Objective To observe the effects of vascular endothelial growth factor antisense oligonucleotide (VEGF-ASODN) on expression of vascular endothelial growth factor (VEGF) and growth in gastric cancer cells. Methods The VEGF-ASODN was synthesized artificially with phosphorothioic acid. After transfecting with VEGF-ASODN in gastric cancer cells SGC-7901, the initial copy number of mRNA was detected by real-time RT-PCR, and the quantity of VEGF protein in both cell and supernatant were detected by ELISA. The levels of expression of survivin protein in cells were measured by Western blot. FCM and MTT method were used to detect cellular apoptosis and the activity of cells, respectively. The effect of transfection on the growth of cells was evaluated by growth curve. Results The copy number of VEGR mRNA, protein levels of VEGF in the cells and in culture fluid all decreased when the concentration of transfected VEGF-ASODN increased, as well as the levels of survivin protein （P＜0.05）. The ratio of apoptosis increased, the activity of cells also decreased as the concentration of transfected VEGF-ASODN increased （P＜0.05）. Conclusion Transfection with VEGF-ASODN in gastric cancer cells SGC-7901 can inhibit the expressions of VEGF and survivin remarkably. It can enhance cellular apoptosis and suppress growth of cells.
Objective To study the clinical and angiographic features in ST Segment Elevation Myocardial Infarction (STEMI) patients with spontaneous reperfusion. Methods A total of 519 patients with STEMI underwent Primary percutaneous coronary intervention (PCI) from January 2006 to December 2009 in Anzhen Hospital were enrolled. All patients were divided into the spontaneous reperfusion group (TIMI flow gradeⅢ ) and the non-spontaneous reperfusion group (TIMI flow grade 0-Ⅱ ) according to the TIMI flow grade before primary PCI. The incidence rate of spontaneous reperfusion through coronary angiography before primary PCI was observed, and the clinical relevant factors and angiographic lesion features of spontaneous reperfusion were analyzed. Results There were significant differences in age, CTnI peak value, high thrombus burden, and lesion location in distant LAD (P=0.000, 0.000, 0.002, 0.000, and 0.003, respectively) between the two groups. But there were no significant differences in gender, hypertension, diabetes mellitus, smoking history, hyperlipemia, angina pectoris history, culprit vessel distribution, lesion distribution in LCX and RCA, and collateral circulation between the two groups (Pgt;0.05 for all). Conclusion Compared to the patients without spontaneous reperfusion, patients with spontaneous reperfusion are younger in age, lower in CTnI peak value, and heavier in thrombosis burden, with culprit lesions mostly located in the distant LAD.
In recent years, the pollution problem of particulate matter, especially PM2.5, is becoming more and more serious, which has attracted many people’s attention from all over the world. In this paper, a Kalman prediction model combined with cubic spline interpolation is proposed, which is applied to predict the concentration of PM2.5 in the micro-regional environment of campus, and to realize interpolation simulation diagram of concentration of PM2.5 and simulate the spatial distribution of PM2.5. The experiment data are based on the environmental information monitoring system which has been set up by our laboratory. And the predicted and actual values of PM2.5 concentration data have been checked by the way of Wilcoxon signed-rank test. We find that the value of bilateral progressive significance probability was 0.527, which is much greater than the significant level α = 0.05. The mean absolute error (MEA) of Kalman prediction model was 1.8 μg/m3, the average relative error (MER) was 6%, and the correlation coefficient R was 0.87. Thus, the Kalman prediction model has a better effect on the prediction of concentration of PM2.5 than those of the back propagation (BP) prediction and support vector machine (SVM) prediction. In addition, with the combination of Kalman prediction model and the spline interpolation method, the spatial distribution and local pollution characteristics of PM2.5 can be simulated.
Cranial defects may result from clinical brain tumor surgery or accidental trauma. The defect skulls require hand-designed skull implants to repair. The edge of the skull implant needs to be accurately matched to the boundary of the skull wound with various defects. For the manual design of cranial implants, it is time-consuming and technically demanding, and the accuracy is low. Therefore, an informer residual attention U-Net (IRA-Unet) for the automatic design of three-dimensional (3D) skull implants was proposed in this paper. Informer was applied from the field of natural language processing to the field of computer vision for attention extraction. Informer attention can extract attention and make the model focus more on the location of the skull defect. Informer attention can also reduce the computation and parameter count from N2 to log(N). Furthermore,the informer residual attention is constructed. The informer attention and the residual are combined and placed in the position of the model close to the output layer. Thus, the model can select and synthesize the global receptive field and local information to improve the model accuracy and speed up the model convergence. In this paper, the open data set of the AutoImplant 2020 was used for training and testing, and the effects of direct and indirect acquisition of skull implants on the results were compared and analyzed in the experimental part. The experimental results show that the performance of the model is robust on the test set of 110 cases fromAutoImplant 2020. The Dice coefficient and Hausdorff distance are 0.940 4 and 3.686 6, respectively. The proposed model reduces the resources required to run the model while maintaining the accuracy of the cranial implant shape, and effectively assists the surgeon in automating the design of efficient cranial repair, thereby improving the quality of the patient’s postoperative recovery.
The increasing number of pulmonary nodules being detected by computed tomography scans significantly increase the workload of the radiologists for scan interpretation. Limitations of traditional methods for differential diagnosis of pulmonary nodules have been increasingly prominent. Artificial intelligence (AI) has the potential to increase the efficiency of discrimination and invasiveness classification for pulmonary nodules and lead to effective nodule management. Chinese Experts Consensus on Artificial Intelligence Assisted Management for Pulmonary Nodule (2022 Version) has been officially released recently. This article closely follows the context, significance, core implications, and the impact of future AI-assisted management on the diagnosis and treatment of pulmonary nodules. It is hoped that through our joint efforts, we can promote the standardization of management for pulmonary nodules and strive to improve the long-term survival and postoperative life quality of patients with lung cancer.