Metformin as an Endogenous Repair Strategy to Activate Neural Precursor Cells and Improve Cognition Following Brain Injury
Bibliographic record
Abstract
Neural stem and progenitor cells, collectively termed neural precursor cells (NPCs), reside in well-defined niches within the mammalian brain: the subventricular zone (SVZ) and the dentate gyrus (DG). NPCs give rise to new neurons throughout the lifespan and possess the ability to self-renew and give rise to all neural cell types. Exploiting this endogenous potential of NPCs is a promising target for repairing the brain after injury. Metformin, an FDA-approved drug used to treat type 2 diabetes, enhances the NPC pool and increases neurogenesis. Further, one week of metformin treatment improves motor recovery in a neonatal hypoxia-ischemia (H-I) model. Age and sex can affect the extent of damage and functional outcome following brain injury. Herein, we investigated the effect of these factors on the ability of metformin to expand the NPC pool and enhance neurogenesis. Our results show that metformin administration in uninjured animals led to age- and sex-dependent effects on NPC expansion and neurogenesis, and that these effects were mediated by niche-related hormones. The presence of estradiol led to a permissive environment with a resulting increase in the NPC pool following metformin treatment, while testosterone led to an inhibitory environment and lack of NPC pool expansion. We then investigated whether metformin treatment was effective at improving cognitive impairments in two models of childhood brain injury, H-I and cranial irradiation. Consistent with our results in uninjured mice, we found sex-dependent effects of metformin treatment following H-I injury, and sex- and brain region-dependent effects following cranial irradiation. These findings suggest that metformin represents a promising strategy for brain repair and highlights the importance of considering sex and age to optimize the translation of this approach.
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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) | 0.001 | 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".