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      Immunoglobulin signature predicts risk of post-acute COVID-19 syndrome

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          Abstract

          Following acute infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) a significant proportion of individuals develop prolonged symptoms, a serious condition termed post-acute coronavirus disease 2019 (COVID-19) syndrome (PACS) or long COVID. Predictors of PACS are needed. In a prospective multicentric cohort study of 215 individuals, we study COVID-19 patients during primary infection and up to one year later, compared to healthy subjects. We discover an immunoglobulin (Ig) signature, based on total IgM and IgG3 levels, which – combined with age, history of asthma bronchiale, and five symptoms during primary infection – is able to predict the risk of PACS independently of timepoint of blood sampling. We validate the score in an independent cohort of 395 individuals with COVID-19. Our results highlight the benefit of measuring Igs for the early identification of patients at high risk for PACS, which facilitates the study of targeted treatment and pathomechanisms of PACS.

          Abstract

          Studying a prospective cohort, the authors develop and validate a predictive score for post-acute COVID-19 syndrome, also known as long-COVID. This score relies on an immunoglobulin signature and is independent of timepoint of blood sampling.

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          Most cited references47

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          pROC: an open-source package for R and S+ to analyze and compare ROC curves

          Background Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. Results With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. Conclusions pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.
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            Acute respiratory distress syndrome: the Berlin Definition.

            The acute respiratory distress syndrome (ARDS) was defined in 1994 by the American-European Consensus Conference (AECC); since then, issues regarding the reliability and validity of this definition have emerged. Using a consensus process, a panel of experts convened in 2011 (an initiative of the European Society of Intensive Care Medicine endorsed by the American Thoracic Society and the Society of Critical Care Medicine) developed the Berlin Definition, focusing on feasibility, reliability, validity, and objective evaluation of its performance. A draft definition proposed 3 mutually exclusive categories of ARDS based on degree of hypoxemia: mild (200 mm Hg < PaO2/FIO2 ≤ 300 mm Hg), moderate (100 mm Hg < PaO2/FIO2 ≤ 200 mm Hg), and severe (PaO2/FIO2 ≤ 100 mm Hg) and 4 ancillary variables for severe ARDS: radiographic severity, respiratory system compliance (≤40 mL/cm H2O), positive end-expiratory pressure (≥10 cm H2O), and corrected expired volume per minute (≥10 L/min). The draft Berlin Definition was empirically evaluated using patient-level meta-analysis of 4188 patients with ARDS from 4 multicenter clinical data sets and 269 patients with ARDS from 3 single-center data sets containing physiologic information. The 4 ancillary variables did not contribute to the predictive validity of severe ARDS for mortality and were removed from the definition. Using the Berlin Definition, stages of mild, moderate, and severe ARDS were associated with increased mortality (27%; 95% CI, 24%-30%; 32%; 95% CI, 29%-34%; and 45%; 95% CI, 42%-48%, respectively; P < .001) and increased median duration of mechanical ventilation in survivors (5 days; interquartile [IQR], 2-11; 7 days; IQR, 4-14; and 9 days; IQR, 5-17, respectively; P < .001). Compared with the AECC definition, the final Berlin Definition had better predictive validity for mortality, with an area under the receiver operating curve of 0.577 (95% CI, 0.561-0.593) vs 0.536 (95% CI, 0.520-0.553; P < .001). This updated and revised Berlin Definition for ARDS addresses a number of the limitations of the AECC definition. The approach of combining consensus discussions with empirical evaluation may serve as a model to create more accurate, evidence-based, critical illness syndrome definitions and to better inform clinical care, research, and health services planning.
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              Pathophysiology, Transmission, Diagnosis, and Treatment of Coronavirus Disease 2019 (COVID-19): A Review

              The coronavirus disease 2019 (COVID-19) pandemic, due to the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a worldwide sudden and substantial increase in hospitalizations for pneumonia with multiorgan disease. This review discusses current evidence regarding the pathophysiology, transmission, diagnosis, and management of COVID-19.
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                Author and article information

                Contributors
                onur.boyman@uzh.ch
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                25 January 2022
                25 January 2022
                2022
                : 13
                : 446
                Affiliations
                [1 ]GRID grid.7400.3, ISNI 0000 0004 1937 0650, Department of Immunology, University Hospital Zurich, , University of Zurich, ; Zurich, Switzerland
                [2 ]GRID grid.7400.3, ISNI 0000 0004 1937 0650, Epidemiology, Biostatistics and Prevention Institute, , University of Zurich, ; Zurich, Switzerland
                [3 ]Clinic for Internal Medicine, Uster Hospital, Uster, Switzerland
                [4 ]GRID grid.459754.e, ISNI 0000 0004 0516 4346, Department of Medicine, , Limmattal Hospital, ; Schlieren, Switzerland
                [5 ]GRID grid.414526.0, ISNI 0000 0004 0518 665X, Clinic for Internal Medicine, , City Hospital Triemli Zurich, ; Zurich, Switzerland
                [6 ]GRID grid.7400.3, ISNI 0000 0004 1937 0650, Faculty of Medicine, , University of Zurich, ; Zurich, Switzerland
                Author information
                http://orcid.org/0000-0001-7120-8739
                http://orcid.org/0000-0001-5387-9950
                http://orcid.org/0000-0001-5970-1846
                http://orcid.org/0000-0001-7357-9090
                http://orcid.org/0000-0002-6161-3156
                http://orcid.org/0000-0003-2609-0246
                http://orcid.org/0000-0003-3105-5840
                http://orcid.org/0000-0003-4721-1879
                http://orcid.org/0000-0001-8279-5545
                Article
                27797
                10.1038/s41467-021-27797-1
                8789854
                35078982
                e4738f74-b906-46bd-bd59-c18324730b94
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 25 October 2021
                : 14 December 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001711, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation);
                Award ID: NRP 78 Implementation Programme
                Award ID: 4078P0-198431
                Award ID: NRP 78 Implementation Programme
                Award ID: 4078P0-198431
                Award ID: 310030-200669
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100008485, Schweizerische Akademie der Medizinischen Wissenschaften (Swiss Academy of Medical Sciences);
                Award ID: 323530-191220
                Award ID: 323530-191230
                Award ID: 323530-177975
                Award ID: YTCR 32/18
                Award Recipient :
                Funded by: University of Zurich (UZH Forschungskredit Candoc (#FK-20-022)
                Funded by: FundRef https://doi.org/10.13039/501100005688, Gottfried und Julia Bangerter-Rhyner-Stiftung (Bangerter-Stiftung);
                Award ID: YTCR 32/18
                Award Recipient :
                Funded by: Clinical Research Priority Program CYTIMM-Z of University of Zurich (UZH), Pandemic Fund of University of Zurich (UZH), Innovation grant of University Hospital Zurich
                Categories
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                © The Author(s) 2022

                Uncategorized
                predictive markers,infection,risk factors
                Uncategorized
                predictive markers, infection, risk factors

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