Reduced Ischemic Injury After Stroke in Mice by Angiogenic Gene Delivery Via Ultrasound-Targeted Microbubble Destruction
Bibliographic record
Abstract
Angiogenic gene therapy in patients with cerebral infarcts may have clinical benefit, but its potential is diminished by the difficulty of introducing genes into the brain. We evaluated the safety and efficacy of ultrasound-targeted microbubble destruction (UTMD) for delivery of genes to the brains of normal mice and after transient middle cerebral artery occlusion. In normal mice, disruption of the blood-brain barrier detected with trypan blue staining was reversible within 24 hours of a single UTMD administration. Expression of reporter genes in the brain after UTMD demonstrated successful targeted gene delivery and transfection. Decreased neurologic function after transient middle cerebral artery occlusion was attenuated versus controls at 7 days after UTMD delivery of vascular endothelial growth factor. Ultrasound-targeted microbubble destruction delivery of the VEGF gene resulted in decreased infarct areas, increased vessel density, and reduced apoptosis versus controls. There was no evidence of permanent brain injury throughout the study. Thus, UTMD was a safe, minimally invasive, effective technique for gene delivery to the brain. Vascular endothelial growth factor transfection of brain cells conferred beneficial effects on histopathologic parameters and neurologic function, and stimulated angiogenesis in a mouse stroke model.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".