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      Distinguishing features of long COVID identified through immune profiling

      research-article
      1 , 2 , 1 , 1 , 3 , 1 , 1 , 4 , 1 , 1 , 2 , 5 , 1 , 1 , 6 , 6 , 6 , 1 , 7 , 1 , 8 , 1 , 1 , 1 , 1 , 9 , 1 , 2 , 2 , 2 , 1 , 1 , 1 , 1 , 1 , 10 , 10 , 11 , 5 , 9 , 12 , 13 , 6 , 1 , 9 , 11 , 7 , 7 , 7 , 5 , 9 , 13 , 14 , 9 , 15 , 16 , 17 , 9 , 18 , 7 , 9 , 9 , 19 , 20 , , 1 , 9 , , 2 , 21 , , 1 , 9 , 11 ,
      Nature
      Nature Publishing Group UK
      Viral infection, Cytokines, Antibodies, SARS-CoV-2

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Post-acute infection syndromes may develop after acute viral disease 1 . Infection with SARS-CoV-2 can result in the development of a post-acute infection syndrome known as long COVID. Individuals with long COVID frequently report unremitting fatigue, post-exertional malaise, and a variety of cognitive and autonomic dysfunctions 24 . However, the biological processes that are associated with the development and persistence of these symptoms are unclear. Here 275 individuals with or without long COVID were enrolled in a cross-sectional study that included multidimensional immune phenotyping and unbiased machine learning methods to identify biological features associated with long COVID. Marked differences were noted in circulating myeloid and lymphocyte populations relative to the matched controls, as well as evidence of exaggerated humoral responses directed against SARS-CoV-2 among participants with long COVID. Furthermore, higher antibody responses directed against non-SARS-CoV-2 viral pathogens were observed among individuals with long COVID, particularly Epstein–Barr virus. Levels of soluble immune mediators and hormones varied among groups, with cortisol levels being lower among participants with long COVID. Integration of immune phenotyping data into unbiased machine learning models identified the key features that are most strongly associated with long COVID status. Collectively, these findings may help to guide future studies into the pathobiology of long COVID and help with developing relevant biomarkers.

          Abstract

          Individuals with long COVID show marked biological changes in cortisol and immune factors relative to convalescent populations.

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          Most cited references72

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          edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

          Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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            Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

            Research electronic data capture (REDCap) is a novel workflow methodology and software solution designed for rapid development and deployment of electronic data capture tools to support clinical and translational research. We present: (1) a brief description of the REDCap metadata-driven software toolset; (2) detail concerning the capture and use of study-related metadata from scientific research teams; (3) measures of impact for REDCap; (4) details concerning a consortium network of domestic and international institutions collaborating on the project; and (5) strengths and limitations of the REDCap system. REDCap is currently supporting 286 translational research projects in a growing collaborative network including 27 active partner institutions.
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              A brief measure for assessing generalized anxiety disorder: the GAD-7.

              Generalized anxiety disorder (GAD) is one of the most common mental disorders; however, there is no brief clinical measure for assessing GAD. The objective of this study was to develop a brief self-report scale to identify probable cases of GAD and evaluate its reliability and validity. A criterion-standard study was performed in 15 primary care clinics in the United States from November 2004 through June 2005. Of a total of 2740 adult patients completing a study questionnaire, 965 patients had a telephone interview with a mental health professional within 1 week. For criterion and construct validity, GAD self-report scale diagnoses were compared with independent diagnoses made by mental health professionals; functional status measures; disability days; and health care use. A 7-item anxiety scale (GAD-7) had good reliability, as well as criterion, construct, factorial, and procedural validity. A cut point was identified that optimized sensitivity (89%) and specificity (82%). Increasing scores on the scale were strongly associated with multiple domains of functional impairment (all 6 Medical Outcomes Study Short-Form General Health Survey scales and disability days). Although GAD and depression symptoms frequently co-occurred, factor analysis confirmed them as distinct dimensions. Moreover, GAD and depression symptoms had differing but independent effects on functional impairment and disability. There was good agreement between self-report and interviewer-administered versions of the scale. The GAD-7 is a valid and efficient tool for screening for GAD and assessing its severity in clinical practice and research.
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                Author and article information

                Contributors
                david.vandijk@yale.edu
                aaron.ring@yale.edu
                david.putrino@mountsinai.org
                akiko.iwasaki@yale.edu
                Journal
                Nature
                Nature
                Nature
                Nature Publishing Group UK (London )
                0028-0836
                1476-4687
                25 September 2023
                25 September 2023
                2023
                : 623
                : 7985
                : 139-148
                Affiliations
                [1 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Immunobiology, , Yale School of Medicine, ; New Haven, CT USA
                [2 ]Abilities Research Center, Icahn School of Medicine at Mount Sinai, ( https://ror.org/04a9tmd77) New York, NY USA
                [3 ]GRID grid.42505.36, ISNI 0000 0001 2156 6853, Department of Ophthalmology, , USC Keck School of Medicine, ; Los Angeles, CA USA
                [4 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Dermatology, , Yale School of Medicine, ; New Haven, CT USA
                [5 ]GRID grid.47100.32, ISNI 0000000419368710, Yale Institute for Global Health, , Yale School of Public Health, ; New Haven, CT USA
                [6 ]SerImmune, ( https://ror.org/006sjd474) Goleta, CA USA
                [7 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Internal Medicine (Pulmonary, Critical Care and Sleep Medicine), , Yale School of Medicine, ; New Haven, CT USA
                [8 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Microbiology, , Yale School of Medicine, ; New Haven, CT USA
                [9 ]GRID grid.47100.32, ISNI 0000000419368710, Center for Infection and Immunity, , Yale School of Medicine, ; New Haven, CT USA
                [10 ]Department of Neurology and Neurological Sciences, Stanford University, ( https://ror.org/00f54p054) Palo Alto, CA USA
                [11 ]Howard Hughes Medical Institute, ( https://ror.org/006w34k90) Chevy Chase, MD USA
                [12 ]Department of Pediatrics (Infectious Diseases), Yale New Haven Hospital, ( https://ror.org/05tszed37) New Haven, CT USA
                [13 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Epidemiology of Microbial Diseases, , Yale School of Public Health, ; New Haven, CT USA
                [14 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Internal Medicine (Infectious Diseases), , Yale School of Medicine, ; New Haven, CT USA
                [15 ]Center for Outcomes Research and Evaluation, Yale New Haven Hospital, ( https://ror.org/05tszed37) New Haven, CT USA
                [16 ]GRID grid.47100.32, ISNI 0000000419368710, Section of Cardiovascular Medicine, Department of Internal Medicine, , Yale School of Medicine, ; New Haven, CT USA
                [17 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Health Policy and Management, , Yale School of Public Health, ; New Haven, CT USA
                [18 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Biostatistics, , Yale School of Public Health, ; New Haven, CT USA
                [19 ]Department of Computer Science, Yale University, ( https://ror.org/03v76x132) New Haven, CT USA
                [20 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Internal Medicine (Cardiology), , Yale School of Medicine, ; New Haven, CT USA
                [21 ]Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, ( https://ror.org/04a9tmd77) New York, NY USA
                Author information
                http://orcid.org/0000-0002-3552-7684
                http://orcid.org/0000-0002-2014-7515
                http://orcid.org/0000-0001-6118-872X
                http://orcid.org/0000-0003-1785-6713
                http://orcid.org/0000-0001-8212-7440
                http://orcid.org/0000-0003-2347-0569
                http://orcid.org/0000-0001-9251-8592
                http://orcid.org/0000-0002-0801-1543
                http://orcid.org/0000-0002-1061-356X
                http://orcid.org/0000-0003-4590-2756
                http://orcid.org/0000-0001-5448-5865
                http://orcid.org/0000-0002-7761-3361
                http://orcid.org/0000-0002-3547-237X
                http://orcid.org/0000-0002-8631-0020
                http://orcid.org/0000-0002-7021-2012
                http://orcid.org/0000-0001-5917-4601
                http://orcid.org/0000-0002-5383-3474
                http://orcid.org/0000-0003-2046-127X
                http://orcid.org/0000-0002-5258-1797
                http://orcid.org/0000-0003-3911-9925
                http://orcid.org/0000-0003-3699-2446
                http://orcid.org/0000-0002-2232-3324
                http://orcid.org/0000-0002-7824-9856
                Article
                6651
                10.1038/s41586-023-06651-y
                10620090
                37748514
                3e5d1b6b-ed10-4225-a1a2-d62abf439d1d
                © The Author(s) 2023

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 8 August 2022
                : 18 September 2023
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                © Springer Nature Limited 2023

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                viral infection,cytokines,antibodies,sars-cov-2
                Uncategorized
                viral infection, cytokines, antibodies, sars-cov-2

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