AAV Systems and Mouse Models for Investigating Ectopic Expression of Neurod1 in Transduced Cells at Subacute and Chronic Times Post-Ischemic Stroke
Bibliographic record
Abstract
Ectopic expression of neurogenic factors in vivo has emerged as a promising approach for replacing lost neurons in disease models. The use of neural basic helix-loop-helix (bHLH) transcription factors via non-propagating virus-like particle systems, including retrovirus, lentivirus, and adeno-associated virus (AAV), has been extensively reported. For in vivo experiments, AAVs are increasingly used due to their low pathogenicity and potential for translatability. This protocol describes two AAV systems for investigating the ectopic expression of transcription factors in transduced cells post-ischemic stroke. In both systems, Neurod1 expression is controlled by the short GFAP (gfaABC(1)D) promoter, which is upregulated in reactive astrocytes post-stroke as well as in endogenous neurons when combined with neurogenic factor expression. In the ischemic stroke model described, focal ischemia is induced by injecting endothelin-1 (ET-1) into the motor cortex of mice, creating a lesion surrounded by reactive GFAP-expressing astrocytes and surviving neurons. Intracerebral injections of AAV are performed to ectopically induce the expression of Neurod1 in the subacute (7 days) and chronic (21 days) phases post-stroke. Within weeks following AAV injection, a significantly higher number of neurons among transduced cells are identified in mice ectopically expressing Neurod1 compared to mice receiving AAV control viruses. The AAV-based strategies used replicated observed outcomes of increased numbers of neurons expressing the reporter gene in a model of mild-to-moderate cortical stroke. This protocol establishes a standard platform for exploring the effects of ectopic expression of transcription factors delivered with AAV-based systems, contributing to the understanding of neurogenic factor expression in the context of 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".