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      Scalable, Micro-Neutralization Assay for Qualitative Assessment of SARS-CoV-2 (COVID-19) Virus-Neutralizing Antibodies in Human Clinical Samples

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          Abstract

          As the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic was expanding, it was clear that effective testing for the presence of neutralizing antibodies in the blood of convalescent patients would be critical for development of plasma-based therapeutic approaches. To address the need for a high-quality neutralization assay against SARS-CoV-2, a previously established fluorescence reduction neutralization assay (FRNA) against Middle East respiratory syndrome coronavirus (MERS-CoV) was modified and optimized. The SARS-CoV-2 FRNA provides a quantitative assessment of a large number of infected cells through use of a high-content imaging system. Because of this approach, and the fact that it does not involve subjective interpretation, this assay is more efficient and more accurate than other neutralization assays. In addition, the ability to set robust acceptance criteria for individual plates and specific test wells provided further rigor to this assay. Such agile adaptability avails use with multiple virus variants. By February 2021, the SARS-CoV-2 FRNA had been used to screen over 5,000 samples, including acute and convalescent plasma or serum samples and therapeutic antibody treatments, for SARS-CoV-2 neutralizing titers.

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          SciPy 1.0: fundamental algorithms for scientific computing in Python

          SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
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            Understanding of COVID‐19 based on current evidence

            Abstract Since December 2019, a series of unexplained pneumonia cases have been reported in Wuhan, China. On 12 January 2020, the World Health Organization (WHO) temporarily named this new virus as the 2019 novel coronavirus (2019‐nCoV). On 11 February 2020, the WHO officially named the disease caused by the 2019‐nCoV as coronavirus disease (COVID‐19). The COVID‐19 epidemic is spreading all over the world, especially in China. Based on the published evidence, we systematically discuss the characteristics of COVID‐19 in the hope of providing a reference for future studies and help for the prevention and control of the COVID‐19 epidemic.
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              Kinetics of SARS-CoV-2 specific IgM and IgG responses in COVID-19 patients

              ABSTRACT The emerging COVID-19 caused by SARS-CoV-2 infection poses severe challenges to global public health. Serum antibody testing is becoming one of the critical methods for the diagnosis of COVID-19 patients. We investigated IgM and IgG responses against SARS-CoV-2 nucleocapsid (N) and spike (S) protein after symptom onset in the intensive care unit (ICU) and non-ICU patients. 130 blood samples from 38 COVID-19 patients were collected. The levels of IgM and IgG specific to N and S protein were detected by ELISA. A series of blood samples were collected along the disease course from the same patient, including 11 ICU patients and 27 non-ICU patients for longitudinal analysis. N and S specific IgM and IgG (N-IgM, N-IgG, S-IgM, S-IgG) in non-ICU patients increased after symptom onset. N-IgM and S-IgM in some non-ICU patients reached a peak in the second week, while N-IgG and S-IgG continued to increase in the third week. The combined detection of N and S specific IgM and IgG could identify up to 75% of SARS-CoV-2 infected patients in the first week. S-IgG was significantly higher in non-ICU patients than in ICU patients in the third week. In contrast, N-IgG was significantly higher in ICU patients than in non-ICU patients. The increase of S-IgG positively correlated with the decrease of C-reactive protein (CRP) in non-ICU patients. N and S specific IgM and IgG increased gradually after symptom onset and can be used for detection of SARS-CoV-2 infection. Analysis of the dynamics of S-IgG may help to predict prognosis.
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                Author and article information

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                05 March 2021
                : 2021.03.05.434152
                Affiliations
                [1 ]Integrated Research Facility at Fort Detrick, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD 21702, USA;
                [2 ]Kearney, Chicago, IL 60606, USA;
                Author notes

                Author Contributions: Conceptualization, E.P. and R.S.B.; methodology, E.P., J.L., R.G., D.G., S.G.-C., S.M., S.D., L.M., G.K., Y.C., S.Y., and V.V.L; formal analysis, E.P. and J.L.; resources, M.R.H.; data curation, R.G., J.L., and M.R.H.; writing—original draft preparation, R.S.B., E.P., J.L., and V.V.L.; writing—review and editing, M.R.H.; supervision, M.R.H.; project administration, M.R.H. All authors have read and agreed to the published version of the manuscript.

                [* ]Corresponding author: Michael R. Holbrook, Ph.D., NIAID Integrated Research Facility, 8200 Research Plaza, Ft. Detrick, Frederick, MD 21702, Tel.: +1-301-631-7265, michael.holbrook@ 123456nih.gov
                Article
                10.1101/2021.03.05.434152
                7941633
                33688658
                1db1f264-534e-4eb4-9a55-dff10303abcd

                This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license.

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                sars-cov,sars-cov-2,coronavirus,covid,covid-19,neutralization,antibodies,diagnosis

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