P.115 Regenerative white matter effect of neurod1-based gene therapy in non-human primate stroke model
Bibliographic record
Abstract
Background: Stroke therapies remain an area of ongoing research. Gene therapies offer a novel approach to repair tissue damage, particularly NeuroD1-mediated astrocyte-to-neuron conversion, which regenerates functional neurons after ischemic injury. Here, we applied NeuroD1 therapy in a non-human primates (NHPs) stroke model to evaluate its effects on corticospinal tract (CST) recovery and motor performance. Methods: Eight NHPs underwent middle cerebral artery occlusion (MCAO). Fourteen days later, six animals received intracranial NeuroD1 treatment (three high-dose, three low-dose), while two received a control solution. Neurological and functional performance were assessed daily. MRI scans were performed at baseline and at 7, 30, 90, 120, and 240 days post-MCAO, with the bilateral CST reconstructed at each time point. All procedures followed Canadian Council of Animal Care guidelines and were approved by Queen’s University’s Animal Use Subcommittee. Results: We found that NHPs receiving the control solution exhibited poorer motor recovery and minimal CST reconstruction. In contrast, those treated with a low dose of NeuroD1 demonstrated motor and functional recovery along with CST reconstruction. Notably, animals receiving the higher dose showed the most significant overall recovery including a greater CST integrity. Conclusions: NeuroD1 treatment promotes white matter tract restoration and facilitates motor recovery following stroke.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".