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      A novel multi-epitope recombined protein for diagnosis of human brucellosis

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          Abstract

          Background

          In epidemic regions of the world, brucellosis is a reemerging zoonosis with minimal mortality but is a serious public hygiene problem. Currently, there are various methods for brucellosis diagnosis, however few of them are available to be used to diagnose, especially for serious cross-reaction with other bacteria.

          Method

          To overcome this disadvantage, we explored a novel multi-epitope recombinant protein as human brucellosis diagnostic antigen. We established an indirect enzyme-linked immunosorbent assay (ELISA) based on this recombinant protein. 248 sera obtained from three different groups including patients with brucellosis (146 samples), non-brucellosis patients (82 samples), and healthy individuals (20 samples) were tested by indirect ELISA. To evaluate the assay, a receiver-operating characteristic (ROC) analysis and immunoblotting were carried out using these characterized serum samples.

          Results

          For this test, the area under the ROC curve was 0.9409 (95 % confidence interval, 0.9108 to 0.9709), and a sensitivity of 88.89 % and a specificity of 85.54 % was given with a cutoff value of 0.3865 from this ROC analysis. The Western blot results indicate that it is feasible to differentiate human brucellosis and non-brucellosis with the newly established method based on this recombinant protein.

          Conclusion

          Our results obtained high diagnostic accuracy of the ELISA assay which encourage the use of this novel recombinant protein as diagnostic antigen to implement serological diagnosis of brucellosis.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12879-016-1552-9) contains supplementary material, which is available to authorized users.

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          Most cited references24

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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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            Improved method for predicting linear B-cell epitopes

            Background B-cell epitopes are the sites of molecules that are recognized by antibodies of the immune system. Knowledge of B-cell epitopes may be used in the design of vaccines and diagnostics tests. It is therefore of interest to develop improved methods for predicting B-cell epitopes. In this paper, we describe an improved method for predicting linear B-cell epitopes. Results In order to do this, three data sets of linear B-cell epitope annotated proteins were constructed. A data set was collected from the literature, another data set was extracted from the AntiJen database and a data sets of epitopes in the proteins of HIV was collected from the Los Alamos HIV database. An unbiased validation of the methods was made by testing on data sets on which they were neither trained nor optimized on. We have measured the performance in a non-parametric way by constructing ROC-curves. Conclusion The best single method for predicting linear B-cell epitopes is the hidden Markov model. Combining the hidden Markov model with one of the best propensity scale methods, we obtained the BepiPred method. When tested on the validation data set this method performs significantly better than any of the other methods tested. The server and data sets are publicly available at .
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              Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide.

              Comparative surface feature analyses of the VP1 sequences of hepatitis A virus (HAV) and poliovirus type 1 allowed an alignment of the two sequences and an identification of probable HAV neutralization antigenic sites. A synthetic peptide containing the HAV-specific amino acid sequence of one of these sites induced anti-HAV-neutralizing antibodies. It is concluded that a structural homology exists between the two viruses, despite minimal primary sequence conservation.
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                Author and article information

                Contributors
                li_juan@jlu.edu.cn
                xukun@jlu.edu.cn
                Journal
                BMC Infect Dis
                BMC Infect. Dis
                BMC Infectious Diseases
                BioMed Central (London )
                1471-2334
                21 May 2016
                21 May 2016
                2016
                : 16
                : 219
                Affiliations
                [ ]Department of Health Laboratory, School of Public Health, Jilin University, Changchun, China
                [ ]Department of Infection Control, First Hospital of Jilin University, Changchun, China
                [ ]Jilin Entry-Exit Inspection and Quarantine Bureau, Changchun, China
                Article
                1552
                10.1186/s12879-016-1552-9
                4875615
                27206475
                3d25c43d-4c0d-4773-91e1-ac75a7cd9ec8
                © Yin et al. 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 20 August 2015
                : 7 May 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 81473018
                Award ID: 81502849
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China (CN);
                Award ID: 81401721
                Award Recipient :
                Funded by: Graduate Innovation Fund of Jilin University
                Award ID: 2015089
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2016

                Infectious disease & Microbiology
                brucellosis,diagnosis,recombinant protein
                Infectious disease & Microbiology
                brucellosis, diagnosis, recombinant protein

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