Serum autoantibodies to myelin peptides distinguish acute disseminated encephalomyelitis from relapsing– remitting multiple sclerosis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Acute disseminated encephalomyelitis (ADEM) and relapsing-remitting multiple sclerosis (RRMS) share overlapping clinical, radiologic and laboratory features at onset. Because autoantibodies may contribute to the pathogenesis of both diseases, we sought to identify autoantibody biomarkers that are capable of distinguishing them. METHODS: We used custom antigen arrays to profile anti-myelin-peptide autoantibodies in sera derived from individuals with pediatric ADEM (n = 15), pediatric multiple sclerosis (Ped MS; n = 11) and adult MS (n = 15). Using isotype-specific secondary antibodies, we profiled both IgG and IgM reactivities. We used Statistical Analysis of Microarrays software to confirm the differences in autoantibody reactivity profiles between ADEM and MS samples. We used Prediction Analysis of Microarrays software to generate and validate prediction algorithms, based on the autoantibody reactivity profiles. RESULTS: ADEM was characterized by IgG autoantibodies targeting epitopes derived from myelin basic protein, proteolipid protein, myelin-associated oligodendrocyte basic glycoprotein, and alpha-B-crystallin. In contrast, MS was characterized by IgM autoantibodies targeting myelin basic protein, proteolipid protein, myelin-associated oligodendrocyte basic glycoprotein and oligodendrocyte-specific protein. We generated and validated prediction algorithms that distinguish ADEM serum (sensitivity 62-86%; specificity 56-79%) from MS serum (sensitivity 40-87%; specificity 62-86%) on the basis of combined IgG and IgM anti-myelin autoantibody reactivity to a small number of myelin peptides. CONCLUSIONS: Combined profiles of serum IgG and IgM autoantibodies identified myelin antigens that may be useful for distinguishing MS from ADEM. Further studies are required to establish clinical utility. Further biological assays are required to delineate the pathogenic potential of these antibodies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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".