F.5 A neurotransmitter-dependent mechanism of ependymal cell activation: Insights into a novel therapeutic target for spinal cord injury
Bibliographic record
Abstract
Background: The drivers that activate endogenous ependymal-derived neural stem/progenitor cells (epNSPCs) remain unknown. Understanding the mechanisms that govern the biology of these cells is critical in developing a therapeutic strategy to harness their regenerative potential after injury. Methods: FoxJ1-CreER-tdTomato reporter mice were used for epNSPC lineage tracing. A conditional genetic knock-out mouse line of glutamate-subtype AMPA receptor (AMPAR) subunits in epNSPCs was generated. Electrophysiological properties were assessed using single cell patch clamp and slice culture recordings. For in vivo studies, mice underwent cervical SCI. To examine the effect of positive modulation of AMPARs, mice received the ampakine CX546 or vehicle and underwent electrophysiological testing, behavioural assessment and spinal cord extraction. Results: Glutamate excitotoxicity, a hallmark in the pathogenesis of acute SCI, drives epNSPCs activation via AMPARs. Genetic knock-out of AMPARs in epNSPCs inhibits their activation following SCI. Positive pharmacological modulation of AMPARs after SCI enhances the migration and differentiation of epNSPCs, increases neuronal sparing and improves long-term locomotor/forelimb function. SCI decreases the excitability of corticospinal tract projections, which is improved with positive AMPAR modulation. Conclusions: Glutamatergic signaling via AMPARs is an important mediator of epNSPC activation after injury. Pharmacological targeting of this mechanism can be used to enhance endogenous regeneration and improve recovery post-SCI.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".