BACKGROUND:Studies have demonstrated that prosthetic valve endocarditis is primarily caused by bacteria adhering on the surface of the materials.Thus,the relationship of prosthetic valve materials with bacteria adhesion and growth is an important subject.OBJECTIVE:To explore the influence of prosthetic valve materials on bacteria growth through observing the relationship of bacteria adhesion on prosthetic valve materials and bacteria growth.DESIGN,TIME AND SETTING:Repetitive measurement was performed at the Department of Cardiac and Thoracic Surgery,Third Hospital of Kunming Medical College from January to March 2001.MATERIALS:Terylene(Dacron)was purchased from Man-made Blood Vessel Laboratory of Suzhou Weaves Belt Factory;polytetrafluoroethylene was provided by Teflon-GoreTexW.L.Gore & Associates,Inc.Arizona,USA;pyrolytic carbon was provided by Department of Biological Material of Sichuan Union University;staphylococcus anreus,Escherichia coli,staphylococcus epidermidis,and Pseudomonas aerugmosa were prepared by our laboratory.METHODS:The growth curve of staphylococcus aureus,staphylococcus epidermidis,Escherichia coli and Pseudomonas aeruginosa on dacron,pyrolytic carbon and pelytetrafluoroethylene were quantitatively determined respectively by plate counting and gamma.ray counting of 125 Ⅰ radiolabeled bacteria in vitro.Bacteria growing normally served as control.All bacteria were cultured for 30 hours,and bacteria concentration was determined every 2 hours.In addition,the adhesive capacities of foMr kinds of bacteria on dacron,pyrolytic carbon and polytetrafluoroethylene were detected Escherichia coli and Pseudomonas aeruginosa on dacron,pyrolytic carbon and polytetrafluoroethytene.RESULTS:There were no significant differences in adhesive capacities of each bacterium on dacron,pyrolytic carbon and polytetrafluoroethylene at the same time point(P>0.05).The differences in growth curve of four kinds of bacteria on prosthetic valve materials were not remarkable compared to the control(P>0.05).Different bacteria showed different adhesion degree on the materials:staphylococcus aureus exhibited strongest adhesion on dacron;staphylococcus epidermidis on pyrolytic carbon;Escherichia coli on dacron.The adhesive capacity of Pseudomonas aerugmosa on dacron reached peak within 12 hours,and gradually decreased,but maintained strong adhesion on the other materials.The adhesive capacmes of four bacteria on the materials did not increase or maintain with time.CONCLUSION:The adhesive capacity of one bacterium to different artificial valve materials and different bacteria to one prosthetic valve materials is different.The materials.show little influence on bacterium growth cycle.

Objective This article aimed to explore the inhibitory effect of β-ionone(BI)on the proliferation of breast canc-er cells through the nuclear factor kappa-B(NF-κB)pathway and its possible mechanism.Methods The methylene blue assay and MTT assay were used to determine the viability of breast cancer cells.The malachite green phosphate assay was used to detect the ac-tivity of protein phosphatase 2A(PP2A).Western blot was used to detect the levels of phosphorylated P65(s534 and s311)(p-P65),PP2A(A,B and C),and phosphorylated ataxia telangiectasia mutant(p-ATM)(s1981)protein.Results BI could significant-ly inhibit the proliferation of human breast cancer BT549 cells and MCF-7 cells in a time-and dose-dependent manner,and the difference was statistically significant(P<0.01).After treated with BI,NF-κB activity was significantly inhibited in MCF-7 cells,as shown by a significant decrease in the level of phosphorylated P65(s311 and s534)protein and an increase in the level of PP2A pro-tein,and the difference was statistically significant(P<0.05).In addition,BI also significantly reduced the phosphorylation of P65 protein and ATM protein in MCF-7 cells by the PP2A inhibitor-okada acid(OA).Conclusion This study shows that BI inhibits the proliferation of breast cancer cells by inhibiting NF-κB activity,and its mechanism may be achieved by increasing PP2A activity to regulate the NF-κB pathway.

Objective: To establish a CT image radiomics-based prediction model for the differential diagnosis of silicosis and tuberculosis nodules. Methods: A total of 53 patients with silicosis and 89 patients with tuberculosis who underwent routine CT scans in Suzhou Fifth People's Hospital from January to August, 2018 were enrolled in this study. AK/ITK software was used to segment the images to obtain 139 silicosis lesions and 119 tuberculosis lesions. For each lesion image, 396 features were extracted, and feature dimension reduction was applied to select the most characteristic feature subset. Support vector machine (SVM) , feedforward back propagation neural network (FNN-BP) , and random forest (RF) were implemented using R software (Rstudio V1.1.463) , and the algorithm that achieved the largest area under of the receiver operating characteristic (ROC) curve (AUC) was selected as the final prediction model. Results: RF was the best prediction model for the differential diagnosis of silicosis and tuberculosis nodules, with an accuracy of 83.1%, a sensitivity of 0.76, a specificity of 0.9, and an AUC of 0.917 (95% confidence interval: 0.8431-0.9758) . RF had a significantly larger AUC than SVM and FNN-BP (P<0.05) . Conclusion CT image-based RF prediction model can be used to differentially diagnose silicosis and tuberculosis nodules.