Age-Dependent B Cell Autoimmunity to a Myelin Surface Antigen in Pediatric Multiple Sclerosis
Bibliographic record
Abstract
Multiple sclerosis (MS) typically manifests in early to mid adulthood, but there is increasing recognition of pediatric-onset MS, aided by improvements in imaging techniques. The immunological mechanisms of disease are largely unexplored in pediatric-onset MS, in part because studies have historically focused on adult-onset disease. We investigated autoantibodies to myelin surface Ags in a large cohort of pediatric MS cases by flow cytometric labeling of transfectants that expressed different myelin proteins. Although Abs to native myelin oligodendrocyte glycoprotein (MOG) were uncommon among adult-onset patients, a subset of pediatric patients had serum Abs that brightly labeled the MOG transfectant. Abs to two other myelin surface Ags were largely absent. Affinity purification of MOG Abs as well as competition of binding with soluble MOG documented their binding specificity. Such affinity purified Abs labeled myelin and glial cells in human CNS white matter as well as myelinated axons in gray matter. The prevalence of such autoantibodies was highest among patients with a very early onset of MS: 38.7% of patients less than 10 years of age at disease onset had MOG Abs, compared with 14.7% of patients in the 10- to 18-year age group. B cell autoimmunity to this myelin surface Ag is therefore most common in patients with a very early onset of MS.
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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 | 0.001 | 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".