Multimodal Enhancement of Remyelination by Exercise with a Pivotal Role for Oligodendroglial PGC1α
Bibliographic record
Abstract
Remyelination is a multistep regenerative process that results in the reformation of myelin sheaths around demyelinated axons and is a critical therapeutic target. Here we show that immediate access to a running wheel following toxin-induced demyelination in mice enhances oligodendrogenesis, the rate of remyelination, and the proportion of remyelinated axons. RNA sequencing suggests broad activation of pro-remyelination pathways including phagocytosis by exercise and highlights peroxisome proliferator-activated receptor gamma co-activator 1-alpha (PGC1α) activation. By immunohistochemistry and cell type-specific conditional deletion, we confirmed PGC1α within oligodendrocytes as a transiently expressed factor required for the rate of myelin thickening by exercise. We validated the exercise-enhanced clearance of inhibitory lipid debris from lesions. Finally, exercise works in parallel with the remyelinating medication clemastine to produce complete remyelination of lesions. Our study demonstrates physical activity as an integrative means to enhance remyelination and details a multimodal mechanism including the pivotal PGC1α-dependent enhancement of myelin thickness.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Mouse study of exercise-enhanced remyelination; neuroscience.
This is a biomedical experiment on exercise and remyelination, not a study of research practice.
Neuroscience mouse study of exercise-enhanced remyelination mechanisms.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".