ArticleJournal of translational medicine2026
Long COVID: Deep single-cell immunophenotyping and machine learning reveal a general signature for fatigue.
Article in Journal of translational medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Effect of Long COVID-19: Self-Reported Questionnaires and Objective Olfactory and Gustatory Evaluations.Diseases (Basel, Switzerland) · 2026Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe post COVID-19 condition, commonly referred to as “Long COVID” (LC), is a constellation of long-lasting and debilitating symptoms following acute SARS-CoV-2 infection, which closely resembles other post-infective fatigue states. The underlying immunological disturbances of LC are poorly understood. Multiple explanatory mechanisms, such as persisting SARS-CoV-2 reservoirs, reactivation of latent viruses, endothelial dysfunction, auto-antibodies, tissue damage, and immune abnormalities, have been proposed but remain incompletely characterized, particularly in younger populations.
methodsThe present study included 12 to 25-year-old females with and without mild SARS-CoV-2 infection, who were prospectively followed for six months after infection and assessed according to the WHO definition of post COVID-19 condition, resulting in four groups (“Long COVID” (LC), recovered convalescents (RC), fatigued controls (FC), healthy controls (HC)). Peripheral blood mononuclear cells were either stimulated with Phorbol-12-myristate-13-acetate/ionomycin or left unstimulated, and analyzed using a 41-antibody CyTOF panel, unsupervised clustering with FlowSOM, dimensionality reduction via UMAP, polyfunctionality assessment with COMPASS, and machine learning for identifying correlates of LC and fatigue. All participants were female with a median age of 18.5 years.
resultsHigher frequencies of Terminal NK cells were associated with LC and FC, showing enhanced polyfunctional responses upon stimulation. Increased CD4 + T cell activation and exhaustion were observed in both LC and FC, with elevated effector memory and effector T cells expressing PD-1, alongside decreased marginal zone B cells and transitional B cells. Machine learning analysis revealed terminal NK cells as the most important feature for predicting fatigue. Importantly, no unique LC-specific immune changes were detected.
conclusionOur results point to a shared underlying pathophysiology in LC and other forms of fatigue. The absence of unique LC-related immune changes supports the notion that LC represents a specific example of post-infective fatigue syndrome triggered by SARS-CoV-2 in susceptible individuals. Innate and adaptive immune dysregulation, particularly higher levels of hyperresponsive terminal NK cells, is most striking and could serve as a general fatigue marker rather than an LC-specific biomarker. These findings highlight the need for further studies to correlate immune perturbations with symptom clusters and inform diagnostic and treatment strategies for fatigue states.
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