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“It is difficult to determine preoperatively whether upper/middle thoracic lymphadenectomy is necessary in patients with adenocarcinoma of the esophagogastric

junction (AEG) or lower esophageal CT99021 squamous cell carcinoma (ESCC). Here, we investigated whether stratification based on the location of the proximal end of the tumor, as assessed using preoperative computed tomography (CT) images, would be useful for predicting upper/middle thoracic lymph node involvement for AEG and lower ESCC. A total of 142 patients with AEG and lower ESCC treated by R0-1 surgical resection via a thoracotomy was retrospectively investigated. The location of the proximal end of the tumor in comparison with the vena cava foramen (VCF) was decided by inspecting preoperative CT images and then correlated with upper/middle thoracic lymph node involvement. The incidence of upper/middle thoracic lymph node involvement was low in AEG and ESCC tumors having proximal ends below the VCF (0 %, 0 of 13, and 5.9 %, 1 of 17, for AEG and ESCC, respectively).

In contrast, when the tumors’ proximal ends were above the VCF, patients had higher frequencies of upper/middle thoracic lymph node involvement (36.4 %, 8 of 22, and 37.8 %, 34 of 90, for AEG and ESCC, respectively). Multivariate analysis showed that the location of the proximal end of the tumor is an independent risk factor related to upper/middle thoracic lymph node involvement (odds ratio 14.3, 95 % confidence interval 1.76-111, p = 0.013), whereas other clinical factors (cT, cN, tumor length, BTSA1 order and histologic types) are not. This manner of stratification buy SYN-117 using preoperative CT images could be useful in deciding

the extent of thoracic lymphadenectomy in both AEG and ESCC.”
“Measuring biomarkers from plant tissue samples is challenging and expensive when the desire is to integrate transcriptomics, fluxomics, metabolomics, lipidomics, proteomics, physiomics and phenomics. We present a computational biology method where only the transcriptome needs to be measured and is used to derive a set of parameters for deterministic kinetic models of metabolic pathways. The technology is called Transcriptome-To-Metabolome (TTM) biosimulations, currently under commercial development, but available for non-commercial use by researchers. The simulated results on metabolites of 30 primary and secondary metabolic pathways in rice (Oryza sativa) were used as the biomarkers to predict whether the transcriptome was from a plant that had been under drought conditions. The rice transcriptomes were accessed from public archives and each individual plant was simulated. This unique quality of the TTM technology allows standard analyses on biomarker assessments, i.e. sensitivity, specificity, positive and negative predictive values, accuracy, receiver operator characteristics (ROC) curve and area under the ROC curve (AUC).