Objective:To observe any effect of an enriched environment (EET) on cognitive functioning and sonic hedgehog (SHH) signaling in rats modeling cerebral ischemia.Methods:Sixty adult male Sprague-Dawley rats were randomly divided into a blank control group, a model group and a training group. A model of cerebral ischemia was established in the model and training groups by thread thrombus. The training group was given an EET. After 7 and 14 days, the rats′ cognition was tested using the Morris water maze and the platform jumping test. Apoptosis of brain cells in the hippocampus was detected by using TUNEL staining, and the expression of SHH, Gli2 and PTCH1 proteins in the hippocampus were measured using qRT-PCRs, western blotting and immunohistochemistry.Results:After 14 days the average escape latency in the Morris water maze test had shortened more in the training group than in the model group, while the average swimming speed, the number of platform crossings and the time spent in the quadrant had increased significantly more. They also received fewer electric shocks and spent significantly less time on the platform in the platform jumping test on average. Apoptosis in the hippocampus after 14 days was significantly less in the training group with significantly greater expression of SHH and Gli2 protein and significantly less PTCH1 protein expression.Conclusion:An EET can significantly improve cognition after cerebral ischemia, at least in rats. Its mechanism may be related to enhanced activation of the SHH signaling pathway.

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.