Objective To observe the value of intratumoral and peritumoral radiomics based on dynamic contrast-enhanced MRI(DCE-MRI)combined with clinical indexes for predicting Ki-67 expression of prostate cancer(PCa).Methods Totally 294 PCa patients were retrospectively enrolled and randomly divided into training set(n=205)and test(n=89)set at a ratio of 7∶3,who were stratified into low-expression subgroup(Ki-67≤10%)and high-expression subgroup(Ki-67＞10%)based on pathological findings.ROIintratumoral and ROIperitumoral were delineated on DCE-MRI,and radiomics features were extracted from ROIintratumora,l ROIperitumoral and ROIintratumoral+peritumora,l respectively.Radiomic features significantly associated with Ki-67 expression status were selected to construct radiomics models.A multivariate logistic regression analysis was performed to develop a clinical predicting model,then a combined model was established combined with the optimal radiomics model.Receiver operating characteristic(ROC)curves were plotted,and the area under the curve(AUC)were calculate to evaluate the predictive efficacy of the models and compared between each two models.Decision curve analysis(DCA)was used to assess the clinical net benefit of each model.Results The AUC of modelintratumora,l modelperitumoral and modelintratumoral+peritumoral for predicting Ki-67 expression in training set was 0.905,0.867 and 0.930,and the last one was the best.In clinical model,total prostate-specific antigen(t-PSA)and T stage≥3 were both independent predictors of high Ki-67 expression of PCa(both P＜0.05).The AUC of combined model based on modelintratumoral+peritumoral and clinical model was 0.911 in test set,being not significantly different with that of modelintratumoral+peritumoral(AUC=0.906;Z=0.349,P=0.727)but higher than that of clinical model(AUC=0.684;Z=4.370,P＜0.05).DCA revealed that the combined model provided higher clinical net benefit than clinical model and modelintratumoral+peritumoral across risk thresholds of 0.10-0.70.Conclusion DCE-MRI intratumoral+peritumoral radiomics model could effectively predict Ki-67 expression status of PCa.Combining with clinical indexes could further enhance its clinical net benefit.

Objective To observe the value of intratumoral and peritumoral radiomics based on dynamic contrast-enhanced MRI(DCE-MRI)combined with clinical indexes for predicting Ki-67 expression of prostate cancer(PCa).Methods Totally 294 PCa patients were retrospectively enrolled and randomly divided into training set(n=205)and test(n=89)set at a ratio of 7∶3,who were stratified into low-expression subgroup(Ki-67≤10%)and high-expression subgroup(Ki-67＞10%)based on pathological findings.ROIintratumoral and ROIperitumoral were delineated on DCE-MRI,and radiomics features were extracted from ROIintratumora,l ROIperitumoral and ROIintratumoral+peritumora,l respectively.Radiomic features significantly associated with Ki-67 expression status were selected to construct radiomics models.A multivariate logistic regression analysis was performed to develop a clinical predicting model,then a combined model was established combined with the optimal radiomics model.Receiver operating characteristic(ROC)curves were plotted,and the area under the curve(AUC)were calculate to evaluate the predictive efficacy of the models and compared between each two models.Decision curve analysis(DCA)was used to assess the clinical net benefit of each model.Results The AUC of modelintratumora,l modelperitumoral and modelintratumoral+peritumoral for predicting Ki-67 expression in training set was 0.905,0.867 and 0.930,and the last one was the best.In clinical model,total prostate-specific antigen(t-PSA)and T stage≥3 were both independent predictors of high Ki-67 expression of PCa(both P＜0.05).The AUC of combined model based on modelintratumoral+peritumoral and clinical model was 0.911 in test set,being not significantly different with that of modelintratumoral+peritumoral(AUC=0.906;Z=0.349,P=0.727)but higher than that of clinical model(AUC=0.684;Z=4.370,P＜0.05).DCA revealed that the combined model provided higher clinical net benefit than clinical model and modelintratumoral+peritumoral across risk thresholds of 0.10-0.70.Conclusion DCE-MRI intratumoral+peritumoral radiomics model could effectively predict Ki-67 expression status of PCa.Combining with clinical indexes could further enhance its clinical net benefit.

Epilepsies are a group of chronic neurological disorders characterized by spontaneous recurrent seizures caused by abnormal synchronous firing of neurons and transient brain dysfunction. The underlying mechanisms are complex and not yet fully understood. Endoplasmic reticulum (ER) stress, as a condition of excessive accumulation of unfolded and/or misfolded proteins in the ER lumen, has been considered as a pathophysiological mechanism of epilepsy in recent years. ER stress can enhance the protein processing capacity of the ER to restore protein homeostasis through unfolded protein response, which may inhibit protein translation and promote misfolded protein degradation through the ubiquitin-proteasome system. However, persistent ER stress can also cause neuronal apoptosis and loss, which may aggravate the brain damage and epilepsy. This review has summarized the role of ER stress in the pathogenesis of genetic epilepsy.
Humans ; Endoplasmic Reticulum Stress/genetics* ; Unfolded Protein Response ; Endoplasmic Reticulum/pathology* ; Apoptosis ; Epilepsy/genetics*

Objective To investigate whether Tumor Necrosis Factor-alpha (TNFα) is capable of activating Rho kinase pathway which leads to smooth muscle cell proliferation and the intervention function of Rosuvastatin, and clarify the mechanism and intervention manner of anti-atherosclerosis by Rosuvastatin. Methods Wistar neonate rat smooth muscle cells were cultured, and the activity of cell proliferation was determined by methyl thiazolyl tetrazolium (MTT). The expression of Rho kinase genes after the stimulation of TNFα was evaluated by RT-PCR. Western blot method was used to measure the protein expression of proliferating cell nuclear antigen (PCNA) after TNFα stimulation and Rosuvastatin intervention in smooth muscle cell. Results The TNFα stimulation significantly enhanced the expression of Rho kinase and increased the expression of PCNA protein in smooth muscle cells (P < 0.05). These effects were positively correlated with prolonged treatment whereas additional Rosuvastatin administration inhibited the above-mentioned effects (P < 0.05). Conclusions The activation of TNFα mediated Rho kinase signaling pathway can significantly promote smooth muscle cell proliferation, and Rosuvastatin can not only inhibit this pathway but also the induced proliferation.

Coronaviruses are the causative agent of respiratory and enteric diseases in animals and humans. One example is SARS, which caused a worldwide health threat in 2003. In coronaviruses, the structural protein N (nucleocapsid protein) associates with the viral RNA to form the filamentous nucleocapsid and plays a crucial role in genome replication and transcription. The structure of N-terminal domain of MHV N protein also implicated its specific affinity with transcriptional regulatory sequence (TRS) RNA. Here we report the crystal structures of the two proteolytically resistant N- (NTD) and C-terminal (CTD) domains of the N protein from murine hepatitis virus (MHV). The structure of NTD in two different crystal forms was solved to 1.5 Å. The higher resolution provides more detailed structural information than previous reports, showing that the NTD structure from MHV shares a similar overall and topology structure with that of SARS-CoV and IBV, but varies in its potential surface, which indicates a possible difference in RNA-binding module. The structure of CTD was solved to 2.0-Å resolution and revealed a tightly intertwined dimer. This is consistent with analytical ultracentrifugation experiments, suggesting a dimeric assembly of the N protein. The similarity between the structures of these two domains from SARS-CoV, IBV and MHV corroborates a conserved mechanism of nucleocapsid formation for coronaviruses.
Amino Acid Sequence ; Binding Sites ; Crystallography, X-Ray ; Molecular Sequence Data ; Murine hepatitis virus ; chemistry ; metabolism ; Nucleocapsid Proteins ; chemistry ; metabolism ; Phosphoproteins ; chemistry ; metabolism ; Protein Binding ; Protein Folding ; Protein Multimerization ; Protein Structure, Secondary ; Protein Structure, Tertiary ; RNA ; metabolism ; Sequence Alignment