Methods: Sixty stents were randomly implanted

in 20 Large White female pigs with a ratio of baremetal/drug-eluting stents of 1:2. After 28 days, euthanasia selleck and histomorphometry were performed. We defined the vessel injury score in accordance to whether the internal elastic lamina was intact or ruptured.

Results: There were no differences between drug-eluting stents and bare metal stents in the intact internal elastic lamina group regarding neointimal area or % restenosis (1.3 [1.1-2.2]) vs 2.0 [1.3-2.5] mm(2); P=.6; and 14.0 [12.1-20.8] vs 22.2 [14.1-23.3] %; P=.5). We assessed statistically significant differences for the ruptured internal elastic lamina group, (neointimal area 1.2 [0.8-2.0] vs 2.9 [2.3-3.7] mm(2); P=.001 and % restenosis 16.63 [11.2-23.5] vs 30.4 [26.4-45.7] %; P=.001).

Conclusions: In our swine model, we did not find any differences between proliferative response of drug-eluting stents and bare metal stents when the internal elastic lamina is intact; differences are only found when

vascular injury is deeper.”
“Carcinogenesis is a complex process with multiple genetic and environmental factors contributing to the development of one or more tumors. Understanding the underlying mechanism of this process and identifying related markers to assess the outcome of this process would lead to more directed treatment and thus significantly reduce the mortality rate of cancers. Recently, molecular diagnostics and prognostics based on the

identification 10058-F4 of patterns within gene expression profiles in the context of protein interaction networks were reported. However, the predictive performances of these approaches were limited. In this study we propose a novel integrated approach, named CAERUS, for the identification of gene signatures to predict cancer outcomes based on the domain interaction network in human proteome. We first developed a model to score each protein by quantifying the domain connections to its interacting partners and the somatic mutations present in the domain. We then defined proteins as gene signatures if their scores were above a preset threshold. Next, for https://www.selleckchem.com/products/azd2014.html each gene signature, we quantified the correlation of the expression levels between this gene signature and its neighboring proteins. The results of the quantification in each patient were then used to predict cancer outcome by a modified naive Bayes classifier. In this study we achieved a favorable accuracy of 88.3%, sensitivity of 87.2%, and specificity of 88.9% on a set of well-documented gene expression profiles of 253 consecutive breast cancer patients with different outcomes. We also compiled a list of cancer-associated gene signatures and domains, which provided testable hypotheses for further experimental investigation. Our approach proved successful on different independent breast cancer data sets as well as an ovarian cancer data set.