Characterization of relapsing–remitting and chronic forms of experimental autoimmune encephalomyelitis in C57BL/6 mice
Bibliographic record
Abstract
Multiple sclerosis (MS) is an autoimmune, demyelinating disease of the central nervous system (CNS). Like MS, the animal model experimental autoimmune encephalomyelitis (EAE) is characterized by CNS inflammation and demyelination and can follow a relapsing-remitting (RR) or chronic (CH) disease course. The molecular and pathological differences that underlie these different forms of EAE are not fully understood. We have compared the differences in RR- and CH-EAE generated in the same mouse strain (C57BL/6) using the same antigen. At the peak of disease when mice in both groups have similar clinical scores, CH-EAE is associated with increased lesion burden, myelin loss, axonal damage, and chemokine/cytokine expression when compared with RR-EAE. We further showed that inflammation and myelin loss continue to worsen in later stages of CH-EAE, whereas these features are largely resolved at the equivalent stage in RR-EAE. Additionally, axonal loss at these later stages is more severe in CH-EAE than in RR-EAE. We also demonstrated that CH-EAE is associated with a greater predominance of CD8(+) T cells in the CNS that exhibit MOG(35-55) antigen specificity. These studies therefore showed that, as early as the peak stage of disease, RR- and CH-EAE differ remarkably in their immune cell profile, chemokine/cytokine responses, and histopathological features. These data also indicated that this model of CH-EAE exhibits pathological features of a chronic-progressive disease profile and suggested that the sustained chronic phenotype is due to a combination of axonal loss, myelin loss, and continuing inflammation.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".