Myelin oligodendrocyte glycoprotein–specific T and B cells cooperate to induce a Devic-like disease in mice
Bibliographic record
Abstract
Multiple sclerosis (MS) is a clinically and pathologically heterogeneous inflammatory/demyelinating disease of the CNS. In the MS variant Devic disease, lesions are predominantly found in the optic nerves and spinal cord but not the brain. The immunological bases of the different forms of MS are unknown. We previously generated myelin oligodendrocyte glycoprotein-specific (MOG-specific) TCR transgenic mice (TCRMOG mice; also referred to as 2D2 mice) and reported that a large proportion of these mice develop spontaneous isolated optic neuritis. We have now crossed the TCRMOG mice with MOG-specific Ig heavy-chain knock-in mice (IgHMOG mice; also referred to as Th mice), in which one-third of the B cells are specific for MOG. In these mice, MOG-specific B cells are very efficient in presenting MOG to the transgenic T cells and undergo class switching to IgG1 in the presence of the transgenic T cells. Sixty percent of TCRMOG x IgHMOG mice spontaneously developed a severe form of experimental autoimmune encephalomyelitis (EAE). Histological examination of the CNS revealed a selective distribution of meningeal and parenchymal inflammatory lesions in the spinal cord and optic nerves. Thus, CNS antigen-specific T and B cells cooperate to induce a distinct clinicopathologic EAE pattern that closely replicates human Devic disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".