Effect of non-acoustic parameters on heterogeneous sonoporation mediated by single-pulse ultrasound and microbubbles
Bibliographic record
Abstract
Sonoporation-transient plasma membrane perforation elicited by the interaction of ultrasound waves with microbubbles--has shown great potential for drug delivery and gene therapy. However, the heterogeneity of sonoporation introduces complexities and challenges in the realization of controllable and predictable drug delivery. The aim of this investigation was to understand how non-acoustic parameters (bubble related and bubble-cell interaction parameters) affect sonoporation. Using a customized ultrasound-exposure and fluorescence-imaging platform, we observed sonoporation dynamics at the single-cell level and quantified exogenous molecular uptake levels to characterize the degree of sonoporation. Sonovue microbubbles were introduced to passively regulate microbubble-to-cell distance and number, and bubble size. 1 MHz ultrasound with 10-cycle pulse duration and 0.6 MPa peak negative pressure were applied to trigger the inertial collapse of microbubbles. Our data revealed the impact of non-acoustic parameters on the heterogeneity of sonoporation. (i) The localized collapse of relatively small bubbles (diameter, D<5.5 μm) led to predictable sonoporation, the degree of which depended on the bubble-to-cell distance (d). No sonoporation was observed when d/D>1, whereas reversible sonoporation occurred when d/D<1. (ii) Large bubbles (D>5.5 μm) exhibited translational movement over large distances, resulting in unpredictable sonoporation. Translation towards the cell surface led to variable reversible sonoporation or irreversible sonoporation, and translation away from the cell caused either no or reversible sonoporation. (iii) The number of bubbles correlated positively with the degree of sonoporation when D<5.5 μm and d/D<1. Localized collapse of two to three bubbles mainly resulted in reversible sonoporation, whereas irreversible sonoporation was more likely following the collapse of four or more bubbles. These findings offer useful insight into the relationship between non-acoustic parameters and the degree of sonoporation.
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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) | 0.000 | 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".