Profoundly reduced neovascularization capacity of bone marrow mononuclear cells derived from patients with chronic ischemic heart disease

Background— Cell therapy with bone marrow–derived stem/progenitor cells is a novel option for improving neovascularization and cardiac function in ischemic heart disease. Circulating endothelial progenitor cells in patients with coronary heart disease are impaired with respect to number and functional activity. However, whether this impairment also extends to bone marrow–derived mononuclear cells (BM-MNCs) in patients with chronic ischemic cardiomyopathy (ICMP) is unclea

Vitamin C inhibits endothelial cell apoptosis in congestive heart failure

Background - Proinflammatory cytokines like tumor necrosis factor- and oxidative stress induce apoptotic cell death in endothelial cells (ECs). Systemic inflammation and increased oxidative stress in congestive heart failure (CHF) coincide with enhanced EC apoptosis and the development of endothelial dysfunction. Therefore, we investigated the effects of antioxidative vitamin C therapy on EC apoptosis in CHF patients. Methods and Results - Vitamin C dose dependently suppressed the induction of EC apoptosis by tumor necrosis factor- and angiotensin II in vitro as assessed by DNA fragmentation, DAPI nuclear staining, and MTT viability assay. The antiapoptotic effect of vitamin C was associated with reduced cytochrome C release from mitochondria and the inhibition of caspase-9 activity. To assess EC protection by vitamin C in CHF patients, we prospectively randomized CHF patients in a double-blind trial to vitamin C treatment versus placebo. Vitamin C administration to CHF patients markedly reduced plasma levels of circulating apoptotic microparticles to 32±8% of baseline levels, whereas placebo had no effect (87±14%, P<0.005). In addition, vitamin C administration suppressed the proapoptotic activity on EC of the serum of CHF patients (P<0.001). Conclusions - Administration of vitamin C to CHF patients suppresses EC apoptosis in vivo, which might contribute to the established functional benefit of vitamin C supplementation on endothelial function

Assessment of the tissue distribution of transplanted human endothelial progenitor cells by radioactive labeling

Background— Transplantation of endothelial progenitor cells (EPCs) improves vascularization and left ventricular function after experimental myocardial ischemia. However, tissue distribution of transplanted EPCs has not yet been monitored in living animals. Therefore, we tested whether radioactive labeling allows us to detect injected EPCs

Transplantation of progenitor cells and regeneration enhancement in acute myocardial infarction - (TOPCARE-AMI)

Background - Experimental studies suggest that transplantation of blood-derived or bone marrow–derived progenitor cells beneficially affects postinfarction remodeling. The safety and feasibility of autologous progenitor cell transplantation in patients with ischemic heart disease is unknown

From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles

The inference of network topologies from relational data is an important problem in data analysis. Exemplary applications include the reconstruction of social ties from data on human interactions, the inference of gene co-expression networks from DNA microarray data, or the learning of semantic relationships based on co-occurrences of words in documents. Solving these problems requires techniques to infer significant links in noisy relational data. In this short paper, we propose a new statistical modeling framework to address this challenge. It builds on generalized hypergeometric ensembles, a class of generative stochastic models that give rise to analytically tractable probability spaces of directed, multi-edge graphs. We show how this framework can be used to assess the significance of links in noisy relational data. We illustrate our method in two data sets capturing spatio-temporal proximity relations between actors in a social system. The results show that our analytical framework provides a new approach to infer significant links from relational data, with interesting perspectives for the mining of data on social systems.Comment: 10 pages, 8 figures, accepted at SocInfo201

Stochastic Gradient MCMC for Nonlinear State Space Models

State space models (SSMs) provide a flexible framework for modeling complex time series via a latent stochastic process. Inference for nonlinear, non-Gaussian SSMs is often tackled with particle methods that do not scale well to long time series. The challenge is two-fold: not only do computations scale linearly with time, as in the linear case, but particle filters additionally suffer from increasing particle degeneracy with longer series. Stochastic gradient MCMC methods have been developed to scale inference for hidden Markov models (HMMs) and linear SSMs using buffered stochastic gradient estimates to account for temporal dependencies. We extend these stochastic gradient estimators to nonlinear SSMs using particle methods. We present error bounds that account for both buffering error and particle error in the case of nonlinear SSMs that are log-concave in the latent process. We evaluate our proposed particle buffered stochastic gradient using SGMCMC for inference on both long sequential synthetic and minute-resolution financial returns data, demonstrating the importance of this class of methods