Exploring Neural Stem Cell Activation in Recovery Strategies to Treat Neonatal Brain Injury
Bibliographic record
Abstract
Harnessing neural stem cells and their progeny—together termed neural precursor cells (NPCs)—as a source of replacement cells after injury is a promising strategy for neural repair. I explored if endogenous NPC activation underlies recovery after neonatal stroke using two approaches: the drug metformin, and the neurorehabilitation strategy constraint-induced movement therapy (CIMT).Expansion of the NPC pool correlates with improved functional outcomes following neonatal stroke, and treatment with the drug metformin has been shown to expand the NPC pool and improve motor behaviour. To explore the role of activated NPCs in the metformin-mediated recovery, I used a GFAP-TK mouse model and designed a strategy to ablate the largest pool of neural stem cells (NSCs) in the postnatal brain in vivo, the definitive neural stem cells (dNSCs). A ~90% depletion of dNSCs in vivo is insufficient to prevent metformin-mediated sensorimotor and cognitive recovery following neonatal stroke. An increase in proliferating neuroblasts (DCX+Ki67+ cells) was observed in the NSC niche after stroke+ablation+metformin, suggesting neuroblast activation may underlie metformin-mediated recovery. Rehabilitation has been shown to confer improved motor outcomes after stroke. We designed a CIMT approach to treat mice following neonatal stroke which involved the administration of Botox injections to induce paralysis in the unaffected forelimb. I designed a CIMT approach in neonatal mice using Botox injections to induce paralysis in the unaffected forelimb at a time when NPC expansion is observed and asked if there was a correlation between NPC activation and CIMT-mediated recovery. CIMT improved motor function post-stroke and NPC activation was not observed at the time of recovery. Recovery was consistent with increased proliferation of microglia/macrophages in the cortex, suggesting that modulating microglia activity may play an important role in the CIMT-induced recovery. Using the novel paradigm developed in my first aim, NSC depletion studies revealed a novel NSC population in the neonatal and adult brain. I characterized the population and found it to be an intermediate neural stem cell (iNSC) between two well characterized populations in the neural stem cell lineage. This discovery redefines the neural stem cell lineage and may inform novel strategies to promote brain repair.
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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".