| Autor: |
Maitray A. Patel, Michael J. Knauer, Michael Nicholson, Mark Daley, Logan R. Van Nynatten, Gediminas Cepinskas, Douglas D. Fraser |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Molecular Medicine, Vol 29, Iss 1, Pp 1-15 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1528-3658 |
| DOI: |
10.1186/s10020-023-00610-z |
| Popis: |
Abstract Background Survivors of acute COVID-19 often suffer prolonged, diffuse symptoms post-infection, referred to as “Long-COVID”. A lack of Long-COVID biomarkers and pathophysiological mechanisms limits effective diagnosis, treatment and disease surveillance. We performed targeted proteomics and machine learning analyses to identify novel blood biomarkers of Long-COVID. Methods A case–control study comparing the expression of 2925 unique blood proteins in Long-COVID outpatients versus COVID-19 inpatients and healthy control subjects. Targeted proteomics was accomplished with proximity extension assays, and machine learning was used to identify the most important proteins for identifying Long-COVID patients. Organ system and cell type expression patterns were identified with Natural Language Processing (NLP) of the UniProt Knowledgebase. Results Machine learning analysis identified 119 relevant proteins for differentiating Long-COVID outpatients (Bonferonni corrected P |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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Full text from SpringerLink