P.116 Improving the NeuroD1-AAV-based gene therapy intracerebral injection protocol for optimal neuronal recovery
Bibliographic record
Abstract
Background: Ischemic stroke increases the number of glial cells, such as astrocytes, and causes neuronal death, disrupting the neuron-to-glia balance, contributing to neurodegeneration. Treatment with NeuroD-adeno-associated virus (NeuroD1-AVV) may enhance neuronal transdifferentiation and improve motor function, but the optimal administration protocol for the drug has yet to be determined. Methods: Non-human primates (NHPs) underwent middle cerebral occlusion surgery. Fourteen days poststroke, subjects received NeuroD1-AVV according to two distinct protocols: Three high doses and three low doses. Neurological deficits and cognitive performance were measured using the NHP stroke scale and coloured glove shift of set task, respectively. Nine months post-stroke, NHPs were euthanized. Brains were harvested and stained for neuronal (NEUN and MAP2) and glial (GFAP, IBA1) markers using immunofluorescence techniques. Results: Our results indicate that both protocols effectively rebalance the neuron-to-glia cell ratio by decreasing GFAP+ cells in the P1 and P2 NHPS ipsilateral hemispheres. No cognitive performance differences were found across groups; however, P2 had better NHPSS outcomes from months 2 to 9. Conclusions: The findings support both injection protocols in restoring histological balance, with P2 being more effective for motor function rehabilitation. Investigations into neuronal functionality and development levels continue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".