AIM:This study aims to examine the ependymin-related protein 1(EPDR1)expression in various tissues from wild-type C57BL/6 mice and type 2 diabetes(db/db)mice.The impact of EPDR1 on lipid accumulation in al-pha mouse liver 12(AML12)hepatocytes was also investigated.METHODS:Western blot was used to detect EPDR1 protein expression in the heart,liver,spleen,lung,kidney,gastrocnemius,brown adipose and brain tissues of C57BL/6 mice.Western blot and immunohistochemical(IHC)staining were also used to compare EPDR1 protein expression in the liver,gastrocnemius muscle,heart and kidney tissues of db/db and C57BL/6 mice.To develop an AML12 cell lipid deposi-tion model,palmitic acid(PA)+oleic acid(OA)was used,and the cells were transfected with adenovirus overexpressing EPDR1 or treated with exogenous recombinant EPDR1 protein(rEPDR1).ELISA was conducted to determine intracellu-lar triglyceride(TG)content,and oil red O staining was employed to assess the effect of EPDR1 on lipid accumulation in AML12 cells.RESULTS:Western blot and IHC staining results revealed that EPDR1 was widely expressed in various tis-sues of wild-type mice,with the liver exhibiting the highest protein expression level.However,EPDR1 expression was down-regulated in the liver,gastrocnemius muscle,heart and kidney tissues in diabetic db/db mice compared with wild-type mice.Oil red O staining revealed that overexpression of EPDR1 in AML12 liver cells or rEPDR1 treatment led to re-duced lipid accumulation.Furthermore,the TG content significantly decreased compared with the model group(P<0.05).CONCLUSION:EPDR1 is expressed in various tissues of wild-type mice,but showed diminished expression in the liver tissues of diabetic mice.Nevertheless,enhancing the expression of EPDR1 can aid in reducing lipid accumula-tion in hepatocytes.These findings provide an experimental foundation for further exploration of the role of EPDR1 in the development of fatty liver in diabetic liver tissue.

Objective To observe the value of CT radiomics for differentiating spinal bone islands(BI)and osteoblastic metastases(OBM).Methods Data of 109 BI lesions in 98 patients and 282 OBM lesions in 158 patients(including 103 OBM in 48 lung cancer cases,86 OBM in 52 breast cancer cases and 93 OBM in 58 prostate cancer cases)from 3 medical institutions were retrospectively analyzed.Data obtained from institution 1 were used as the internal dataset and divided into internal training set and internal validation set at a ratio of 7∶3,from institution 2 and 3 were used as external dataset.All datasets were divided into female data subset(including OBM of female lung cancer and breast cancer)and male data subset(including OBM of male lung cancer and prostate cancer).Radiomics features were extracted and screened to construct 3 different support vector machine(SVM)models,including model1 for distinguishing BI and OBM,model2 for differentiating OBM of female lung cancer and breast cancer,and model3 for differentiating OBM of male lung cancer and prostate cancer.Diagnostic efficacy of model1,CT value alone and 3 physicians(A,B,C)for distinguishing BI and OBM were assessed,as well as differentiating efficacy for different OBM of model2 and model3.Receiver operating characteristic(ROC)curves were drawn,and area under the curves(AUC)were calculated and compared.The differential diagnostic efficacy of model2 and model3 were also assessed with ROC analysis and AUC.Results AUC of model1 for distinguishing spinal OBM from BI in internal training set,internal validation set and external dataset was 0.99,0.98 and 0.86,respectively.In internal training set,model1 had higher AUC for distinguishing BI and OBM than that of physician A(AUC=0.78),B(AUC=0.87)and C(AUC=0.93)as well as that of mean CT value(AUC=0.78,all P＜0.05).AUC in internal training set,internal validation set and external dataset of model2 for identifying female lung cancer and breast cancer OBM was 0.79,0.75 and 0.73,respectively,of model3 for discriminating male lung cancer from prostate cancer OBM was 0.77,0.74 and 0.77,respectively.Conclusion CT radiomics SVM model might reliablely distinguish OBM and BI.