<i>In vitro</i>characterization of perfluorocarbon droplets for focused ultrasound therapy
Bibliographic record
Abstract
Focused ultrasound therapy can be enhanced with microbubbles by thermal and cavitation effects. However, localization of treatment is difficult as bioeffects can occur outside of the target region. Spatial control of bubbles can be achieved by ultrasound-induced conversion of liquid perfluorocarbon droplets to gas bubbles. This study was undertaken to determine the acoustic parameters for bubble production by droplet conversion and how it depends on the acoustic conditions and droplet physical parameters. Lipid-encapsulated droplets containing dodecafluoropentane were manufactured with sizes ranging from 1.9 to 7.2 microm in diameter and diluted to a concentration of 8 x 10(6) droplets mL(-1). The droplets were sonicated in vitro with a focused ultrasound transducer and varying frequency and exposure under flow conditions through an acoustically transparent vessel. The sonications were 10 ms in duration at frequencies of 0.578, 1.736 and 2.855 MHz. The pressure threshold for droplet conversion was measured with an active transducer operating in pulse-echo mode and simultaneous measurements of broadband acoustic emissions were performed with passive acoustic detection. The results show that droplets cannot be converted at low frequency without broadband emissions occurring. However, the pressure threshold for droplet conversion decreased with increasing frequency, exposure and droplet size. The pressure threshold for broadband emissions was independent of the droplet size and was 2.9, 4.4 and 5.3 MPa for 0.578, 1736 and 2.855 MHz, respectively. In summary, we have demonstrated that droplet conversion is feasible for clinically relevant sized droplets and acoustic exposures.
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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".