Bax inhibitor 1 inhibits vascular calcification in mice by activating optic atrophy 1 expression / 南方医科大学学报

OBJECTIVE@#To investigate the effects of Bax inhibitor 1 (BI- 1) and optic atrophy protein 1 (OPA1) on vascular calcification (VC).@*METHODS@#Mouse models of VC were established in ApoE-deficient (ApoE-/-) diabetic mice by high-fat diet feeding for 12 weeks followed by intraperitoneal injections with Nε-carboxymethyl-lysine for 16 weeks. ApoE-/- mice (control group), ApoE-/- diabetic mice (VC group), ApoE-/- diabetic mice with BI-1 overexpression (VC + BI-1TG group), and ApoE-/- diabetic mice with BI-1 overexpression and OPA1 knockout (VC+BI-1TG+OPA1-/- group) were obtained for examination of the degree of aortic calcification using von Kossa staining. The changes in calcium content in the aorta were analyzed using ELISA. The expressions of Runt-related transcription factor 2 (RUNX2) and bone morphogenetic protein 2 (BMP-2) were detected using immunohistochemistry, and the expression of cleaved caspase-3 was determined using Western blotting. Cultured mouse aortic smooth muscle cells were treated with 10 mmol/L β-glycerophosphate for 14 days to induce calcification, and the changes in BI-1 and OPA1 protein expressions were examined using Western blotting and cell apoptosis was detected using TUNEL staining.@*RESULTS@#ApoE-/- mice with VC showed significantly decreased expressions of BI-1 and OPA1 proteins in the aorta (P=0.0044) with obviously increased calcium deposition and expressions of RUNX2, BMP-2 and cleaved caspase-3 (P= 0.0041). Overexpression of BI-1 significantly promoted OPA1 protein expression and reduced calcium deposition and expressions of RUNX2, BMP-2 and cleaved caspase-3 (P=0.0006). OPA1 knockdown significantly increased calcium deposition and expressions of RUNX2, BMP-2 and cleaved caspase-3 in the aorta (P=0.0007).@*CONCLUSION@#BI-1 inhibits VC possibly by promoting the expression of OPA1, reducing calcium deposition and inhibiting osteogenic differentiation and apoptosis of the vascular smooth muscle cells.

A model study of diagnosing mediastinal metastasis lymph nodes in non-small cell lung cancer based on CT radiomics / 中华放射医学与防护杂志

Objective To establish radiomics models based on different CT scaning phases to distinguish mediastinal metastatic lymph nodes in NSCLC and to explore the diagnostic efficacy of these models.Methods The CT images of 86 preoperative patients with NSCLC who were performed both plain and enhanced CT scans were analyzed retrospectively.The 231 mediastinal lymph nodes were enrolled in this study which were divided into two independent cohorts:163 lymph nodes enrolled from January 2015 to June 2017 constituted the training cohort,and 68 lymph nodes enrolled from July 2017 to June 2018 constituted the validation cohort.The regions of interest (ROIs) were delineated on plain scan phase,arterial phase and venous phase CT images respectively,and 841 features were extracted from each ROI.LASSO-logistic regression analysis was used to select features and develop models.The area under the ROC curve (AUC value),sensitivity,specificity,accuracy,positive predictive value and negative predictive value of different models for distinguishing metastatic lymph nodes were compared.Results A total of 6 models were established,and the AUC values were all greater than 0.800.The plain CT model yielded the highest AUC,specificity,accuracy and positive predictive value with 0.926,0.860,0.871,0.906 in the training cohort and 0.925,0.769,0.882,0.870 in the validation cohort.When plain and venous phase CT images were combined with arterial phase CT images,the sensitivity and negative predictive value of the models increased from 0.879,0.821 and 0.919,0.789 to 0.949,0.878 and 0.979,0.900 respectively.Conclusions The CT radiomics model could be used to assist the clinical diagnosis of lymph nodes.The AUC value of the model based on plain scanning was the highest,while the sensitivity and negative predictive value of the model could be improved by combining the arterial phase CT images.

A model study of diagnosing mediastinal metastasis lymph nodes in non-small cell lung cancer based on CT radiomics / 中华放射医学与防护杂志

Objective@#To establish radiomics models based on different CT scaning phases to distinguish mediastinal metastatic lymph nodes in NSCLC and to explore the diagnostic efficacy of these models.@*Methods@#The CT images of 86 preoperative patients with NSCLC who were performed both plain and enhanced CT scans were analyzed retrospectively. The 231 mediastinal lymph nodes were enrolled in this study which were divided into two independent cohorts: 163 lymph nodes enrolled from January 2015 to June 2017 constituted the training cohort, and 68 lymph nodes enrolled from July 2017 to June 2018 constituted the validation cohort. The regions of interest (ROIs) were delineated on plain scan phase, arterial phase and venous phase CT images respectively, and 841 features were extracted from each ROI. LASSO-logistic regression analysis was used to select features and develop models. The area under the ROC curve (AUC value), sensitivity, specificity, accuracy, positive predictive value and negative predictive value of different models for distinguishing metastatic lymph nodes were compared.@*Results@#A total of 6 models were established, and the AUC values were all greater than 0.800. The plain CT model yielded the highest AUC, specificity, accuracy and positive predictive value with 0.926, 0.860, 0.871, 0.906 in the training cohort and 0.925, 0.769, 0.882, 0.870 in the validation cohort. When plain and venous phase CT images were combined with arterial phase CT images, the sensitivity and negative predictive value of the models increased from 0.879, 0.821 and 0.919, 0.789 to 0.949, 0.878 and 0.979, 0.900 respectively.@*Conclusions@#The CT radiomics model could be used to assist the clinical diagnosis of lymph nodes. The AUC value of the model based on plain scanning was the highest, while the sensitivity and negative predictive value of the model could be improved by combining the arterial phase CT images.

Simvastatin protect retinal ganglion cells against optic nerve crush in mice / 眼科新进展

Objectivc To investigate the protective effect of simvastatin on retinal ganglion cells (RGC) after optic nerve crush (ONC).Methods Together 50 Kunming mice were randomly divided into normal group,sham group,ONC group and simvastatin protection group.The mice in the normal group were untreated,the sham group was treated with the exposure of the optic nerve without injury,the ONC group mice underwent ONC operation on the left eyes,followed by intravitreal administration of equilibrium solvent [50 mg · mL-1 ethanol plus 1 mol · L-1 NaOH,which were activated by 1 mol · L-1 HC1 (pH 7.2)],and the simvastatin protection group was intravitreally injected different concentrations of simvastatin (0.5 g · L-1,1.0 g · L-1,1.5 g · L-1) after ONC operation.Brn3a immunofluorescence staining,HE staining and toluidine blue staining were used to detect the apoptosis of RGC and pathological changes of optic nerve.Results On day 7 after operation,in the ONC group,the apoptosis of RGC was observed obviously,with the survival rate dropping to (35.1 ± 3.9) ％,and the thickness from the retinal ganglion cell layer to outer nuclear layer was decreased from (123.13 ± 1.04) μm to (97.48 ± 2.33) μm,which was significantly thinner than that in the control group (P ＜ 0.01);moreover,the fibrous bundle of optic nerve disappeared,and the neuroglial cells were clustered into groups,as well as the axons showed swelling and serious degeneration,but after intravitreal injection of 1.0 g · L-1 simvastatin,the survival rate of retinal ganglion cells increased to (76.3 ± 3.7) ％ (P ＜ 0.05),and the aforementioned thickness was increased to (111.39 ± 4.06) μm,which was statistically significant when compared with the ONC group (P ＜ 0.01).The degeneration of optic nerve was improved,and the structure of neuroglial cell axons and the nerve fibers became normal.Conclusion Simvastatin can reduce the optic nerve degeneration and improve the survival rote of retinal ganglion cells.