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Many Markers (many + marker)

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Life Sciences	50%

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Activity of CuZn-superoxide dismutase, catalase and glutathione peroxidase in erythrocytes in kidney allografts during reperfusion in patients with and without delayed graft function

CLINICAL TRANSPLANTATION, Issue 1 2006
L Doma
Abstract:, Background:, Generation of reactive oxygen species (ROS) is the main mechanism involved in the ischemic/reperfusion damage of the transplanted organ. Oxygen burst is a trigger for complex biochemical events leading to generation of oxygenated lipids and changes in microcirculation. Many markers have been researched to prove the presence of ROS in the transplanted tissue. Some of them, like superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx) are considered to play a major role in graft protection against oxygen stress during reperfusion. Methods:, The aim of this study was to examine the changes of SOD1, CAT and GPx activity in erythrocytes during the first minutes after total graft reperfusion. Forty patients undergoing kidney transplantation at our center were assigned to two groups: with or without delayed graft function (DGF). Before anastomosing kidney vessels with recipient's iliac vessels, the ,0' blood sample was taken from the iliac vein. Next blood samples I, II and III were taken from the graft's renal vein. The reperfusion of the transplanted kidney was evaluated precisely with the thermovision camera. Erythrocyte SOD1, CAT and GPx activity was measured with a spectrophotometric method. Results:, We did not observe statistically significant changes in SOD1, CAT and GPx activity in erythrocytes during the early phase of reperfusion in patients with and without DGF. Conclusions:, Erythrocyte-antioxidative system in graft's vein remain stable during the early phase of reperfusion. The results of the study suggest that further studies on extracellular enzymes are required for the assessment of antioxidant system in the conditions of ischemia/reperfusion. [source]

Maximum-likelihood estimation of haplotype frequencies in nuclear families

GENETIC EPIDEMIOLOGY, Issue 1 2004
Tim Becker
Abstract The importance of haplotype analysis in the context of association fine mapping of disease genes has grown steadily over the last years. Since experimental methods to determine haplotypes on a large scale are not available, phase has to be inferred statistically. For individual genotype data, several reconstruction techniques and many implementations of the expectation-maximization (EM) algorithm for haplotype frequency estimation exist. Recent research work has shown that incorporating available genotype information of related individuals largely increases the precision of haplotype frequency estimates. We, therefore, implemented a highly flexible program written in C, called FAMHAP, which calculates maximum likelihood estimates (MLEs) of haplotype frequencies from general nuclear families with an arbitrary number of children via the EM-algorithm for up to 20 SNPs. For more loci, we have implemented a locus-iterative mode of the EM-algorithm, which gives reliable approximations of the MLEs for up to 63 SNP loci, or less when multi-allelic markers are incorporated into the analysis. Missing genotypes can be handled as well. The program is able to distinguish cases (haplotypes transmitted to the first affected child of a family) from pseudo-controls (non-transmitted haplotypes with respect to the child). We tested the performance of FAMHAP and the accuracy of the obtained haplotype frequencies on a variety of simulated data sets. The implementation proved to work well when many markers were considered and no significant differences between the estimates obtained with the usual EM-algorithm and those obtained in its locus-iterative mode were observed. We conclude from the simulations that the accuracy of haplotype frequency estimation and reconstruction in nuclear families is very reliable in general and robust against missing genotypes. © 2004 Wiley-Liss, Inc. [source]

Studies on the cell treatment conditions to elicit lipolytic responses from 3T3-L1 adipocytes to TCDD, 2,3,7,8-tetrachlorodibenzo-p-dioxin

JOURNAL OF CELLULAR BIOCHEMISTRY, Issue 2 2007
Wen Li
Abstract Wasting syndrome is one of the hallmark symptoms of poisoning by TCDD (=dioxin), which is associated with the massive loss of adipose tissue and serum hyperlipidemia in vivo. Yet, the most widely used in vitro cell model 3T3-L1 adipocyte has not been useful for studying such an action of TCDD because of the difficulty of inducing their mature adipocytes to respond to TCDD to go through lipolysis. Here, we made efforts to find the right cell culture and treatment conditions to induce mature 3T3-L1 adipocytes to go through lipolysis, which is defined as events leading to reduction of lipids in adipocytes. The optimum condition was found to require 7-day differentiated adipocytes being subjected to DMEM medium containing TCDD (but without insulin) for 5 day incubation with two medium changes (the same composition) on incubation days 2 and 4. After 24 h, the early effect of TCDD on adipocytes was predominantly on inflammation, particularly induction of COX-2 and KC (IL-8), which is accompanied by upregulation of C/EBP, and ,. The sign of TCDD-induced lipolysis starts slowly and by incubation day 3, a few markers showed modestly significant changes. By day 5 of incubation, however, many markers show highly significant signs of lipolytic changes. Although this process could take place without exogenous macrophages or their cytokines, addition of exogenous TNF, considerably synergized this action of TCDD. In conclusion, under a right condition, 3T3-L1 adipocytes were found to respond to TCDD to go through lipolysis. The early trigger of such a response appears to be activation of COX-2, which is amplified by TNF,. J. Cell. Biochem. 102: 389,402, 2007. © 2007 Wiley-Liss, Inc. [source]

Application of the Time-Dependent ROC Curves for Prognostic Accuracy with Multiple Biomarkers

BIOMETRICS, Issue 1 2006
Yingye Zheng
Summary The rapid advancement in molecule technology has led to the discovery of many markers that have potential applications in disease diagnosis and prognosis. In a prospective cohort study, information on a panel of biomarkers as well as the disease status for a patient are routinely collected over time. Such information is useful to predict patients' prognosis and select patients for targeted therapy. In this article, we develop procedures for constructing a composite test with optimal discrimination power when there are multiple markers available to assist in prediction and characterize the accuracy of the resulting test by extending the time-dependent receiver operating characteristic (ROC) curve methodology (Heagerty, Lumley, and Pepe, 2000, Biometrics56, 337,344). We employ a modified logistic regression model to derive optimal linear composite scores such that their corresponding ROC curves are maximized at every false positive rate. We provide theoretical justification for using such a model for prognostic accuracy. The proposed method allows for time-varying marker effects and accommodates censored failure time outcome. When the effects of markers are approximately constant over time, we propose a more efficient estimating procedure under such models. We conduct numerical studies to evaluate the performance of the proposed procedures. Our results indicate the proposed methods are both flexible and efficient. We contrast these methods with an application concerning the prognostic accuracies of expression levels of six genes. [source]