Metformin treatment reduces inflammation, dysmyelination and disease severity in a mouse model of multiple sclerosis, experimental autoimmune encephalomyelitis
Bibliographic record
Abstract
Multiple sclerosis (MS) is an autoimmune disease characterized by inflammation, death or damage of oligodendrocytes, and axonal degeneration. Current MS treatments are non-curative, associated with undesired side-effects, and expensive, highlighting the need for expanded therapeutic options for patients. There is great interest in developing interventions using drugs or therapeutics to reduce symptom onset and protect pre-existing myelin. Metformin is a well-tolerated drug used to treat Type 2 diabetes that has pleiotropic effects in the central nervous system (CNS), including reducing inflammation, enhancing oligodendrogenesis, increasing the survival/proliferation of neural stem cells (NSCs), and increasing myelination. Here, we investigated whether metformin administration could improve functional outcomes, modulate oligodendrocyte precursor cells (OPCs), and reduce inflammation in a well-established mouse model of MS- experimental autoimmune encephalomyelitis (EAE). Male and female mice received metformin treatment at the time of EAE induction ("acute") or upon presentation of disease symptoms ("delayed"). We found that acute metformin treatment improved functional outcomes, concomitant with reduced microglia numbers and decreased dysmyelination. Conversely, delayed metformin treatment did not improve functional outcomes. Our findings reveal that metformin administration can improve EAE outcomes when administered before symptom onset in both sexes.
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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 | 0.000 |
| 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) | 0.001 | 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".