Acupuncture Therapy ; Adolescent ; Drugs, Chinese Herbal ; administration & dosage ; Female ; Furunculosis ; drug therapy ; therapy ; Humans

Objective To comprehensively consider the effect of low diverter (FD) implantation on aneurysmal sac and its branches, so as to provide references for making a more reasonable surgical strategy for intracranial aneurysm embolization in clinical practice. Methods Based on computational fluid dynamics (CFD) method, the FD implantation procedure was simulated by using porous media model innovatively. Changes in hemodynamic parameters of aneurysmal sac and side branch with different diameters before and after FD implantation were compared and analyzed, such as blood flow field, velocity, wall pressure and wall shear stress (WSS). Results FD changed the hemodynamic characteristics of aneurysms. The blood flow velocity decreased significantly. The WSS on aneurysmal neck increased, while the difference of WSS between proximal and distal cervical area reduced conversely. Different side branch diameters of vessels had different effects on hemodynamic characteristic changes. The larger diameter would cause the greater blood flow reduction in side branch after FD implantation, but the decrease in velocity of aneurysmal sac and pressure on aneurysmal roof became smaller simultaneously. Meanwhile, the increase of WSS on aneurysmal neck was inversely proportional to the diameter of side branch. Conclusions The larger branch diameter of vessels would cause the worse effect of FD embolization therapy for intracranial aneurysm, worse atherosclerosis improvements and greater possibilities of branch occlusion or other ischemic complications. Doctors should pay more attention to such cases in FD interventional intravascular embolization in clinic.

Objective:To understand the genome analysis and molecular typing of foodborne Yersinia enterocolitica ( Y.e) strains in Liaocheng City of Shandong Province from 2020 to 2021. Methods:The Y.e strains were isolated from raw meat and meat products. Then we made the strain identification, drug sensitivity test, virulence gene test, pulsed-field gel electrophoresis (PFGE), and whole genome sequencing (WGS). The genome sequencing data were assembled with the microbial genome annotation package. We performed the multilocus sequence typing (MLST) and core genome multilocus sequence typing (cgMLST) and used WGS-based single nucleotide polymorphism typing (wg-SNPs) method to carry out genetic evolution analysis with 14 domestic and Y.e genomes obtained from the NCBI. Results:A total of 21 strains of Y.e were detected from 165 samples, with a detection rate of 12.73%. The 20 strains of Y.e were sequenced successfully. The 20 strains of Y.e carries a variety of drug resistance genes and virulence genes, showing multiple drug resistance. The virulence gene PCR test showed that 21 strains of Y.e having two virulence genes. Cluster analysis of PFGE, MLST, and cgMLST showed that the genomics of 21 strains was highly diverse. The genetic evolution analysis of wg-SNPs showed that 20 Y.e strains could be divided into two main evolutionary branches. Conclusions:Y.e strains isolated from raw meat in Liaocheng City carry a variety of drug resistance genes and virulence genes, and the molecular typing is highly diverse, which may cause infection risk. The molecular biological monitoring of Y.e in raw meat should be strengthened, and genome sequencing and molecular typing detection be carried out to provide the theoretical basis for foodborne illness caused by Y.e.

Objective To evaluate the application of three deep learning algorithms in automatic segmentation of clinical target volumes (CTVs) in high-dose-rate brachytherapy after surgery for endometrial carcinoma. Methods A dataset comprising computed tomography scans from 306 post-surgery patients with endometrial carcinoma was divided into three subsets: 246 cases for training, 30 cases for validation, and 30 cases for testing. Three deep convolutional neural network models, 3D U-Net, 3D Res U-Net, and V-Net, were compared for CTV segmentation. Several commonly used quantitative metrics were employed, i.e., Dice similarity coefficient, Hausdorff distance, 95th percentile of Hausdorff distance, and Intersection over Union. Results During the testing phase, CTV segmentation with 3D U-Net, 3D Res U-Net, and V-Net showed a mean Dice similarity coefficient of 0.90 ± 0.07, 0.95 ± 0.06, and 0.95 ± 0.06, a mean Hausdorff distance of 2.51 ± 1.70, 0.96 ± 1.01, and 0.98 ± 0.95 mm, a mean 95th percentile of Hausdorff distance of 1.33 ± 1.02, 0.65 ± 0.91, and 0.40 ± 0.72 mm, and a mean Intersection over Union of 0.85 ± 0.11, 0.91 ± 0.09, and 0.92 ± 0.09, respectively. Segmentation based on V-Net was similarly to that performed by experienced radiation oncologists. The CTV segmentation time was < 3.2 s, which could save the work time of clinicians. Conclusion V-Net is better than other models in CTV segmentation as indicated by quantitative metrics and clinician assessment. Additionally, the method is highly consistent with the ground truth, reducing inter-doctor variability and treatment time.