectively.The DeLong test revealed a statistically significant difference in the predictive capability of the combined model compared to the clinical model both in the training and test sets(training set:P＜0.001;test set:P=0.019).Conclusion The combined model based on clinical features and enhanced MRI radiomics features demonstrates good predictive effi-cacy and can provide valuable insights for clinical interventions aimed at preventing PPDM-A.