Possible physical mechanisms of the high echogenicity of lipid coated nanobubbles
Bibliographic record
Abstract
Lipid coated nanobubbles (NBs) have attracted a great level of interest as ultrasound (US) contrast agents due to their ability to extravagate through leaky tumor vasculature. Their linear resonance frequency is in the range of ∼50 M Hz–200 MHz, leading to confusion over their observed strong contrast in diagnostic US frequencies. By solving the Marmottant model, the dynamics of uncoated and lipid coated NBs and microbubbles (MBs) are studied over the frequency and pressure ranges (6–12 MHz, 0.1–1.2 MPa) generally used in diagnostic US. A novel bifurcation analysis in tandem with the analysis of the frequency component of the scattered pressure are conducted. Results show that despite the increased linear resonance frequency and viscous damping due to the lipid shell, buckling and rupture of the shell enhances the generation of the 2nd and 3rd harmonic resonances at pressures as low as 0.2 MPa, not observed with uncoated NBs. The generation of the harmonic resonances are concomitant with an abrupt increase in the 2nd and 3rd harmonic frequency component of the scattered pressure with their pressure threshold (PT) increasing with decreasing NBs size. For the same gas volume, and above the PT, the maximum non-destructive 2nd and 3rd harmonic powers of NBs can become higher than the 2–4 μm MBs. Similar to the lower subharmonic pressure threshold of MBs, the dynamic variation of the NBs effective surface tension due to buckling and rupture may be the potential reason behind the observed harmonic echogenicity.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".