Animal models to investigate the effects of inflammation on remyelination in multiple sclerosis
Bibliographic record
Abstract
Multiple sclerosis (MS) is a chronic inflammatory, demyelinating, and neurodegenerative disease of the central nervous system (CNS). In people with MS, impaired remyelination and axonal loss lead to debilitating long-term neurologic deficits. Current MS disease-modifying drugs mainly target peripheral immune cells and have demonstrated little efficacy for neuroprotection or promoting repair. To elucidate the pathological mechanisms and test therapeutic interventions, multiple animal models have been developed to recapitulate specific aspects of MS pathology, particularly the acute inflammatory stage. However, there are few animal models that facilitate the study of remyelination in the presence of inflammation, and none fully replicate the biology of chronic demyelination in MS. In this review, we describe the animal models that have provided insight into the mechanisms underlying demyelination, myelin repair, and potential therapeutic targets for remyelination. We highlight the limitations of studying remyelination in toxin-based demyelination models and discuss the combinatorial models that recapitulate the inflammatory microenvironment, which is now recognized to be a major inhibitor of remyelination mechanisms. These models may be useful in identifying novel therapeutics that promote CNS remyelination in inflammatory diseases such as 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".