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      Prototype multi-biomarker test for point-of-care leprosy diagnostics

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          Summary

          To end the decade-long, obstinately stagnant number of new leprosy cases, there is an urgent need for field-applicable diagnostic tools that detect infection with Mycobacterium leprae, leprosy's etiologic agent. Since immunity against M. leprae is characterized by humoral and cellular markers, we developed a lateral flow test measuring multiple host proteins based on six previously identified biomarkers for various leprosy phenotypes. This multi-biomarker test (MBT) demonstrated feasibility of quantitative detection of six host serum proteins simultaneously, jointly allowing discrimination of patients with multibacillary and paucibacillary leprosy from control individuals in high and low leprosy endemic areas. Pilot testing of fingerstick blood showed similar MBT performance in point-of-care (POC) settings as observed for plasma and serum. Thus, this newly developed prototype MBT measures six biomarkers covering immunity against M. leprae across the leprosy spectrum. The MBT thereby provides the basis for immunodiagnostic POC tests for leprosy with potential for other (infectious) diseases as well.

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          Highlights

          • Prototype MBT that quantitatively detects six host-derived biomarkers is developed

          • The immunopathological spectrum of leprosy is ideally suited to evaluate the MBT

          • MBT discriminated patients with leprosy from controls in a high and non-endemic area

          • Application of the MBT using low invasive fingerstick blood is technically feasible

          Abstract

          Diagnostic Technique in Health Technology; Applied Microbiology; Biotechnology

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          Most cited references46

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          Targets of T cell responses to SARS-CoV-2 coronavirus in humans with COVID-19 disease and unexposed individuals

          Summary Understanding adaptive immunity to SARS-CoV-2 is important for vaccine development, interpreting coronavirus disease 2019 (COVID-19) pathogenesis, and calibration of pandemic control measures. Using HLA class I and II predicted peptide ‘megapools’, circulating SARS-CoV-2−specific CD8+ and CD4+ T cells were identified in ∼70% and 100% of COVID-19 convalescent patients, respectively. CD4+ T cell responses to spike, the main target of most vaccine efforts, were robust and correlated with the magnitude of the anti-SARS-CoV-2 IgG and IgA titers. The M, spike and N proteins each accounted for 11-27% of the total CD4+ response, with additional responses commonly targeting nsp3, nsp4, ORF3a and ORF8, among others. For CD8+ T cells, spike and M were recognized, with at least eight SARS-CoV-2 ORFs targeted. Importantly, we detected SARS-CoV-2−reactive CD4+ T cells in ∼40-60% of unexposed individuals, suggesting cross-reactive T cell recognition between circulating ‘common cold’ coronaviruses and SARS-CoV-2.
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            Robust T cell immunity in convalescent individuals with asymptomatic or mild COVID-19

            Summary SARS-CoV-2-specific memory T cells will likely prove critical for long-term immune protection against COVID-19. We here systematically mapped the functional and phenotypic landscape of SARS-CoV-2-specific T cell responses in unexposed individuals, exposed family members, and individuals with acute or convalescent COVID-19. Acute phase SARS-CoV-2-specific T cells displayed a highly activated cytotoxic phenotype that correlated with various clinical markers of disease severity, whereas convalescent phase SARS-CoV-2-specific T cells were polyfunctional and displayed a stem-like memory phenotype. Importantly, SARS-CoV-2-specific T cells were detectable in antibody-seronegative exposed family members and convalescent individuals with a history of asymptomatic and mild COVID-19. Our collective dataset shows that SARS-CoV-2 elicits robust, broad and highly functional memory T cell responses, suggesting that natural exposure or infection may prevent recurrent episodes of severe COVID-19.
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              Estimation of the Youden Index and its associated cutoff point.

              The Youden Index is a frequently used summary measure of the ROC (Receiver Operating Characteristic) curve. It both, measures the effectiveness of a diagnostic marker and enables the selection of an optimal threshold value (cutoff point) for the marker. In this paper we compare several estimation procedures for the Youden Index and its associated cutoff point. These are based on (1) normal assumptions; (2) transformations to normality; (3) the empirical distribution function; (4) kernel smoothing. These are compared in terms of bias and root mean square error in a large variety of scenarios by means of an extensive simulation study. We find that the empirical method which is the most commonly used has the overall worst performance. In the estimation of the Youden Index the kernel is generally the best unless the data can be well transformed to achieve normality whereas in estimation of the optimal threshold value results are more variable.
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                Author and article information

                Contributors
                Journal
                iScience
                iScience
                iScience
                Elsevier
                2589-0042
                29 December 2020
                22 January 2021
                29 December 2020
                : 24
                : 1
                : 102006
                Affiliations
                [1 ]Department of Infectious Diseases Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, the Netherlands
                [2 ]Department of Cell and Chemical Biology, Leiden University Medical Center, the Netherlands
                [3 ]Rural Health Program, The Leprosy Mission International Bangladesh, Nilphamari, Bangladesh
                [4 ]Institute for Leprosy Research, Korean Hansen Welfare Association, Gyeonggi-do, South Korea
                [5 ]Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
                Author notes
                []Corresponding author a.geluk@ 123456lumc.nl
                [6]

                These authors contributed equally

                [7]

                Lead contact

                Article
                S2589-0042(20)31203-7 102006
                10.1016/j.isci.2020.102006
                7807156
                33490914
                c46d4b3a-a23a-470a-8ba6-5c126167bab0
                © 2020 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 24 September 2020
                : 19 November 2020
                : 23 December 2020
                Categories
                Article

                diagnostic technique in health technology,applied microbiology,biotechnology

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