Long COVID: Deep single-cell immunophenotyping and machine learning reveal a general signature for fatigue.

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Title: Long COVID: Deep single-cell immunophenotyping and machine learning reveal a general signature for fatigue.
Authors: Sommen SL; Department of Pediatrics, Akershus University Hospital, Lørenskog, Norway.; University of Oslo, Oslo, Norway., Segtnan S; University of Oslo, Oslo, Norway., Selvakumar J; Department of Pediatrics, Akershus University Hospital, Lørenskog, Norway.; Institute for Clinical Medicine, University of Oslo, Oslo, Norway., Havdal LB; Department of Pediatrics, Akershus University Hospital, Lørenskog, Norway., Stiansen-Sonerud T; Department of Pediatrics, Akershus University Hospital, Lørenskog, Norway.; Department of Clinical Molecular Biology (EpiGen), University of Oslo and Akershus University Hospital, Lørenskog, Norway., Gjerstad J; Department of Research and Development in Mental Health, Akershus University Hospital, Lørenskog, Norway., Mjaaland S; Division of Infection Control, Norwegian Institute of Public Health, Lovisneberggata 8, Room 4361, Oslo, 0456, Norway., Nygaard UC; Division of Infection Control, Norwegian Institute of Public Health, Lovisneberggata 8, Room 4361, Oslo, 0456, Norway., Wyller VBB; Department of Pediatrics, Akershus University Hospital, Lørenskog, Norway.; Institute for Clinical Medicine, University of Oslo, Oslo, Norway., Mukherjee R; Division of Infection Control, Norwegian Institute of Public Health, Lovisneberggata 8, Room 4361, Oslo, 0456, Norway. ratnadeep.mukherjee@fhi.no., Berven LL; Department of Pediatrics, Akershus University Hospital, Lørenskog, Norway.
Source: Journal of translational medicine [J Transl Med] 2026 Apr 22; Vol. 24 (1). Date of Electronic Publication: 2026 Apr 22.
Publication Type: Journal Article
Journal Info: Publisher: BioMed Central Country of Publication: England NLM ID: 101190741 Publication Model: Electronic Cited Medium: Internet ISSN: 1479-5876 (Electronic) Linking ISSN: 14795876 NLM ISO Abbreviation: J Transl Med Subsets: MEDLINE; In Process
Database: MEDLINE Ultimate
Description
ISSN:1479-5876
DOI:10.1186/s12967-026-08149-3