Galanin Transgenic Mice with Elevated Circulating Galanin Levels Alleviate Demyelination in a Cuprizone-Induced MS Mouse Model
Bibliographic record
Abstract
Multiple Sclerosis (MS) is a demyelinating autoimmune disease of the central nervous system (CNS) with a presumed autoimmune etiology. Approved treatments for MS are immunoregulatory and are able to reduce the inflammatory components of the disease. However, these treatments do not suppress progressive clinical disability. Approaches that directly protect myelin-producing oligodendrocytes and enhance remyelination are likely to improve long-term outcomes and reduce the rate of axonal damage. Galanin (GAL) is a bioactive neuropeptide that is widely distributed throughout the nervous system and has diverse neuromodulatory effects. In this study, using the cuprizone (CPZ) demyelination model of MS, we demonstrate that GAL has pronounced neuroprotective effects with respect to demyelination and remyelination. Using our GAL transgenic mouse (GAL-Tg), we identified a novel attenuation of OLs against CPZ induced demyelination, which was exerted independently of progenitor cells. Alleviation of myelin breakdown in the GAL-Tg mice was observed to be significant. Furthermore, we observed changes in the expression of the GAL receptor GalR1 during the demyelination and remyelination processes. Our data strongly indicate that GAL has the capacity to influence the outcome of primary insults that directly target OLs, as opposed to cases where immune activation is the primary pathogenic event. Taken together, these results suggest that GAL is a promising next-generation target for the treatment of 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".