Local magnetic delivery of adeno-associated virus AAV2(quad Y-F)-mediated BDNF gene therapy restores hearing after noise injury
Bibliographic record
Abstract
Moderate noise exposure may cause acute loss of cochlear synapses without affecting the cochlear hair cells and hearing threshold; thus, it remains "hidden" to standard clinical tests. This cochlear synaptopathy is one of the main pathologies of noise-induced hearing loss (NIHL). There is no effective treatment for NIHL, mainly because of the lack of a proper drug-delivery technique. We hypothesized that local magnetic delivery of gene therapy into the inner ear could be beneficial for NIHL. In this study, we used superparamagnetic iron oxide nanoparticles (SPIONs) and a recombinant adeno-associated virus (AAV) vector (AAV2(quad Y-F)) to deliver brain-derived neurotrophic factor (BDNF) gene therapy into the rat inner ear via minimally invasive magnetic targeting. We found that the magnetic targeting effectively accumulates and distributes the SPION-tagged AAV2(quad Y-F)-BDNF vector into the inner ear. We also found that AAV2(quad Y-F) efficiently transfects cochlear hair cells and enhances BDNF gene expression. Enhanced BDNF gene expression substantially recovers noise-induced BDNF gene downregulation, auditory brainstem response (ABR) wave I amplitude reduction, and synapse loss. These results suggest that magnetic targeting of AAV2(quad Y-F)-mediated BDNF gene therapy could reverse cochlear synaptopathy after NIHL.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".