Inflammation induces fibrinogen nitration in experimental human endotoxemia.

Elevated plasma fibrinogen is a prothrombotic risk factor for cardiovascular disease (CVD). Recent small studies report that fibrinogen oxidative modifications, specifically tyrosine residue nitration, can occur in inflammatory states and may modify fibrinogen function. HDL cholesterol is inversely related to CVD and suggested to reduce the oxidation of LDL cholesterol, but whether these antioxidant functions extend to fibrinogen modifications is unknown.

Atheroprotective lipoprotein effects of a niacin-simvastatin combination compared to low- and high-dose simvastatin monotherapy.

Background: Niacin has multiple lipoprotein effects that may provide cardiovascular benefit when added to statin monotherapy.

Methods: In this randomized, placebo-controlled trial (n = 75) of magnetic resonance imaging of carotid atherosclerosis, we performed a secondary comparison of combination niacin-statin (simvastatin 20 mg/Niacin-ER 2G [S20/N]) to monotherapy with moderate (20 mg [S20]) and high-dose (80 mg [S80]) simvastatin on lipids, apolipoproteins (apo), low density lipoprotein (LDL) and high density lipoprotein (HDL) particle subclasses, and inflammatory markers.

Results: At baseline, average age was 71, 72% were male, 62.

Multi-class alignment of LC-MS data using probabilistic-based mixture regression models.

In this paper, a framework of probabilistic-based mixture regression models (PMRM) is presented for multi-class alignment of liquid chromatography-mass spectrometry (LC-MS) data. The proposed framework performs the alignment in both time and measurement spaces of the LC-MS spectra. The expectation maximization (EM) algorithm is used to estimate the joint parameters of spline-based mixture regression models and prior transformation densities.

Singular value decomposition-based regression identifies activation of endogenous signaling pathways in vivo.

The ability to detect activation of signaling pathways based solely on gene expression data represents an important goal in biological research. We tested the sensitivity of singular value decomposition-based regression by focusing on functional interactions between the Ras and transforming growth factor beta signaling pathways. Our findings demonstrate that this approach is sufficiently sensitive to detect the secondary activation of endogenous signaling pathways as it occurs through crosstalk following ectopic activation of a primary pathway.

Modeling genetic inheritance of copy number variations.

Copy number variations (CNVs) are being used as genetic markers or functional candidates in gene-mapping studies. However, unlike single nucleotide polymorphism or microsatellite genotyping techniques, most CNV detection methods are limited to detecting total copy numbers, rather than copy number in each of the two homologous chromosomes. To address this issue, we developed a statistical framework for intensity-based CNV detection platforms using family data.

Resistin gene variation is associated with systemic inflammation but not plasma adipokine levels, metabolic syndrome or coronary atherosclerosis in nondiabetic Caucasians.

Objective: Resistin causes insulin resistance and diabetes in mice whereas in humans it is linked to inflammation and atherosclerosis. Few human genetic studies of resistin in inflammation and atherosclerosis have been performed. We hypothesized that the -420C>G putative gain-of-function resistin variant would be associated with inflammatory markers and atherosclerosis but not with metabolic syndrome or adipokines in humans.

Screening for antibacterial and antifungal activities in marine benthic invertebrates from northern Norway.

Benthic marine invertebrates collected from sub-Arctic regions of northern Norway, were found to be a promising source of novel bioactive compounds against human and fish pathogenic bacteria and fungi. Lyophilized material from seven species of ascidians, six sponges and one soft alcyonid coral were extracted with 60% acidified acetonitrile (ACN). After separation into an ACN-rich phase (ACN-extract) and an aqueous phase, and subsequent solid-phase extraction of the aqueous phase, fractions differing in polarity were obtained and screened for antibacterial and antifungal activities, along with the more lipophilic ACN-extracts.

Adipokines, insulin resistance, and coronary artery calcification.

Objectives: We evaluated the hypothesis that plasma levels of adiponectin and leptin are independently but oppositely associated with coronary artery calcification (CAC), a measure of subclinical atherosclerosis. In addition, we assessed which biomarkers of adiposity and insulin resistance are the strongest predictors of CAC beyond traditional risk factors, metabolic syndrome, and plasma C-reactive protein (CRP).

Background: Adipokines are fat-secreted biomolecules with pleiotropic actions that converge in diabetes and cardiovascular disease.

Building future scenarios and uncovering persisting challenges of participatory forest management in Chilimo Forest, Central Ethiopia.

We examined the changes in forest status and people's livelihoods through building future scenarios for Chilimo Forest in Central Ethiopia where participatory forest management (PFM) is being implemented. Participatory methods were employed to collect data, and a dynamic modeling technique was applied to explore trends over time. By integrating the more quantitative model outputs with qualitative insights, information on forests and livelihoods was summarized and returned to users, both to inform them and get feedback.

The influence of pravastatin and atorvastatin on markers of oxidative stress in hypercholesterolemic humans.

Objectives: The aim of this study was to determine the effects of pravastatin and atorvastatin on markers of oxidative stress in plasma.

Background: Hydroxymethylglutaryl coenzyme A reductase inhibitors reduce low-density lipoprotein cholesterol (LDL-C) and cardiovascular risk, but their effects on circulating biomarkers of oxidative stress are not well-defined.

Methods: Hypercholesterolemic subjects (n = 120, ages 21 to 80 years with LDL-C 130 to 220 mg/dl) were randomized in a double-blind, parallel design to pravastatin 40 mg/day (prava40), atorvastatin 10 mg/day (atorva10), atorvastatin 80 mg/day (atorva80), or placebo.

An assessment of the relative contributions of redox and steric issues to laccase specificity towards putative substrates.

Laccases catalyze the one-electron oxidation of a broad range of substrates coupled to the 4 electron reduction of O2 to H2O. Phenols are typical substrates, because their redox potentials (ranging from 0.5 to 1.

Identification of cord blood-derived mesenchymal stem/stromal cell populations with distinct growth kinetics, differentiation potentials, and gene expression profiles.

Phenotypic heterogeneity has been observed among mesenchymal stem/stromal cell (MSC) populations, but specific genes associated with this variability have not been defined. To study this question, we analyzed two distinct isogenic MSC populations isolated from umbilical cord blood (UCB1 and UCB2). The use of isogenic populations eliminated differences contributed by genetic background.

Identifying biomarkers from mass spectrometry data with ordinal outcome.

In recent years, there has been an increased interest in using protein mass spectroscopy to identify molecular markers that discriminate diseased from healthy individuals. Existing methods are tailored towards classifying observations into nominal categories. Sometimes, however, the outcome of interest may be measured on an ordered scale.

Bayesian variable selection for the analysis of microarray data with censored outcomes.

Motivation: A common task in microarray data analysis consists of identifying genes associated with a phenotype. When the outcomes of interest are censored time-to-event data, standard approaches assess the effect of genes by fitting univariate survival models. In this paper, we propose a Bayesian variable selection approach, which allows the identification of relevant markers by jointly assessing sets of genes.

Bayesian error-in-variable survival model for the analysis of GeneChip arrays.

DNA microarrays in conjunction with statistical models may help gain a deeper understanding of the molecular basis for specific diseases. An intense area of research is concerned with the identification of genes related to particular phenotypes. The technology, however, is subject to various sources of error that may lead to expression readings that are substantially different from the true transcript levels.

Novel genomic targets in oxidant-induced vascular injury.

To study the complex interaction between oxidative injury and the pathogenesis of vascular disease, vascular gene expression was examined in male Sprague-Dawley rats given 35 or 70 mg/kg allylamine, a synthetic amine converted to acrolein and hydrogen peroxide within the vascular wall. Vascular lesions and extensive vascular remodeling, coupled to increased production of 8-epi-PGF2alpha, nuclear localization of NFkappaB, and alterations in glutathione homeostasis, were observed in animals treated with allylamine for up to 20 days. Transcriptional profiling, immunohistochemistry, and in situ hybridization showed that genes involved in adhesion and extracellular matrix (ECM) (alpha(1) integrin, collagen), cytoskeletal rearrangements (alpha-smooth muscle actin, alpha-tropomyosin), and signal transduction (NFkappaB, osteopontin, and LINE) were altered by oxidant treatment.

Wavelet thresholding with bayesian false discovery rate control.

The false discovery rate (FDR) procedure has become a popular method for handling multiplicity in high-dimensional data. The definition of FDR has a natural Bayesian interpretation; it is the expected proportion of null hypotheses mistakenly rejected given a measure of evidence for their truth. In this article, we propose controlling the positive FDR using a Bayesian approach where the rejection rule is based on the posterior probabilities of the null hypotheses.

Bayesian variable selection in multinomial probit models to identify molecular signatures of disease stage.

Here we focus on discrimination problems where the number of predictors substantially exceeds the sample size and we propose a Bayesian variable selection approach to multinomial probit models. Our method makes use of mixture priors and Markov chain Monte Carlo techniques to select sets of variables that differ among the classes. We apply our methodology to a problem in functional genomics using gene expression profiling data.

A bayesian hierarchical model for the analysis of Affymetrix arrays.

An area of active research in DNA microarray analysis focuses on identifying differentially expressed genes between normal and malignant tissues. The analysis is complicated by the presence of several unreliable expression readings. Here, we illustrate a methodology where the expression estimates are modeled as censored data and discriminating genes are selected using ANOVA-based criteria.

Making sense of molecular signatures in the immune system.

The development of Functional Genomics technologies has opened new avenues to investigate the complexity of the immune system. Microarray technology has been particularly successful because of its relatively low cost and high genome coverage. Consequently to our ability to monitor the expression of a significant proportion of an organism genome, our understanding of the molecular dynamics behind cell differentiation and cell response has greatly improved.

Identification of DNA regulatory motifs using Bayesian variable selection.

Motivation: Understanding the mechanisms that determine gene expression regulation is an important and challenging problem. A common approach consists of identifying DNA-binding sites from a collection of co-regulated genes and their nearby non-coding DNA sequences. Here, we consider a regression model that linearly relates gene expression levels to a sequence matching score of nucleotide patterns.

Unraveling gene-gene interactions regulated by ligands of the aryl hydrocarbon receptor.

The co-expression of genes coupled to additive probabilistic relationships was used to identify gene sets predictive of the complex biological interactions regulated by ligands of the aryl hydrocarbon receptor ((Italic)Ahr(/Italic)). To maximize the number of possible gene-gene combinations, data sets from murine embryonic kidney, fetal heart, and vascular smooth muscle cells challenged (Italic)in vitro(/Italic) with ligands of the (Italic)Ahr(/Italic) were used to create predictor/training data sets. Biologically relevant gene predictor sets were calculated for (Italic)Ahr(/Italic), cytochrome P450 1B1, insulin-like growth factor-binding protein-5, lysyl oxidase, and osteopontin.

Identification of differentially expressed genes in high-density oligonucleotide arrays accounting for the quantification limits of the technology.

In DNA microarray analysis, there is often interest in isolating a few genes that best discriminate between tissue types. This is especially important in cancer, where different clinicopathologic groups are known to vary in their outcomes and response to therapy. The identification of a small subset of gene expression patterns distinctive for tumor subtypes can help design treatment strategies and improve diagnosis.

Genomic profiles and predictive biological networks in oxidant-induced atherogenesis.

Atherogenic stimuli trigger complex responses in vascular smooth muscle cells (VSMCs) that culminate in activation/repression of overlapping signal transduction cascades involving oxidative stress. In the case of benzo[a]pyrene (BaP), a polycyclic aromatic hydrocarbon present in tobacco smoke, the atherogenic response involves interference with redox homeostasis by oxidative intermediates of BaP metabolism. The present studies were conducted to define genomic profiles and predictive gene biological networks associated with the atherogenic response of murine (aortic) VSMCs to BaP.

Race, Candida sepsis, and retinopathy of prematurity.

The objective of this observational cohort study at Georgetown University Hospital from January 1, 1994 through December 31, 1997 was to investigate race, Candida sepsis, and duration of oxygen exposure in infants with retinopathy of prematurity (ROP) with birth weight < or = 1,000 g. The incidence of ROP was 70.8% (114/161).