Immune exhaustion in ME/CFS and long COVID
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it