Metformin Improves Functional Outcomes, Activates Neural Precursor Cells, and Modulates Microglia in a Sex-Dependent Manner After Spinal Cord Injury
Bibliographic record
Abstract
Spinal cord injury (SCI) results in devastating patient outcomes with few treatment options. A promising approach to improve outcomes following SCI involves the activation of endogenous precursor populations including neural stem and progenitor cells (NSPCs) which are located in the periventricular zone (PVZ), and oligodendrocyte precursor cells (OPCs) found throughout the parenchyma. In the adult spinal cord, resident NSPCs are primarily mitotically quiescent and aneurogenic, while OPCs contribute to ongoing oligodendrogenesis into adulthood. Each of these populations is responsive to SCI, increasing their proliferation and migration to the site of injury; however, their activation is not sufficient to support functional recovery. Previous work has shown that administration of the FDA-approved drug metformin is effective at promoting endogenous brain repair following injury, and this is correlated with enhanced NSPC activation. Here, we ask whether metformin can promote functional recovery and neural repair following SCI in both males and females. Our results reveal that acute, but not delayed metformin administration improves functional outcomes following SCI in both sexes. The functional improvement is concomitant with OPC activation and oligodendrogenesis. Our data also reveal sex-dependent effects of metformin following SCI with increased activation of NSPCs in females and reduced microglia activation in males. Taken together, these findings support metformin as a viable therapeutic strategy following SCI and highlight its pleiotropic effects in the spinal cord.
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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) | 0.000 | 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 | 0.001 |
| 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".