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      Quantitative proteomics of delirium cerebrospinal fluid

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          Abstract

          Delirium is a common cause and complication of hospitalization in older people, being associated with higher risk of future dementia and progression of existing dementia. However relatively little data are available on which biochemical pathways are dysregulated in the brain during delirium episodes, whether there are protein expression changes common among delirium subjects and whether there are any changes which correlate with the severity of delirium. We now present the first proteomic analysis of delirium cerebrospinal fluid (CSF), and one of few studies exploring protein expression changes in delirium. More than 270 proteins were identified in two delirium cohorts, 16 of which were dysregulated in at least 8 of 17 delirium subjects compared with a mild Alzheimer's disease neurological control group, and 31 proteins were significantly correlated with cognitive scores (mini-mental state exam and acute physiology and chronic health evaluation III). Bioinformatics analyses revealed expression changes in several protein family groups, including apolipoproteins, secretogranins/chromogranins, clotting/fibrinolysis factors, serine protease inhibitors and acute-phase response elements. These data not only provide confirmatory evidence that the inflammatory response is a component of delirium, but also reveal dysregulation of protein expression in a number of novel and unexpected clusters of proteins, in particular the granins. Another surprising outcome of this work is the level of similarity of CSF protein profiles in delirium patients, given the diversity of causes of this syndrome. These data provide additional elements for consideration in the pathophysiology of delirium as well as potential biomarker candidates for delirium diagnosis.

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          Most cited references 35

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          Evaluation of clustering algorithms for protein-protein interaction networks

          Background Protein interactions are crucial components of all cellular processes. Recently, high-throughput methods have been developed to obtain a global description of the interactome (the whole network of protein interactions for a given organism). In 2002, the yeast interactome was estimated to contain up to 80,000 potential interactions. This estimate is based on the integration of data sets obtained by various methods (mass spectrometry, two-hybrid methods, genetic studies). High-throughput methods are known, however, to yield a non-negligible rate of false positives, and to miss a fraction of existing interactions. The interactome can be represented as a graph where nodes correspond with proteins and edges with pairwise interactions. In recent years clustering methods have been developed and applied in order to extract relevant modules from such graphs. These algorithms require the specification of parameters that may drastically affect the results. In this paper we present a comparative assessment of four algorithms: Markov Clustering (MCL), Restricted Neighborhood Search Clustering (RNSC), Super Paramagnetic Clustering (SPC), and Molecular Complex Detection (MCODE). Results A test graph was built on the basis of 220 complexes annotated in the MIPS database. To evaluate the robustness to false positives and false negatives, we derived 41 altered graphs by randomly removing edges from or adding edges to the test graph in various proportions. Each clustering algorithm was applied to these graphs with various parameter settings, and the clusters were compared with the annotated complexes. We analyzed the sensitivity of the algorithms to the parameters and determined their optimal parameter values. We also evaluated their robustness to alterations of the test graph. We then applied the four algorithms to six graphs obtained from high-throughput experiments and compared the resulting clusters with the annotated complexes. Conclusion This analysis shows that MCL is remarkably robust to graph alterations. In the tests of robustness, RNSC is more sensitive to edge deletion but less sensitive to the use of suboptimal parameter values. The other two algorithms are clearly weaker under most conditions. The analysis of high-throughput data supports the superiority of MCL for the extraction of complexes from interaction networks.
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            The genetics of Alzheimer disease: back to the future.

            Three decades of genetic research in Alzheimer disease (AD) have substantially broadened our understanding of the pathogenetic mechanisms leading to neurodegeneration and dementia. Positional cloning led to the identification of rare, disease-causing mutations in APP, PSEN1, and PSEN2 causing early-onset familial AD, followed by the discovery of APOE as the single most important risk factor for late-onset AD. Recent genome-wide association approaches have delivered several additional AD susceptibility loci that are common in the general population, but exert only very small risk effects. As a result, a large proportion of the heritability of AD continues to remain unexplained by the currently known disease genes. It seems likely that much of this "missing heritability" may be accounted for by rare sequence variants, which, owing to recent advances in high-throughput sequencing technologies, can now be assessed in unprecedented detail. Copyright © 2010 Elsevier Inc. All rights reserved.
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              Delirium in elderly adults: diagnosis, prevention and treatment.

              Delirium is a common and serious acute neuropsychiatric syndrome with core features of inattention and global cognitive dysfunction. The etiologies of delirium are diverse and multifactorial and often reflect the pathophysiological consequences of an acute medical illness, medical complication or drug intoxication. Delirium can have a widely variable presentation, and is often missed and underdiagnosed as a result. At present, the diagnosis of delirium is clinically based and depends on the presence or absence of certain features. Management strategies for delirium are focused on prevention and symptom management. This article reviews current clinical practice in delirium in elderly individuals, including the diagnosis, treatment, outcomes and economic impact of this syndrome. Areas of future research are also discussed.
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                Author and article information

                Journal
                Transl Psychiatry
                Transl Psychiatry
                Translational Psychiatry
                Nature Publishing Group
                2158-3188
                November 2014
                04 November 2014
                1 November 2014
                : 4
                : 11
                : e477
                Affiliations
                [1 ]Bioanalytical Mass Spectrometry Facility, University of New South Wales , Sydney, NSW, Australia
                [2 ]School of Medical Sciences, University of New South Wales , Sydney, NSW, Australia
                [3 ]Center for Healthy Brain Ageing, University of New South Wales , Sydney, NSW, Australia
                [4 ]Edinburgh Delirium Research Group, University of Edinburgh , Edinburgh, Scotland, UK
                [5 ]Prince of Wales Clinical School, University of New South Wales , Sydney, NSW, Australia
                [6 ]Department of Geriatric Medicine, Prince of Wales Hospital , Sydney, NSW, Australia
                Author notes
                [* ]Bioanalytical Mass Spectrometry Facility, University of New South Wales , Anzac Pde, Kensington, Sydney, NSW 2052, Australia. E-mail: a.poljak@ 123456unsw.edu.au
                Article
                tp2014114
                10.1038/tp.2014.114
                4259987
                25369144
                Copyright © 2014 Macmillan Publishers Limited

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/

                Categories
                Original Article

                Clinical Psychology & Psychiatry

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