Childhood exposure due to the Chernobyl accident and thyroid cancer risk in contaminated areas of Belarus and Russia

The thyroid dose due to 131I releases during the Chernobyl accident was reconstructed for children and adolescents in two cities and 2122 settlements in Belarus, and in one city and 607 settlements in the Bryansk district of the Russian Federation. In this area, which covers the two high contamination spots in the two countries following the accident, data on thyroid cancer incidence during the period 1991-1995 were analysed in the light of possible increased thyroid surveillance. Two methods of risk analysis were applied: Poisson regression with results for the single settlements and Monte Carlo (MC) calculations for results in larger areas or sub-populations. Best estimates of both methods agreed well. Poisson regression estimates of 95% confidence intervals (CIs) were considerably smaller than the MC results, which allow for extra-Poisson uncertainties due to reconstructed doses and the background thyroid cancer incidence. The excess absolute risk per unit thyroid dose (EARPD) for the birth cohort 1971-1985 by the MC analysis was 2.1 (95% CI 1.0-4.5) cases per 10(4) person-year Gy. The point estimate is lower by a factor of two than that observed in a pooled study of thyroid cancer risk after external exposures. The excess relative risk per unit thyroid dose was 23 (95% CI 8.6-82) Gy(-1). No significant differences between countries or cities and rural areas were found. In the lowest dose group of the settlements with an average thyroid dose of 0.05 Gy the risk was statistically significantly elevated. Dependencies of risks on age-at-exposure and on gender are consistent with findings after external exposures

Metabolic characterization of Palatinate German white wines according to sensory attributes, varieties, and vintages using NMR spectroscopy and multivariate data analyses

1H NMR (nuclear magnetic resonance spectroscopy) has been used for metabolomic analysis of ‘Riesling’ and ‘Mueller-Thurgau’ white wines from the German Palatinate region. Diverse two-dimensional NMR techniques have been applied for the identification of metabolites, including phenolics. It is shown that sensory analysis correlates with NMR-based metabolic profiles of wine. 1H NMR data in combination with multivariate data analysis methods, like principal component analysis (PCA), partial least squares projections to latent structures (PLS), and bidirectional orthogonal projections to latent structures (O2PLS) analysis, were employed in an attempt to identify the metabolites responsible for the taste of wine, using a non-targeted approach. The high quality wines were characterized by elevated levels of compounds like proline, 2,3-butanediol, malate, quercetin, and catechin. Characterization of wine based on type and vintage was also done using orthogonal projections to latent structures (OPLS) analysis. ‘Riesling’ wines were characterized by higher levels of catechin, caftarate, valine, proline, malate, and citrate whereas compounds like quercetin, resveratrol, gallate, leucine, threonine, succinate, and lactate, were found discriminating for ‘Mueller-Thurgau’. The wines from 2006 vintage were dominated by leucine, phenylalanine, citrate, malate, and phenolics, while valine, proline, alanine, and succinate were predominantly present in the 2007 vintage. Based on these results, it can be postulated the NMR-based metabolomics offers an easy and comprehensive analysis of wine and in combination with multivariate data analyses can be used to investigate the source of the wines and to predict certain sensory aspects of wine

Systematic NMR Analysis of Stable Isotope Labeled Metabolite Mixtures in Plant and Animal Systems: Coarse Grained Views of Metabolic Pathways

BACKGROUND: Metabolic phenotyping has become an important 'bird's-eye-view' technology which can be applied to higher organisms, such as model plant and animal systems in the post-genomics and proteomics era. Although genotyping technology has expanded greatly over the past decade, metabolic phenotyping has languished due to the difficulty of 'top-down' chemical analyses. Here, we describe a systematic NMR methodology for stable isotope-labeling and analysis of metabolite mixtures in plant and animal systems. METHODOLOGY/PRINCIPAL FINDINGS: The analysis method includes a stable isotope labeling technique for use in living organisms; a systematic method for simultaneously identifying a large number of metabolites by using a newly developed HSQC-based metabolite chemical shift database combined with heteronuclear multidimensional NMR spectroscopy; Principal Components Analysis; and a visualization method using a coarse-grained overview of the metabolic system. The database contains more than 1000 (1)H and (13)C chemical shifts corresponding to 142 metabolites measured under identical physicochemical conditions. Using the stable isotope labeling technique in Arabidopsis T87 cultured cells and Bombyx mori, we systematically detected >450 HSQC peaks in each (13)C-HSQC spectrum derived from model plant, Arabidopsis T87 cultured cells and the invertebrate animal model Bombyx mori. Furthermore, for the first time, efficient (13)C labeling has allowed reliable signal assignment using analytical separation techniques such as 3D HCCH-COSY spectra in higher organism extracts. CONCLUSIONS/SIGNIFICANCE: Overall physiological changes could be detected and categorized in relation to a critical developmental phase change in B. mori by coarse-grained representations in which the organization of metabolic pathways related to a specific developmental phase was visualized on the basis of constituent changes of 56 identified metabolites. Based on the observed intensities of (13)C atoms of given metabolites on development-dependent changes in the 56 identified (13)C-HSQC signals, we have determined the changes in metabolic networks that are associated with energy and nitrogen metabolism