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10.1016/S2666-5247(20)30197-X http://scihub22266oqcxt.onion/10.1016/S2666-5247(20)30197-X 33521709!7837364!33521709     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Lancet+Microbe 2021 ; 2 (2): e60-e69 Nephropedia Template TP {{tp|p=33521709|t=2021. Multiplex assays for the identification of serological signatures of SARS-CoV-2 infection: an antibody-based diagnostic and machine learning study |pdf=|usr=}}{{33521709}} gab.com Text Multiplex assays for the identification of serological signatures of SARS-CoV-2 infection: an antibody-based diagnostic and machine learning study JO: Lancet Microbe http://www.kidney.de/pget.php?&tw=1&pmid=33521709 #FOAvip BACKGROUND: Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induces an antibody response targeting multiple antigens that changes over time. This study aims to take advantage of this complexity to develop more accurate serological diagnostics. METHODS: A multiplex serological assay was developed to measure IgG and IgM antibody responses to seven SARS-CoV-2 spike or nucleoprotein antigens, two antigens for the nucleoproteins of the 229E and NL63 seasonal coronaviruses, and three non-coronavirus antigens. Antibodies were measured in serum samples collected up to 39 days after symptom onset from 215 adults in four French hospitals (53 patients and 162 health-care workers) with quantitative RT-PCR-confirmed SARS-CoV-2 infection, and negative control serum samples collected from healthy adult blood donors before the start of the SARS-CoV-2 epidemic (335 samples from France, Thailand, and Peru). Machine learning classifiers were trained with the multiplex data to classify individuals with previous SARS-CoV-2 infection, with the best classification performance displayed by a random forests algorithm. A Bayesian mathematical model of antibody kinetics informed by prior information from other coronaviruses was used to estimate time-varying antibody responses and assess the sensitivity and classification performance of serological diagnostics during the first year following symptom onset. A statistical estimator is presented that can provide estimates of seroprevalence in very low-transmission settings. FINDINGS: IgG antibody responses to trimeric spike protein (S(tri)) identified individuals with previous SARS-CoV-2 infection with 91.6% (95% CI 87.5-94.5) sensitivity and 99.1% (97.4-99.7) specificity. Using a serological signature of IgG and IgM to multiple antigens, it was possible to identify infected individuals with 98.8% (96.5-99.6) sensitivity and 99.3% (97.6-99.8) specificity. Informed by existing data from other coronaviruses, we estimate that 1 year after infection, a monoplex assay with optimal anti-S(tri) IgG cutoff has 88.7% (95% credible interval 63.4-97.4) sensitivity and that a four-antigen multiplex assay can increase sensitivity to 96.4% (80.9-100.0). When applied to population-level serological surveys, statistical analysis of multiplex data allows estimation of seroprevalence levels less than 2%, below the false-positivity rate of many other assays. INTERPRETATION: Serological signatures based on antibody responses to multiple antigens can provide accurate and robust serological classification of individuals with previous SARS-CoV-2 infection. This provides potential solutions to two pressing challenges for SARS-CoV-2 serological surveillance: classifying individuals who were infected more than 6 months ago and measuring seroprevalence in serological surveys in very low-transmission settings. FUNDING: European Research Council. Fondation pour la Recherche Medicale. Institut Pasteur Task Force COVID-19. Twit Text FOAVip Multiplex assays for the identification of serological signatures of SARS-CoV-2 infection: an antibody-based diagnostic and machine learning study ????Lancet Microbe http://www.kidney.de/pget.php?&tw=1&pmid=33521709 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33521709 #FOAvip English Wikipedia <ref>{{Cite pmid|33521709}}</ref> Multiplex assays for the identification of serological signatures of SARS-CoV-2 infection: an antibody-based diagnostic and machine learning study #MMPMID33521709 Rosado J; Pelleau S; Cockram C; Merkling SH; Nekkab N; Demeret C; Meola A; Kerneis S; Terrier B; Fafi-Kremer S; de Seze J; Bruel T; Dejardin F; Petres S; Longley R; Fontanet A; Backovic M; Mueller I; White MT Lancet Microbe 2021[Feb]; 2 (2): e60-e69 PMID33521709show ga BACKGROUND: Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induces an antibody response targeting multiple antigens that changes over time. This study aims to take advantage of this complexity to develop more accurate serological diagnostics. METHODS: A multiplex serological assay was developed to measure IgG and IgM antibody responses to seven SARS-CoV-2 spike or nucleoprotein antigens, two antigens for the nucleoproteins of the 229E and NL63 seasonal coronaviruses, and three non-coronavirus antigens. Antibodies were measured in serum samples collected up to 39 days after symptom onset from 215 adults in four French hospitals (53 patients and 162 health-care workers) with quantitative RT-PCR-confirmed SARS-CoV-2 infection, and negative control serum samples collected from healthy adult blood donors before the start of the SARS-CoV-2 epidemic (335 samples from France, Thailand, and Peru). Machine learning classifiers were trained with the multiplex data to classify individuals with previous SARS-CoV-2 infection, with the best classification performance displayed by a random forests algorithm. A Bayesian mathematical model of antibody kinetics informed by prior information from other coronaviruses was used to estimate time-varying antibody responses and assess the sensitivity and classification performance of serological diagnostics during the first year following symptom onset. A statistical estimator is presented that can provide estimates of seroprevalence in very low-transmission settings. FINDINGS: IgG antibody responses to trimeric spike protein (S(tri)) identified individuals with previous SARS-CoV-2 infection with 91.6% (95% CI 87.5-94.5) sensitivity and 99.1% (97.4-99.7) specificity. Using a serological signature of IgG and IgM to multiple antigens, it was possible to identify infected individuals with 98.8% (96.5-99.6) sensitivity and 99.3% (97.6-99.8) specificity. Informed by existing data from other coronaviruses, we estimate that 1 year after infection, a monoplex assay with optimal anti-S(tri) IgG cutoff has 88.7% (95% credible interval 63.4-97.4) sensitivity and that a four-antigen multiplex assay can increase sensitivity to 96.4% (80.9-100.0). When applied to population-level serological surveys, statistical analysis of multiplex data allows estimation of seroprevalence levels less than 2%, below the false-positivity rate of many other assays. INTERPRETATION: Serological signatures based on antibody responses to multiple antigens can provide accurate and robust serological classification of individuals with previous SARS-CoV-2 infection. This provides potential solutions to two pressing challenges for SARS-CoV-2 serological surveillance: classifying individuals who were infected more than 6 months ago and measuring seroprevalence in serological surveys in very low-transmission settings. FUNDING: European Research Council. Fondation pour la Recherche Medicale. Institut Pasteur Task Force COVID-19. |*COVID-19/diagnosis[MESH] |Adult[MESH] |Antibodies, Viral[MESH] |Bayes Theorem[MESH] |Humans[MESH] |Immunoglobulin G[MESH] |Immunoglobulin M[MESH] |Machine Learning[MESH] |SARS-CoV-2[MESH] |Sensitivity and Specificity[MESH]Multiplex assays for the identification of serological signatures of S(epmc).pdf DeepDyve Pubget Overpricing