Investigating the resonance response of a system of two ultrasound-driven lipid encapsulated microbubbles
Bibliographic record
Abstract
Ultrasound-stimulated microbubbles are clinical imaging agents and being developed for therapeutic applications. Here, we aim to understand the behaviour of two individual phospholipid-encapsulated microbubbles in close proximity to each other, typically the case given clinical doses. We developed a finite element model to study the radial resonance response of each microbubble within a two-microbubble system from 1–8 MHz with bubble diameters ranging from 2 to 4 μm, bubble center-to-center distances h = 8–24 μm, and peak-negative pressures of 30–45 kPa. For two identical microbubbles, our results show the frequency of maximum response (fMR) decreases (7%–10%) and the amplitude of maximum response (AMR) increases (9%–11%) as the microbubbles approach one another. For a two-bubble system of different microbubble sizes, the larger microbubble shows no change in fMR and a slight shift of AMR (2–3%). However, the smaller microbubble exhibits an increase in fMR (7–11%) and a significant decrease of AMR (38–52%). Furthermore, when in very close proximity (h = 8 μm), smaller bubbles exhibit a secondary resonance peak corresponding to the fMR of the larger bubble with amplitudes comparable to its primary resonance peak. Our work suggests that microbubble resonance behaviour is greatly affected by the presence of nearby bubbles, which has implications in imaging and therapy.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".