Comparing nanodroplets and microbubbles for enhancing ultrasound-mediated gene transfection
Bibliographic record
Abstract
Ultrasound is capable of transfecting cells with DNA through the process of sonoporation. Cell transfection by ultrasound alone is a relatively inefficient process but sonicating cells in the presence of microbubbles can significantly increase transfection efficiency. Unfortunately, microbubbles are relatively large (~3 μm diameter) and cannot leave the vasculature, preventing their direct interaction with cancer cells in solid tumors. Perfluorobutane microbubbles can be condensed into submicron nanodroplets that may be able to extravasate out of the tumor vasculature, potentially allowing these nanodroplets to accumulate in tumors. The current study compares microbubbles and nanodroplets for their abilities to enhance ultrasound-mediated transfection of cultured HEK293 cells. The nanodroplets in the current study were almost 6-fold smaller than their corresponding microbubbles and could be phase changed back into microbubbles with 400 cycles of 1.7 mechanical index ultrasound. Given enough cycles of ultrasound, low concentrations of nanodroplets (2 % v/v) were 2- to 3-fold more effective than microbubbles for transfecting HEK293 cells. The current study supports the use of nanodroplets to enhance ultrasound-mediated transfection due to their equivalent or greater transfection enhancing abilities as well as their smaller size which may allow nanodroplets to accumulate in tumors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".