Immune exhaustion in ME/CFS and long COVID
Bibliographic record
Abstract
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and long COVID are debilitating multisystemic conditions sharing similarities in immune dysregulation and cellular signaling pathways contributing to the pathophysiology. In this study, immune exhaustion gene expression was investigated in participants with ME/CFS or long COVID concurrently. RNA was extracted from peripheral blood mononuclear cells isolated from participants with ME/CFS (n = 14), participants with long COVID (n = 15), and healthy controls (n = 18). Participants with ME/CFS were included according to Canadian Consensus Criteria. Participants with long COVID were eligible according to the case definition for "Post COVID-19 Condition" published by the World Health Organization. RNA was analyzed using the NanoString nCounter Immune Exhaustion gene expression panel. Differential gene expression analysis in ME/CFS revealed downregulated IFN signaling and immunoglobulin genes, and this suggested a state of immune suppression. Pathway analysis implicated dysregulated macrophage activation, cytokine production, and immunodeficiency signaling. Long COVID samples exhibited dysregulated expression of genes regarding antigen presentation, cytokine signaling, and immune activation. Differentially expressed genes were associated with antigen presentation, B cell development, macrophage activation, and cytokine signaling. This investigation elucidates the intricate role of both adaptive and innate immune dysregulation underlying ME/CFS and long COVID, emphasizing the potential importance of immune exhaustion in disease progression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".