Lesion stage-dependent causes for impaired remyelination in MS
Bibliographic record
Abstract
Multiple sclerosis (MS) is the most frequent demyelinating disease and a leading cause for disability in young adults. Despite significant advances in immunotherapies in recent years, disease progression still cannot be prevented. Remyelination, meaning the formation of new myelin sheaths after a demyelinating event, can fail in MS lesions. Impaired differentiation of progenitor cells into myelinating oligodendrocytes may contribute to remyelination failure and, therefore, the development of pharmacological approaches which promote oligodendroglial differentiation and by that remyelination, represents a promising new treatment approach. However, this generally accepted concept has been challenged recently. To further understand mechanisms contributing to remyelination failure in MS, we combined detailed histological analyses assessing oligodendroglial cell numbers, presence of remyelination as well as the inflammatory environment in different MS lesion types in white matter with in vitro experiments using induced-pluripotent stem cell (iPSC)-derived oligodendrocytes (hiOL) and supernatants from polarized human microglia. Our findings suggest that there are multiple reasons for remyelination failure in MS which are dependent on lesion stage. These include lack of myelin sheath formation despite the presence of mature oligodendrocytes in a subset of active lesions as well as oligodendroglial loss and a hostile tissue environment in mixed active/inactive lesions. Therefore, we conclude that better in vivo and in vitro models which mimic the pathological hallmarks of the different MS lesion types are required for the successful development of remyelination promoting drugs.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".