Inner Ear Gene Transfection in Neonatal Mice Using Adeno-Associated Viral Vector: A Comparison of Two Approaches
Bibliographic record
Abstract
Local gene transfection is a promising technique for the prevention and/or correction of inner ear diseases, particularly those resulting from genetic defects. Adeno-associated virus (AAV) is an ideal viral vector for inner ear gene transfection because of its safety, stability, long-lasting expression, and its high tropism for many different cell types. Recently, a new generation of AAV vectors with a tyrosine mutation (mut-AAV) has demonstrated significant improvement in transfection efficiency. A method for inner ear gene transfection via the intact round window membrane (RWM) has been developed in our laboratory. This method has not been tested in neonatal mice, an important species for the study of inherited hearing loss. Following a preliminary study to optimize the experimental protocol in order to reduce mortality, the present study investigated inner ear gene transfection in mice at postnatal day 7. We compared transfection efficiency, the safety of the scala tympani injection via RWM puncture, and the trans-RWM diffusion following partial digestion with an enzyme technique. The results revealed that approximately 47% of inner hair cells (IHCs) and 17% of outer hair cells (OHCs) were transfected via the trans-RWM approach. Transfection efficiency via RWM puncture (58% and 19% for IHCs and OHCs, respectively) was slightly higher, but the difference was not significant.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".