We defined a poor initial response to darbepoetin alfa (which occurred in 471 patients) as the lowest quartile of percent change in hemoglobin level (<2%) after the first two standardized CP-690550 order doses of the drug.

Results: Patients who had

a poor initial response to darbepoetin alfa had a lower average hemoglobin level at 12 weeks and during follow-up than did patients with a better hemoglobin response (a change in hemoglobin level ranging from 2 to 15% or more) (P<0.001 for both comparisons), despite receiving higher doses of darbepoetin alfa (median dose, 232 microg vs. 167 microg; P<0.001). Patients with a poor response, as compared with those with a better response, had higher rates of the composite cardiovascular end point (adjusted hazard ratio, TH-302 nmr 1.31; 95% confidence interval [CI], 1.09 to 1.59) or death (adjusted hazard ratio, 1.41; 95% CI, 1.12 to 1.78).

Conclusions: A poor initial hematopoietic response to darbepoetin alfa was associated with an increased subsequent risk of death or cardiovascular events as doses were escalated to meet target hemoglobin levels. Although the mechanism of this differential effect is not

known, these findings raise concern about current target-based strategies for treating anemia in patients with chronic kidney disease. (Funded by Amgen; ClinicalTrials.gov number, NCT00093015.)

N Engl J Med 2010;363:1146-55.”
“Gene regulatory networks (GRN) are being studied with increasingly precise quantitative tools and can provide a testing ground for ideas regarding the emergence and evolution of complex biological networks. We analyze the global statistical

properties of the transcriptional regulatory network of the prokaryote Escherichia coli, identifying each operon with a node of the network. We propose a null model for this network using the content-based approach applied earlier to the eukaryote Saccharomyces cerevisiae (Balcan et al., 2007). Random sequences that represent promoter regions Docetaxel clinical trial and binding sequences are associated with the nodes. The length distributions of these sequences are extracted from the relevant databases. The network is constructed by testing for the occurrence of binding sequences within the promoter regions. The ensemble of emergent networks yields an exponentially decaying in-degree distribution and a putative power law dependence for the out-degree distribution with a flat tail, in agreement with the data. The clustering coefficient, degree-degree correlation, rich club coefficient and k-core visualization all agree qualitatively with the empirical network to an extent not yet achieved by any other computational model, to our knowledge. The significant statistical differences can point the way to further research into non-adaptive and adaptive processes in the evolution of the E. coli GRN. (C) 2009 Elsevier Ltd. All rights reserved.