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      Global Metabolite Profiling of Synovial Fluid for the Specific Diagnosis of Rheumatoid Arthritis from Other Inflammatory Arthritis

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          Abstract

          Currently, reliable biomarkers that can be used to distinguish rheumatoid arthritis (RA) from other inflammatory diseases are unavailable. To find possible distinctive metabolic patterns and biomarker candidates for RA, we performed global metabolite profiling of synovial fluid samples. Synovial fluid samples from 38 patients with RA, ankylosing spondylitis, Behçet's disease, and gout were analyzed by gas chromatography/time-of-flight mass spectrometry (GC/TOF MS). Orthogonal partial least-squares discriminant and hierarchical clustering analyses were performed for the discrimination of RA and non-RA groups. Variable importance for projection values were determined, and the Wilcoxon-Mann-Whitney test and the breakdown and one-way analysis of variance were conducted to identify potential biomarkers for RA. A total of 105 metabolites were identified from synovial fluid samples. The score plot of orthogonal partial least squares discriminant analysis showed significant discrimination between the RA and non-RA groups. The 20 metabolites, including citrulline, succinate, glutamine, octadecanol, isopalmitic acid, and glycerol, were identified as potential biomarkers for RA. These metabolites were found to be associated with the urea and TCA cycles as well as fatty acid and amino acid metabolism. The metabolomic analysis results demonstrated that global metabolite profiling by GC/TOF MS might be a useful tool for the effective diagnosis and further understanding of RA.

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          Measuring the accuracy of diagnostic systems.

          J Swets (1988)
          Diagnostic systems of several kinds are used to distinguish between two classes of events, essentially "signals" and "noise". For them, analysis in terms of the "relative operating characteristic" of signal detection theory provides a precise and valid measure of diagnostic accuracy. It is the only measure available that is uninfluenced by decision biases and prior probabilities, and it places the performances of diverse systems on a common, easily interpreted scale. Representative values of this measure are reported here for systems in medical imaging, materials testing, weather forecasting, information retrieval, polygraph lie detection, and aptitude testing. Though the measure itself is sound, the values obtained from tests of diagnostic systems often require qualification because the test data on which they are based are of unsure quality. A common set of problems in testing is faced in all fields. How well these problems are handled, or can be handled in a given field, determines the degree of confidence that can be placed in a measured value of accuracy. Some fields fare much better than others.
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            Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests.

            We review the principles and practical application of receiver-operating characteristic (ROC) analysis for diagnostic tests. ROC analysis can be used for diagnostic tests with outcomes measured on ordinal, interval or ratio scales. The dependence of the diagnostic sensitivity and specificity on the selected cut-off value must be considered for a full test evaluation and for test comparison. All possible combinations of sensitivity and specificity that can be achieved by changing the test's cut-off value can be summarised using a single parameter; the area under the ROC curve. The ROC technique can also be used to optimise cut-off values with regard to a given prevalence in the target population and cost ratio of false-positive and false-negative results. However, plots of optimisation parameters against the selected cut-off value provide a more-direct method for cut-off selection. Candidates for such optimisation parameters are linear combinations of sensitivity and specificity (with weights selected to reflect the decision-making situation), odds ratio, chance-corrected measures of association (e. g. kappa) and likelihood ratios. We discuss some recent developments in ROC analysis, including meta-analysis of diagnostic tests, correlated ROC curves (paired-sample design) and chance- and prevalence-corrected ROC curves.
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              Quality control for plant metabolomics: reporting MSI-compliant studies.

              The Metabolomics Standards Initiative (MSI) has recently released documents describing minimum parameters for reporting metabolomics experiments, in order to validate metabolomic studies and to facilitate data exchange. The reporting parameters encompassed by MSI include the biological study design, sample preparation, data acquisition, data processing, data analysis and interpretation relative to the biological hypotheses being evaluated. Herein we exemplify how such metadata can be reported by using a small case study - the metabolite profiling by GC-TOF mass spectrometry of Arabidopsis thaliana leaves from a knockout allele of the gene At1g08510 in the Wassilewskija ecotype. Pitfalls in quality control are highlighted that can invalidate results even if MSI reporting standards are fulfilled, including reliable compound identification and integration of unknown metabolites. Standardized data processing methods are proposed for consistent data storage and dissemination via databases.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                2 June 2014
                : 9
                : 6
                : e97501
                Affiliations
                [1 ]Department of Biotechnology, Korea University Graduate School, Seoul, Republic of Korea
                [2 ]Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
                Yonsei University, Republic of Korea
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: HSC KHK. Performed the experiments: SK JH JX YHJ. Analyzed the data: SK JH HSC KHK. Contributed reagents/materials/analysis tools: SK JH HSC KHK. Wrote the paper: SK JH HSC KHK.

                Article
                PONE-D-14-07181
                10.1371/journal.pone.0097501
                4041724
                24887281
                4a6dfb74-dd4e-4401-960b-79d1dee0ccc4
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 14 February 2014
                : 17 April 2014
                Page count
                Pages: 9
                Funding
                This work was supported by the Advanced Biomass R&D Center of Korea (2011-0031353) and the National Foundation of Research (2013059103), both funded by the Korean Government. Experiments were performed by using the facilities of the Institute of Biomedical Science and Food Safety at the Korea University Food Safety Hall. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Bone and Mineral Metabolism
                Biotechnology
                Small Molecules
                Immunology
                Clinical Immunology
                Autoimmune Diseases
                Ankylosing Spondylitis
                Rheumatoid Arthritis
                Immune Response
                Systems Biology
                Medicine and Health Sciences
                Rheumatology
                Arthritis
                Inflammatory Arthritis
                Physical Sciences
                Chemistry
                Analytical Chemistry
                Chemical Analysis

                Uncategorized
                Uncategorized

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