The engineered AAV2-HBKO promotes non-invasive gene delivery to large brain regions beyond ultrasound targeted sites
Bibliographic record
Abstract
Magnetic resonance imaging-guided focused ultrasound combined with microbubbles injected in the bloodstream (MRIgFUS) temporarily increases the permeability of the blood-brain barrier (BBB), which facilitates the entry of intravenously administered adeno-associated viruses (AAVs) from the blood to targeted brain areas. To date, the properties of the AAVs used for MRIgFUS delivery resulted in cell transduction limited to MRIgFUS-targeted sites. Considering future clinical applications, strategies are needed to deliver genes to multiple locations and large brain volumes while creating minimal BBB modulation. Here we combine MRIgFUS with a vector that has enhanced biodistribution following brain entry, AAV2-HBKO, to mediate broad gene delivery to targeted brain regions at levels with potential therapeutic relevance. Expression of a reporter gene was achieved in 13% and 21% of all neurons present in the striatum and thalamus, respectively, while targeting only 28% of the brain regions with MRIgFUS. Compared with AAV9, MRIgFUS-mediated delivery of AAV2-HBKO showed greater diffusion in the brain and a higher percentage of the neurons expressing the transgene. MRIgFUS AAV2-HBKO gene delivery to the brain has the potential to reach levels that are functionally and clinically relevant, and this even when using relatively low intravenous AAV dosages, compared with what is currently used in clinical trials.
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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) | 0.001 | 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 | 0.000 |
| 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".