Alpha tocopherol supplementation elevates plasma apolipoprotein A1 isoforms in normal healthy subjects

Plasma α-tocopherol (AT) concentrations are inversely related to cardiovascular (CV) risk; however, intervention studies with AT have failed to show any consistent benefit against CV disease (CVD). Proteomics offers the opportunity to examine novel effects of AT supplementation on protein expression and therefore improve our understanding of the physiological roles of AT. Thus, to investigate the effects of AT supplementation on the plasma proteome of healthy subjects we have undertaken a double-blind, randomised, parallel design supplementation study in which healthy subjects (n = 32; 11 male and 21 female) consumed AT supplements (134 or 268 mg/day) or placebo capsules for up to 28 days. Plasma samples were obtained before supplementation and after 14 and 28 days of supplementation for analysis of changes in the plasma proteome using 2-DE and MALDI-MS. Using semiquantitative proteomics, we observed that proapolipoprotein A1 (identified by MS and Western blotting) was altered at least two-fold. Using quantitative ELISA techniques, we confirmed a significant increase in plasma apolipoprotein A1 concentration following supplementation with AT which was both time and dose dependent (p < 0.01 after 28 days supplementation with 268 mg AT/day). These data demonstrate the time and dose sensitivity of the plasma proteome to AT supplementation. © 2006 Wiley-VCH Verlag GmbH & Co. KGaA.

A comparison of denoising methods for X-ray fluoroscopic images

Fluoroscopic images exhibit severe signal-dependent quantum noise, due to the reduced X-ray dose involved in image formation, that is generally modelled as Poisson-distributed. However, image gray-level transformations, commonly applied by fluoroscopic device to enhance contrast, modify the noise statistics and the relationship between image noise variance and expected pixel intensity. Image denoising is essential to improve quality of fluoroscopic images and their clinical information content. Simple average filters are commonly employed in real-time processing, but they tend to blur edges and details. An extensive comparison of advanced denoising algorithms specifically designed for both signal-dependent noise (AAS, BM3Dc, HHM, TLS) and independent additive noise (AV, BM3D, K-SVD) was presented. Simulated test images degraded by various levels of Poisson quantum noise and real clinical fluoroscopic images were considered. Typical gray-level transformations (e.g. white compression) were also applied in order to evaluate their effect on the denoising algorithms. Performances of the algorithms were evaluated in terms of peak-signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), mean square error (MSE), structural similarity index (SSIM) and computational time. On average, the filters designed for signal-dependent noise provided better image restorations than those assuming additive white Gaussian noise (AWGN). Collaborative denoising strategy was found to be the most effective in denoising of both simulated and real data, also in the presence of image gray-level transformations. White compression, by inherently reducing the greater noise variance of brighter pixels, appeared to support denoising algorithms in performing more effectively. © 2012 Elsevier Ltd. All rights reserved.