P.017 EBNA1 titres are elevated in radiologically isolated syndrome and correlate with plasma GFAP
Bibliographic record
Abstract
Background: Epstein-Barr virus (EBV) infection is believed to be a critical prerequisite for the development of multiple sclerosis (MS). This study aims to investigate whether anti-EBV titres are elevated before the onset of MS symptoms in people with radiologically isolated syndrome (pwRIS) and to evaluate their association with markers of adverse clinical outcomes. Methods: Epstein-Barr nuclear antigen 1 (EBNA1) and viral capsid antigen (VCA) titres were quantified in a cohort of 47 pwRIS and 24 healthy controls using Enzyme-Linked Immuno-Sorbent Assay. Plasma glial fibrillary acidic protein (GFAP) and neurofilament light protein (NfL) were measured using single-molecule array. MRI lesion metrics and the development of MS symptoms over time were also evaluated. Results: EBNA1 titres were higher pwRIS compared to healthy controls (p=0.038), while VCA titres were not (p=0.237). A positive correlation was observed between EBNA1 titres and plasma GFAP in pwRIS (p=0.005). Neither EBNA1 nor VCA titres correlated with NfL. MRI lesion measures and the development of MS symptoms did not show any significant relationship with EBNA1 or VCA titres. Conclusions: Eelevated EBNA1 titres are detectable prior to MS symptom onset and correlate with GFAP, a biomarker associated with worse clinical outcomes. However, their role in disease progression and clinical outcomes requires further investigation.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".