Therapeutic Arteriogenesis by Ultrasound-Mediated VEGF <sub>165</sub> Plasmid Gene Delivery to Chronically Ischemic Skeletal Muscle
Bibliographic record
Abstract
Current methods of gene delivery for therapeutic angiogenesis are invasive, requiring either intraarterial or intramuscular administration. A noninvasive method of gene delivery has been developed using ultrasound-mediated destruction of intravenously administered DNA-bearing carrier microbubbles during their microcirculatory transit. Here we show that chronic ischemia could be markedly improved by ultrasound-mediated destruction of microbubbles bearing vascular endothelial growth factor-165 (VEGF(165)) plasmid DNA. Using a model of severe chronic hindlimb ischemia in rats, we demonstrated that ultrasound mediated VEGF(165)/green fluorescent protein (GFP) plasmid delivery resulted in a significant improvement in microvascular blood flow by contrast-enhanced ultrasound, and an increased vessel density by fluorescent microangiography, with minimal changes in control groups. The improvement in tissue perfusion was attributed predominantly to increases in noncapillary blood volume or arteriogenesis, with perfusion peaking at 14 days after delivery, followed by a partial regression of neovascularization at 6 weeks. Transfection was localized predominantly to the vascular endothelium of arterioles in treated ischemic muscle. RT-PCR confirmed the presence of VEGF(165)/GFP mRNA within treated ischemic muscle, being highest at day 3 postdelivery, and subsequently decreasing, becoming almost undetectable by 6 weeks. We found a modulation of endogenous growth factor expression in VEGF-treated ischemic muscle, consistent with a biologic effect of ultrasound mediated gene delivery. The results of our study demonstrate the utility of ultrasonic destruction of plasmid-bearing microbubbles to induce therapeutic arteriogenesis in the setting of severe chronic ischemia.
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 | 0.000 | 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".