Improving the Efficiency of Ultrasound and Microbubble Mediated Gene Delivery by Manipulation of Microbubble Lipid Composition
Bibliographic record
Abstract
Ultrasound and microbubble-mediated gene delivery is emerging as a powerful nonviral gene delivery approach due to its ability to target various tissues. Since microbubble cavitation plays a crucial role in gene delivery, factors affecting cavitation, such as microbubble composition, size, ultrasound pressure, frequency, and pulse interval, can directly affect the efficiency of gene delivery. The effect of ultrasound parameters on gene delivery efficiency has been systematically investigated in numerous studies. However, relatively few studies have investigated the influence of different microbubble compositions on gene delivery. In this paper, we report that microbubbles made with the same lipids but different poly(ethylene glycol) (PEG) derivatives lead to significantly different gene delivery efficiencies in vitro . Moreover, we show that the type of PEG derivative used in microbubble formulations greatly influences the acoustic response of microbubbles (i.e., resonance frequency and frequency-dependent attenuation coefficient), thus explaining the differences in gene delivery efficiencies. Our results highlight that changing a single component in the microbubble formulation, i.e., the type of PEG derivative, can improve gene delivery efficiency by 3-fold. This comparative study of microbubbles made with different PEG derivatives may help researchers in designing microbubble formulations for optimal gene delivery.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".