BACKGROUND:Recent studies have found that long non-coding RNAs (lncRNAs) can regulate stem cel proliferation and differentiation. But it is unclear that how lncRNA NR_033474 regulate stem cel proliferation and cel cycle. OBJECTIVE:To investigate the effect of lncRNA NR_033474 on the proliferation and cel cycle regulation in C3H10T1/2 mesenchymal stem cel s after the NR_033474 overexpressed by lentivirus, and to study the possible regulation mechanism of NR_033474 on mesenchymal stem cel s. METHODS:LncRNA NR_033474 was cloned into a lentivirus vector. Lentivirus particles were infected into C3H10T1/2 cel s to upregulate the expression of NR_033474. The NR_033474 expression level was detected by real-time PCR. Compared with the empty lentivirus vector, the proliferation of C3H10T1/2 cel s which overexpressed NR_033474 was detected by cel counting assay and cel cycle was detected using flow cytometry. The expression of cel cycle-associated proteins such as CDK1, Cyclin B1, Cyclin D1 and P53 were detected by western blot assay. RESULTS AND CONCLUSION:Compared with the control group, lncRNA NR_033474 in C3H10T1/2 cel s which overexpressed NR_033474 was increased by about 100 times (P<0.01), and the proliferation of C3H10T1/2 cel s was significantly inhibited after NR_033474 overexpression by lentivirus (P<0.05). In addition, flow cytometry showed that C3H10T1/2 cel s overexpressing NR_033474 were arrested in G2/M phase compared to the control group. Western blot showed that the expression levels of CDK1 and Cyclin B1 were downregulated, while there were no changes in Cyclin D1 and P53 expression. To conclude, these findings suggest that the NR_033474 overexpression significantly inhibits the cel growth of C3H10T1/2 cel s, at least in part, through induction of cel cycle arrest at G2/M phase.

Objective We conducted a survey on the awareness rate of stroke in Yangquan residents and analyzed factors affecting awareness to provide basic information for Yangquan residents to prevent and treat stroke in Shanxi.MethodsA questionnaire regarding awareness and basic knowledge of stroke was used in this study and applied to Yangquan residents.A cluster stratified sample of 7983 residents were questionnaired by cross-sectional method.Results7921 returned copies were valid.The awareness rate of stroke in 7921 selected Yangquan residents was 30.14％ (2387/7921). Upon the occurrence of stroke,the awareness rates of the way to see a doctor,the right department to look for medical care and test manners to diagnose the illness as stroke were relatively high in these investigated people,which were 74.03％ (5864/7921),78.17％ (6192/7921) and 84.04％ (6657/7921),respectively.However,the number of people who knew characteristic symptoms and complications of stoke,risk factors that cause stroke and how to treat stroke was 2021 (25.51％),841 ( 10.62％ ),and 902 ( 11.39％ ),respectively,which was relatively low. After multivariate analysis,the awareness rate of stroke in Yangquan residents was positively correlated with an education degree.ConclusionsYangquan residents know little about stroke.Therefore,it is necessary to increase the level of their knowledge regarding stroke by extended propaganda and encourage them to live a healthy life to be able to treat stoke in time and to reduce the possibility of stroke occurring.

Objective:To construct a combined radiomics model based on preoperative enhanced computed tomography (CT) examination for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC), and provide biological explanations for the radiomics model.Methods:The retrospective cohort study was conducted. The messenger RNA (mRNA) of 424 HCC patients, the clinicopathological data of 39 HCC patients entered into the Cancer Genome Atlas database from its establishment until January 2023, and the clinicopathological data of 53 HCC patients who were admitted to the Gansu Provincial People′s Hospital from January 2020 to January 2023 were collected. The 92 HCC patients were randomly divided into a training dataset of 64 cases and a test dataset of 28 cases with a ratio of 7∶3 based on a random number table method. The CT images of patients in the arterial phase and portal venous phase as well as the corresponding clinical data were analyzed. The 3Dslicer software (version 5.0.3) was used to register the CT images in the arterial phase and portal venous phase and delineate the three-dimensional regions of interest. The original images were preprocessed and the corresponding features were extracted by the open-source software FAE (version 0.5.5). After selecting features using the Least Absolute Shrinkage and Selection Operator, the radiomics model was constructed and the radiomics score (R-score) was calculated. The nomogram was constructed by integrating clinical parameters, imaging features and R-score based on Logistic regression. The gene modules related to radiomics model were obtained and subjected to enrichment analysis by conducting weighted gene co-expression network analysis and correlation analysis. Observation indicators: (1) comparison of clinical characteristics of patients with different MVI properties; (2) establishment of MVI risk model; (3) evaluation of MVI risk model; (4) clustering of gene modules; (5) functional enrichment of feature-correlated gene modules. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was conducted using the independent sample t test. Measurement data with skewed distribution were represented as M(range), and comparison between groups was conducted using the Mann-Whitney U test. Comparison of count data was conducted using the chi-square test. The intra-/inter-class correlation coefficient (ICC) was used to assess the inter-observer consistency of radiomics feature extracted by different observers. ICC >0.75 indicated a good consistency in feature extraction. The Logistic regression model was used for univariate and multivariate analyses. The receiver operating characteristic curve was drawn, and the area under curve (AUC), the decision curve and the calibration curve were used to evaluate the diagnostic efficacy and clinical practicality of the model. Results:(1) Comparison of clinical characteristics of patients with different MVI properties. Of 92 HCC patients, there were 47 cases with MVI-positive and 45 cases with MVI-negative, and there were significant differences in hepatitis, tumor diameter, peritumoral enhancement, intratumoral arteries, pseudocapsule and smoothness of tumor margin between them ( χ2=5.308, 9.977, 47.370, 32.368, 21.105, 31.711, P<0.05). (2) Establishment of MVI risk model. A total of 1 781 features were extrac-ted from arterial and portal venous phases of the intratumoral and peritumoral regions. After feature dimension reduction, 8 radiomics features were selected from arterial and portal venous phases to construct the combined model. Results of multivariate analysis showed that peritumoral enhancement, intratumoral arteries, pseudocapsule, smoothness of tumor margins, and R-score were independent risk factors for MVI in patients with HCC [ hazard ratio=0.049, 0.017, 0.017, 0.021, 2.539, 95% confidence interval ( CI) as 0.005-0.446, 0.001-0.435, 0.001-0.518, 0.001-0.473, 1.220-5.283, P<0.05]. A nomogram model was constructed incorporating peritumoral enhancement, intratumoral arteries, pseudocapsule, smoothness of tumor margins, and R-score. (3) Evaluation of the MVI risk model. The AUC of radiomics model was 0.923 (95% CI as 0.887-0.944) and 0.918 (95% CI as 0.894-0.945) in the training dataset and test dataset, respectively. The AUC of nomogram model, incorpora-ting both the R-score and radiomics features, was 0.973 (95% CI as 0.954-0.988) and 0.962 (95% CI as 0.942-0.987) in the training dataset and test dataset, respectively. Results of decision curve showed that the nomogram had better clinical utility compared to the R-score. Results of calibration curve showed good consistency between the actual observed outcomes and the nomogram or the R-score. (4) Clustering of gene module. Results of weighted gene co-expression network analysis showed that 8 gene modules were obtained. (5) Functional enrichment of feature-related gene modules. Results of correlation analysis showed 4 gene modules were significantly associated with radiomics features. The radiomics features predicting of MVI may be related to pathways such as the cell cycle, neutrophil extracellular trap formation, and PPAR signaling pathway. Conclusions:The combined radiomics model based on preoperative enhanced CT imaging can predict the MVI status of HCC. By obtaining mRNA gene expression profiles associated with radiomics features, a biological interpretation of the radiomics model is provided.