Exploratory study on autoantibodies to arginine-rich human peptides mimicking Epstein-Barr virus in women with post-COVID and myalgic encephalomyelitis/chronic fatigue syndrome
Bibliographic record
Abstract
Introduction: Epstein-Barr virus (EBV) infection is a well-established trigger and risk factor for both myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and post-COVID syndrome (PCS). In previous studies, we identified elevated IgG responses to arginine-rich (poly-R) sequences within the EBV nuclear antigens EBNA4 and EBNA6 in post-infectious ME/CFS (piME/CFS). Building on these findings, this exploratory study examines IgG reactivity to poly-R-containing EBV-derived peptides and homologous human peptides in women with PCS and ME/CFS. Methods: IgG reactivity to poly-R containing peptides derived from EBNA4 and EBNA6, and homologous human 15-mer peptides and the corresponding full-length proteins, was assessed using a cytometric bead array (CBA) and a multiplex dot-blot assay. Serum samples were analyzed from 45 female PCS patients diagnosed according to WHO criteria, including 26 who also met the Canadian Consensus criteria for ME/CFS (pcME/CFS), 36 female patients with non-COVID post-infectious ME/CFS (piME/CFS), and 34 female healthy controls (HC). Results: Autoantibodies targeting poly-R peptide sequences of the neuronal antigen SRRM3, the ion channel SLC24A3, TGF-β signaling regulator TSPLY2, and the angiogenesis-related protein TSPYL5, as well as full-length α-adrenergic receptor (ADRA) proteins, were more frequently detected in patient groups. Several of these autoantibodies showed positive correlations with core symptoms, including autonomic dysfunction, fatigue, cognitive impairment, and pain. Conclusion: This exploratory study identify autoantibodies directed against EBV mimicking arginine-rich sequences in human proteins, suggesting a potential role for molecular mimicry in the pathogenesis of PCS and ME/CFS.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".