Time-dependent nanobubble stability: Correlating bubble size and concentration with ultrasound performance
Bibliographic record
Abstract
Lipid shell-stabilized nanobubbles (NB, <500 nm) are widely explored as next-generation contrast agents for diagnostic ultrasound (US) imaging and drug delivery. For a successful clinical translation, it is important to understand the factors which contribute to the stability and rate of signal decay from the NB over time. The small size and fragile nature of NB have limited the characterization of their stability to correlations with their loss of signal over time under US. Bubble oscillations in the acoustic field, however, can accelerate their dissolution process. In this study, the passive, non-acoustically driven dissolution of lipid-shelled, C3F8 NB, and the relationship between bubble size/concentration and US signal intensity were assessed. The change in the acoustic activity of NB over time was correlated with the changes in size and concentration of the buoyant (bubbles) and non-buoyant particle population, measured using a novel resonant mass measurement technique. Clinical US was used to measure signal enhancement at different time points in a tissue phantom (f = 12.0 MHz, MI: 0.29, 1 fp/s). Results demonstrate a clear nonlinear relationship between the rate of ultrasound signal decay and concentration. While US signal decayed significantly over time (from 0 to 5 h), bubble concentration did not change significantly. A statistically significant decrease in the NB diameter was observed 1 h after the NBs were prepared and isolated while no change in the size was observed between 1 and 5 h.
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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".