Targeted Delivery of Self-Complementary Adeno-Associated Virus Serotype 9 to the Brain, Using Magnetic Resonance Imaging-Guided Focused Ultrasound
Bibliographic record
Abstract
Noninvasive drug delivery to the brain remains a major challenge for the treatment of neurological disorders. Transcranial focused ultrasound combined with lipid-coated gas microspheres injected into the bloodstream has been shown to increase the permeability of the blood-brain barrier locally and transiently. Coupled with magnetic resonance imaging, ultrasound can be guided to allow therapeutics administered in the blood to reach brain regions of interest. Using this approach, we perform gene transfer from the blood to specific regions of the mouse brain. Focused ultrasound was targeted to the right hemisphere, at multiple foci, or restricted to one focal point of the hippocampus or the striatum. Doses from 5 × 10(8) to 1.25 × 10(10) vector genomes per gram (VG/g) of self-complementary adeno-associated virus serotype 9 carrying the green fluorescent protein were injected into the tail vein. A dose of 2.5 × 10(9) VG/g was optimal to express the transgene, 12 days later, in neurons, astrocytes, and oligodendrocytes in brain regions targeted with ultrasound, while minimizing the infection of peripheral organs. In the hippocampus and striatum, predominantly neurons and astrocytes were infected, respectively. Transcranial focused ultrasound applications could fulfill a long-term goal of gene therapy: delivering vectors to diseased brain areas directly from the circulation, in a noninvasive manner.
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) | 0.000 | 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 | 0.001 | 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".