Antibodies from Inflamed Central Nervous System Tissue Recognize Myelin Oligodendrocyte Glycoprotein
Bibliographic record
Abstract
Autoantibodies to myelin oligodendrocyte glycoprotein (MOG) can induce demyelination and oligodendrocyte loss in models of multiple sclerosis (MS). Whether anti-MOG Abs play a similar role in patients with MS or inflammatory CNS diseases by epitope spreading is unclear. We have therefore examined whether autoantibodies that bind properly folded MOG protein are present in the CNS parenchyma of MS patients. IgG was purified from CNS tissue of 14 postmortem cases of MS and 8 control cases, including cases of encephalitis. Binding was assessed using two independent assays, a fluorescence-based solid-phase assay and a solution-phase RIA. MOG autoantibodies were identified in IgG purified from CNS tissue by solid-phase immunoassay in 7 of 14 cases with MS and 1 case of subacute sclerosing panencephalitis, but not in IgG from noninflamed control tissue. This finding was confirmed with a solution-phase RIA, which measures higher affinity autoantibodies. These data demonstrate that autoantibodies recognizing MOG are present in substantially higher concentrations in the CNS parenchyma compared with cerebrospinal fluid and serum in subjects with MS, indicating that local production/accumulation is an important aspect of autoantibody-mediated pathology in demyelinating CNS diseases. Moreover, chronic inflammatory CNS disease may induce autoantibodies by virtue of epitope spreading.
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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) | 0.002 | 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".