Inhibitory Effects of Green Tea Polyphenols on Microbial Metabolism of Aromatic Amino Acids in Humans Revealed by Metabolomic Analysis.

The bioactivities and potential health benefits of green tea polyphenols (GTP) have been extensively investigated, but the metabolic impact of chronic GTP intake on humans is not well defined. In this study, fecal and urine samples from postmenopausal female subjects taking a GTP supplement or placebo for 12 months were compared by liquid chromatography-mass spectrometry-based metabolomic analysis. The GTP-derived and GTP-responsive metabolites were identified and characterized by structural elucidation and quantitative analysis of the metabolites contributing to the separation of control and treatment samples in the multivariate models. Major GTP and their direct sulfate and glucuronide metabolites were absent in feces and urine. In contrast, GTP-derived phenyl-γ-valerlactone and phenylvaleric acid metabolites were identified as the most abundant GTP-derived metabolites in feces and urine, suggesting extensive microbial biotransformation of GTP in humans. Interestingly, GTP decreased the levels of microbial metabolites of aromatic amino acids (AAA), including indoxyl sulfate, phenylacetylglutamine, and hippuric acid, in urine. However, it did not affect the levels of AAA, as well as other microbial metabolites, including short-chain fatty acids and secondary bile acids, in feces. 16S rRNA gene sequencing indicated that the fecal microbiome was not significantly affected by chronic consumption of GTP. Overall, microbial metabolism is responsible for the formation of GTP metabolites while GTP metabolism may inhibit the formation of AAA metabolites from microbial metabolism. Because these GTP-derived and GTP-responsive metabolites have diverse bioactivities, microbial metabolism of GTP and AAA may play important roles in the beneficial health effects of green tea consumption in humans.

Abdominal multi-organ segmentation with organ-attention networks and statistical fusion.

Accurate and robust segmentation of abdominal organs on CT is essential for many clinical applications such as computer-aided diagnosis and computer-aided surgery. But this task is challenging due to the weak boundaries of organs, the complexity of the background, and the variable sizes of different organs. To address these challenges, we introduce a novel framework for multi-organ segmentation of abdominal regions by using organ-attention networks with reverse connections (OAN-RCs) which are applied to 2D views, of the 3D CT volume, and output estimates which are combined by statistical fusion exploiting structural similarity. More specifically, OAN is a two-stage deep convolutional network, where deep network features from the first stage are combined with the original image, in a second stage, to reduce the complex background and enhance the discriminative information for the target organs. Intuitively, OAN reduces the effect of the complex background by focusing attention so that each organ only needs to be discriminated from its local background. RCs are added to the first stage to give the lower layers more semantic information thereby enabling them to adapt to the sizes of different organs. Our networks are trained on 2D views (slices) enabling us to use holistic information and allowing efficient computation (compared to using 3D patches). To compensate for the limited cross-sectional information of the original 3D volumetric CT, e.g., the connectivity between neighbor slices, multi-sectional images are reconstructed from the three different 2D view directions. Then we combine the segmentation results from the different views using statistical fusion, with a novel term relating the structural similarity of the 2D views to the original 3D structure. To train the network and evaluate results, 13 structures were manually annotated by four human raters and confirmed by a senior expert on 236 normal cases. We tested our algorithm by 4-fold cross-validation and computed Dice-Sørensen similarity coefficients (DSC) and surface distances for evaluating our estimates of the 13 structures. Our experiments show that the proposed approach gives strong results and outperforms 2D- and 3D-patch based state-of-the-art methods in terms of DSC and mean surface distances.

Ubiquitous occurrence of chlorinated byproducts of bisphenol A and nonylphenol in bleached food contacting papers and their implications for human exposure.

The occurrence of bisphenol A (BPA), nonylphenol (NP), and their six chlorinated byproducts were investigated in 74 food contacting papers (FCPs) from China, the U.S.A., Japan, and Europe using a sensitive dansylation LC-MS/MS method. BPA (