995 resultados para Genes, Synthetic


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A rapid liquid chromatographic-tandem mass spectrometric (LC-MS/MS) multi-residue method for the simultaneous quantitation and identification of sixteen synthetic growth promoters and bisphenol A in bovine milk has been developed and validated. Sample preparation was straightforward, efficient and economically advantageous. Milk was extracted with acetonitrile followed by phase separation with NaCl. After centrifugation, the extract was purified by dispersive solid-phase extraction with C18 sorbent material. The compounds were analysed by reversed-phase LC-MS/MS using both positive and negative ionization and operated in multiple reaction monitoring (MRM) mode, acquiring two diagnostic product ions from each of the chosen precursor ions for unambiguous confirmation. Total chromatographic run time was less than 10 min for each sample. The method was validated at a level of 1 mu g L-1. A wide variety of deuterated internal standards were used to improve method performance. The accuracy and precision of the method were satisfactory for all analytes. The confirmative quantitative liquid chromatographic tandem mass spectrometric (LC-MS/MS) method was validated according to Commission Decision 2002/657/EC. The decision limit (CC alpha) and the detection capability (CC beta) were found to be below the chosen validation level of 1 mu g L-1 for all compounds. (C) 2010 Elsevier B.V. All rights reserved.

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Background: Doxorubicin is one of the most effective anti-cancer drugs but its use is limited by cumulative cardiotoxicity that restricts lifetime dose. Redox damage is one of the most accepted mechanisms of toxicity, but not fully substantiated. Moreover doxorubicin is not an efficient redox cycling compound due to its low redox potential. Here we used genomic and chemical systems approaches in vivo to investigate the mechanisms of doxorubicin cardiotoxicity, and specifically test the hypothesis of redox cycling mediated cardiotoxicity.

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Several studies have provided compelling evidence implicating the Notch signalling pathway in diabetic nephropathy. Co-regulation of Notch signalling pathway genes with GREM1 has recently been demonstrated and several genes involved in the Notch pathway are differentially expressed in kidney biopsies from individuals with diabetic nephropathy. We assessed single-nucleotide polymorphisms (SNPs; n = 42) in four of these key genes (JAG1, HES1, NOTCH3 and ADAM10) for association with diabetic nephropathy using a case-control design.
Tag SNPs and potentially functional SNPs were genotyped using Sequenom or Taqman technologies in a total of 1371 individuals with type 1 diabetes (668 patients with nephropathy and 703 controls without nephropathy). Patients and controls were white and recruited from the UK and Ireland. Association analyses were performed using PLINK (http://pngu.mgh.harvard.edu/similar to purcell/plink/) and haplotype frequencies in patients and controls were compared. Adjustment for multiple testing was performed by permutation testing.
In analyses stratified by centre, we identified six SNPs, rs8708 and rs11699674 (JAG1), rs10423702 and rs1548555 (NOTCH3), rs2054096 and rs8027998 (ADAM10) as being associated with diabetic nephropathy before, but not after, adjustment for multiple testing. Haplotype and subgroup analysis according to duration of diabetes also failed to find an association with diabetic nephropathy.
Our results suggest that common variants in JAG1, HES1, NOTCH3 and ADAM10 are not strongly associated with diabetic nephropathy in type 1 diabetes among white individuals. Our findings, however, cannot entirely exclude these genes from involvement in the pathogenesis of diabetic nephropathy.

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This paper presents an analysis of entropy-based molecular descriptors. Specifically, we use real chemical structures, as well as synthetic isomeric structures, and investigate properties of and among descriptors with respect to the used data set by a statistical analysis. Our numerical results provide evidence that synthetic chemical structures are notably different to real chemical structures and, hence, should not be used to investigate molecular descriptors. Instead, an analysis based on real chemical structures is favorable. Further, we find strong hints that molecular descriptors can be partitioned into distinct classes capturing complementary information.

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We develop an approach utilizing randomized genotypes to rigorously infer causal regulatory relationships among genes at the transcriptional level, based on experiments in which genotyping and expression profiling are performed. This approach can be used to build transcriptional regulatory networks and to identify putative regulators of genes. We apply the method to an experiment in yeast, in which genes known to be in the same processes and functions are recovered in the resulting transcriptional regulatory network.