{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"Research Square"}],"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T06:43:25Z","timestamp":1747205005847,"version":"3.40.5"},"posted":{"date-parts":[[2023,11,9]]},"group-title":"In Review","reference-count":70,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T00:00:00Z","timestamp":1699488000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2023,10,29]]},"abstract":"<title>Abstract<\/title>\n        <p><bold>Introduction:<\/bold> Apply machine learning models to identify new biomarkers associated with the early diagnosis and prognosis of SARS-CoV-2 infection, aiming to prevent long COVID.\n<bold>Material and methods:<\/bold> Plasma and serum samples from COVID-19 patients (mild, moderate, and severe), patients with other pneumonias (but with negative COVID-19 RT-PCR) and from healthy volunteers (control), from hospitals in four different countries (China, Spain, France, and Italy) were analyzed by GC-MS, LC -MS and NMR. Machine learning models (PCA and PLS-DA) were developed for predicting the diagnosis and prognosis of COVID-19 and identifying biomarkers associated with these outcomes.\n<bold>Results.<\/bold> A total of 1410 patient samples were analyzed. In all analyzed data, the PLS-DA model presented a diagnostic and prognostic accuracy of around 95%. A total of 23 biomarkers (e.g. spermidine, taurine, L-aspartic, L-glutamic, L-phenylalanine and xanthine, ornithine and ribothimidine) have been identified as being associated with the diagnosis and prognosis of COVID-19. Additionally, we also identified for the first time six new biomarkers (N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine and glycerol 1-myristate) that are also associated with the severity and diagnosis of COVID-19. These six new biomarkers were elevated in severe COVID-19 patients when compared to patients with mild disease or healthy volunteers.\n<bold>Conclusion:<\/bold> The PLS-DA model was able to miss the diagnosis and prognosis of COVID-19 around 95%. We also identified six new biomarkers that were increased in plasma and serum of COVID-19 patients (N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine and glycerol 1-myristate) and should be deeply evaluated as prognostic and diagnostic indicators of COVID-19.<\/p>","DOI":"10.21203\/rs.3.rs-3506910\/v1","type":"posted-content","created":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T17:52:52Z","timestamp":1699552372000},"source":"Crossref","is-referenced-by-count":0,"title":["Multi-omics data analysis of COVID-19 patients from Italy, China, Spain and France reveals new biomarkers for early diagnosis and prognosis of SARS-CoV-2 infection"],"prefix":"10.21203","author":[{"given":"Alexandre de F\u00e1tima","family":"Cobre","sequence":"first","affiliation":[{"name":"UFPR: Universidade Federal do Parana"}]},{"given":"Alexessander Couto","family":"Alves","sequence":"additional","affiliation":[{"name":"University of Surrey"}]},{"given":"Ana Raquel Manuel","family":"Gotine","sequence":"additional","affiliation":[{"name":"USP: Universidade de Sao Paulo"}]},{"given":"Karime Zeraik Abdalla","family":"Domingues","sequence":"additional","affiliation":[{"name":"UFPR: Universidade Federal do Parana"}]},{"given":"Raul Edison Luna","family":"Lazo","sequence":"additional","affiliation":[{"name":"UFPR: Universidade Federal do Parana"}]},{"given":"Luana Mota","family":"Ferreira","sequence":"additional","affiliation":[{"name":"UFPR: Universidade Federal do Parana"}]},{"given":"Fernanda Stumpf","family":"Tonin","sequence":"additional","affiliation":[{"name":"Instituto Polit\u00e9cnico de Lisboa: Instituto Politecnico de Lisboa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7049-4363","authenticated-orcid":false,"given":"Roberto","family":"Pontarolo","sequence":"additional","affiliation":[{"name":"Universidade Federal do Paran\u00e1: Universidade Federal do Parana"}]}],"member":"297","reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1016\/j.dsx.2021.04.007","article-title":"An overview, Diabetes Metab","volume":"15","author":"Raveendran AV","year":"2021","unstructured":"Raveendran AV, Jayadevan R, Sashidharan S, Long COVID (2021) An overview, Diabetes Metab. 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