Nitric oxide synthase inhibition in sepsis? Lessons learned from large-animal studies

Nitric Oxide (NO) plays a controversial role in the pathophysiology of sepsis and septic shock. Its vasodilatory effects are well known, but it also has pro- and antiinflammatory properties, assumes crucial importance in antimicrobial host defense, may act as an oxidant as well as an antioxidant, and is said to be a vital poison for the immune and inflammatory network. Large amounts of NO and peroxynitrite are responsible for hypotension, vasoplegia, cellular suffocation, apoptosis, lactic acidosis, and ultimately multiorgan failure. Therefore, NO synthase (NOS) inhibitors were developed to reverse the deleterious effects of NO. Studies using these compounds have not met with uniform success however, and a trial using the nonselective NOS inhibitor N-G-methyl-L-arginine hydrochloride was terminated prematurely because of increased mortality in the treatment arm despite improved shock resolution. Thus, the issue of NOS inhibition in sepsis remains a matter of debate. Several publications have emphasized the differences concerning clinical applicability of data obtained from unresuscitated, hypodynamic rodent models using a pretreatment approach versus resuscitated, hyperdynamic models in high-order species using posttreatment approaches. Therefore, the present review focuses on clinically relevant large-animal studies of endotoxin or living bacteria-induced, hyperdynamic models of sepsis that integrate standard day-today care resuscitative measures.

GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

Background The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease. Results We show that GPNN has high power to detect even relatively small genetic effects (2–3% heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (

GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

Background: The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease. Results: We show that GPNN has high power to detect even relatively small genetic effects (2-3% heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (

Differential Priors for Elastic Nets

The elastic net and related algorithms, such as generative topographic mapping, are key methods for discretized dimension-reduction problems. At their heart are priors that specify the expected topological and geometric properties of the maps. However, up to now, only a very small subset of possible priors has been considered. Here we study a much more general family originating from discrete, high-order derivative operators. We show theoretically that the form of the discrete approximation to the derivative used has a crucial influence on the resulting map. Using a new and more powerful iterative elastic net algorithm, we confirm these results empirically, and illustrate how different priors affect the form of simulated ocular dominance columns.