Prospect of ultrasound-mediated gene delivery in cardiovascular applications
Bibliographic record
Abstract
INTRODUCTION: The field of regenerative medicine has evolved over the years, investigating gene and stem/progenitor cell therapies to help address the increasing burden of cardiovascular disease (CVD). While the lack of success of gene therapy in clinical trials has dampened enthusiasm, the search continues for a successful and translatable gene therapy strategy for CVD. Ultrasound-mediated gene delivery (UMGD) is a non-invasive technique for gene delivery that utilizes gene-bearing carrier microbubbles and high power ultrasound to facilitate transfection in vivo. Many pre-clinical studies have shown benefit in animal models of CVD, but this has yet to be translated to human applications. AREAS COVERED: In this review, the basic principles of UMGD will be examined along with an overview of pre-clinical studies to date in CVD, focusing on cardiac and vascular applications and key findings. In addition, the potential path to the clinical translation of UMGD is discussed. EXPERT OPINION: Ultrasound-mediated gene delivery holds promise as a non-invasive technique for gene delivery in CVD, with the ability to deliver multiple genes with repeated deliveries over time. If the substantial hurdles to clinical translation can be overcome, UMGD may prove to be a key aspect in the success of cardiovascular gene therapy in the future.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".