Anti-KSR1 monoclonal antibody, clone MINT/1/14

The importance of automatic diagnostic systems for nuclear power plants (NPPs) has been discussed in numerous studies, and various such systems have been proposed. None of those systems were designed to predict the severity of the diagnosed scenario. A classification and severity prediction system for NPP transients is developed. The system is based on nearest neighbors modeling, which is optimized using genetic algorithms. The optimization process is used to determine the most important variables for each of the transient types analyzed. An enhanced version of the genetic algorithms is used in which a local downhill search is performed to further increase the accuracy achieved. The genetic algorithms search was implemented on a massively parallel supercomputer, the KSR1-64, to perform the analysis in a reasonable time. The data for this study were supplied by the high-fidelity simulator of the San Onofre unit I pressurized water reactor.

BackgroundThe current prognosis of thymic epithelial tumors (TETs) is according to the World Health Organization (WHO) histologic classification and the Masaoka staging system. These methods of prognosis have certain limitations in clinical application and there is a need to seek new method for determining the prognosis of patients with TETs. To date, there have been no studies done on the use of DNA methylation biomarkers for prognosis of TETs. The present study was therefore carried out to identify DNA methylation biomarkers that can determine the overall survival in patients with TETs.MethodsBioinformatic analysis of TCGA 450K methylation array data, transcriptome sequencing data, WHO histologic classification and Masaoka staging system was performed to identify differentially expressed methylation sites between thymoma and thymic carcinoma as well as the different DNA methylation sites associated with the overall survival in patients with TETs. Using pyrosequencing, 4 different methylation sites (cg05784862, cg07154254, cg02543462, and cg06288355) were sequenced from tumor tissues of 100 Chinese patients with TETs. A prognostic model for TETs was constructed using these four methylation sites.ResultsThe TCGA dataset showed 5155 and 6967 hyper- and hypomethylated CpG sites in type A-B3 group and type C group, respectively, of which 3600 were located within the gene promoter regions. One hundred thirty-four genes were silenced by promoter hypermethylation and 174 mRNAs were upregulated. Analysis of univariate and multivariate Cox regression showed significant association between the methylation levels of 187 sites and the overall survival in patients with TETs. cg05784862(KSR1), cg07154254(ELF3), cg02543462(ILRN), and cg06288355(RAG1) were identified as independent prognostic factors for overall survival in patients with TETs after adjusting for Masaoka staging in 100 Chinese patients. The prognostic model which consists of the four abovementioned genes had higher accuracy for predicting the 5-year overall survival in patients with TETs as compared to the Masaoka clinical staging. (Time-dependent ROC analysis AUC 1.000 vs 0.742, P=2.7x10(-6)).ConclusionsThe methylation levels of cg05784862(KSR1), cg07154254(ELF3), cg02543462(ILRN), and cg06288355(RAG1) sites are associated with the progression of TETs and may serve as new biomarkers for predicting the overall survival in patients with TETs.