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      The plasma metabolome of long COVID patients two years after infection

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          Abstract

          One of the major challenges currently faced by global health systems is the prolonged COVID-19 syndrome (also known as “long COVID”) which has emerged as a consequence of the SARS-CoV-2 epidemic. It is estimated that at least 30% of patients who have had COVID-19 will develop long COVID. In this study, our goal was to assess the plasma metabolome in a total of 100 samples collected from healthy controls, COVID-19 patients, and long COVID patients recruited in Mexico between 2020 and 2022. A targeted metabolomics approach using a combination of LC–MS/MS and FIA MS/MS was performed to quantify 108 metabolites. IL-17 and leptin were measured in long COVID patients by immunoenzymatic assay. The comparison of paired COVID-19/long COVID-19 samples revealed 53 metabolites that were statistically different. Compared to controls, 27 metabolites remained dysregulated even after two years. Post-COVID-19 patients displayed a heterogeneous metabolic profile. Lactic acid, lactate/pyruvate ratio, ornithine/citrulline ratio, and arginine were identified as the most relevant metabolites for distinguishing patients with more complicated long COVID evolution. Additionally, IL-17 levels were significantly increased in these patients. Mitochondrial dysfunction, redox state imbalance, impaired energy metabolism, and chronic immune dysregulation are likely to be the main hallmarks of long COVID even two years after acute COVID-19 infection.

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          The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment.

          To develop a 10-minute cognitive screening tool (Montreal Cognitive Assessment, MoCA) to assist first-line physicians in detection of mild cognitive impairment (MCI), a clinical state that often progresses to dementia. Validation study. A community clinic and an academic center. Ninety-four patients meeting MCI clinical criteria supported by psychometric measures, 93 patients with mild Alzheimer's disease (AD) (Mini-Mental State Examination (MMSE) score > or =17), and 90 healthy elderly controls (NC). The MoCA and MMSE were administered to all participants, and sensitivity and specificity of both measures were assessed for detection of MCI and mild AD. Using a cutoff score 26, the MMSE had a sensitivity of 18% to detect MCI, whereas the MoCA detected 90% of MCI subjects. In the mild AD group, the MMSE had a sensitivity of 78%, whereas the MoCA detected 100%. Specificity was excellent for both MMSE and MoCA (100% and 87%, respectively). MCI as an entity is evolving and somewhat controversial. The MoCA is a brief cognitive screening tool with high sensitivity and specificity for detecting MCI as currently conceptualized in patients performing in the normal range on the MMSE.
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            A RATING SCALE FOR DEPRESSION

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              MetaboAnalyst: a web server for metabolomic data analysis and interpretation

              Metabolomics is a newly emerging field of ‘omics’ research that is concerned with characterizing large numbers of metabolites using NMR, chromatography and mass spectrometry. It is frequently used in biomarker identification and the metabolic profiling of cells, tissues or organisms. The data processing challenges in metabolomics are quite unique and often require specialized (or expensive) data analysis software and a detailed knowledge of cheminformatics, bioinformatics and statistics. In an effort to simplify metabolomic data analysis while at the same time improving user accessibility, we have developed a freely accessible, easy-to-use web server for metabolomic data analysis called MetaboAnalyst. Fundamentally, MetaboAnalyst is a web-based metabolomic data processing tool not unlike many of today's web-based microarray analysis packages. It accepts a variety of input data (NMR peak lists, binned spectra, MS peak lists, compound/concentration data) in a wide variety of formats. It also offers a number of options for metabolomic data processing, data normalization, multivariate statistical analysis, graphing, metabolite identification and pathway mapping. In particular, MetaboAnalyst supports such techniques as: fold change analysis, t-tests, PCA, PLS-DA, hierarchical clustering and a number of more sophisticated statistical or machine learning methods. It also employs a large library of reference spectra to facilitate compound identification from most kinds of input spectra. MetaboAnalyst guides users through a step-by-step analysis pipeline using a variety of menus, information hyperlinks and check boxes. Upon completion, the server generates a detailed report describing each method used, embedded with graphical and tabular outputs. MetaboAnalyst is capable of handling most kinds of metabolomic data and was designed to perform most of the common kinds of metabolomic data analyses. MetaboAnalyst is accessible at http://www.metaboanalyst.ca
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                Author and article information

                Contributors
                ylopezher@conacyt.mx
                dwishart@ualberta.ca
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                1 August 2023
                1 August 2023
                2023
                : 13
                : 12420
                Affiliations
                [1 ]GRID grid.412865.c, ISNI 0000 0001 2105 1788, CONAHCyT-Metabolomics and Proteomics Laboratory, Academic Unit of Biological Sciences, , Autonomous University of Zacatecas, ; 98000 Zacatecas, Mexico
                [2 ]GRID grid.440451.0, ISNI 0000 0004 1766 8816, Department of Health Research, , Christus Muguerza del Parque Hospital – University of Monterrey, ; 31125 Chihuahua, Mexico
                [3 ]GRID grid.412865.c, ISNI 0000 0001 2105 1788, Academic Unit of Biological Sciences, , Autonomous University of Zacatecas, ; 98000 Zacatecas, Mexico
                [4 ]GRID grid.17089.37, ISNI 0000 0001 2190 316X, The Metabolomics Innovation Centre, , University of Alberta, ; Edmonton, AB T6G 1C9 Canada
                [5 ]GRID grid.419157.f, ISNI 0000 0001 1091 9430, Departamento de Epidemiología, , Hospital General de Zona #1 “Emilio Varela Luján”, Instituto Mexicano del Seguro Social, ; Zacatecas, 98000 México
                [6 ]GRID grid.17089.37, ISNI 0000 0001 2190 316X, Department of Biological Sciences, , University of Alberta, ; Edmonton, AB T6G 1C9 Canada
                [7 ]GRID grid.452651.1, ISNI 0000 0004 0627 7633, Laboratory of Cell Communication & Extracellular Vesicles, Division of Basic Science, , Instituto Nacional de Medicina Genómica, ; 14610 Ciudad de México, Mexico
                [8 ]GRID grid.412865.c, ISNI 0000 0001 2105 1788, MicroRNAs and Cancer Laboratory, Academic Unit of Biological Sciences, , Autonomous University of Zacatecas, ; 98000 Zacatecas, Mexico
                Article
                39049
                10.1038/s41598-023-39049-x
                10394026
                37528111
                5fff3f84-5809-4f31-a3d5-2376b6d4a5fc
                © 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
                : 19 May 2023
                : 19 July 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100003141, Consejo Nacional de Ciencia y Tecnología;
                Award ID: 311880
                Award ID: 2166969
                Award Recipient :
                Funded by: Christus Muguerza del Parque
                Award ID: 28022022
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100010787, Genome Alberta;
                Award ID: TMIC MC4
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000024, Canadian Institutes of Health Research;
                Award ID: FS 148461
                Award Recipient :
                Funded by: Canadian Foundation for Innovation
                Award ID: MSIF 35456
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2023

                Uncategorized
                biomarkers,diseases,molecular medicine
                Uncategorized
                biomarkers, diseases, molecular medicine

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