The qualitative and quantitative evaluations showed that our algorithm substantially suppresses streaking artifacts and can outperform both linear interpolation and NMAR algorithms.”
“Objective(s): Heat shock protein 70 (Hsp70) is detected in substantial amounts in normal neurons and this basal content may protect a cell against harmful conditions without the need for additional synthesis. Herein, we investigate the potential

protective role of these basal levels of Hsp70, in an early ischaemic preconditioning (IPC) experimental model, suggesting a possible role of this protein as a first window of protection.

Design, material and methods: Forty-two pigs were used in an experimental thoraco-abdominal aortic occlusion model. Twelve animals (two groups) were used for neurological evaluation. The remaining 30 animals (five groups) were used for immunoprecipitation and immunohistochemical

studies. These were performed to study the PXD101 ic50 binding relationship of Hsp70/cytoskeleton elements and the cellular distribution of Hsp70, BLZ945 respectively.

Results: The IPC ischaemia-group showed significant better neurologic scores compared with those of the ischaemia group, indicating a protective role for IPC (P = 0.003). The immunoprecipitations demonstrated that early IPC increased significantly the binding profile of Hsp70/neurofilaments (P = 0.025). In addition, translocation of Hsp70 into the nucleus was observed, which was conserved until the sustained ischaemia.

Conclusions: These results indicate that Hsp70 may have an important role in early IPC of the spinal cord, by protecting neurofilaments and by

ensuring the functionality and the integrity of the nucleus, at the time the intensive insult begins. (C) 2012 European Society for Vascular Surgery. Published by Elsevier Ltd. All rights reserved.”
“A robust automated find more segmentation of abdominal organs can be crucial for computer aided diagnosis and laparoscopic surgery assistance. Many existing methods are specialized to the segmentation of individual organs and struggle to deal with the variability of the shape and position of abdominal organs. We present a general, fully-automated method for multi-organ segmentation of abdominal computed tomography (CT) scans. The method is based on a hierarchical atlas registration and weighting scheme that generates target specific priors from an atlas database by combining aspects from multi-atlas registration and patch-based segmentation, two widely used methods in brain segmentation. The final segmentation is obtained by applying an automatically learned intensity model in a graph-cuts optimization step, incorporating high-level spatial knowledge. The proposed approach allows to deal with high inter-subject variation while being flexible enough to be applied to different organs. We have evaluated the segmentation on a database of 150 manually segmented CT images.