Theoretical and Experimental Gas Volume Quantification of Micro- and Nanobubble Ultrasound Contrast Agents
Bibliographic record
Abstract
The amount of gas in ultrasound contrast agents is related to their acoustic activity. Because of this relationship, gas volume has been used as a key variable in normalizing the in vitro and in vivo acoustic behavior of lipid shell-stabilized bubbles with different sizes and shell components. Despite its importance, bubble gas volume has typically only been theoretically calculated based on bubble size and concentration that is typically measured using the Coulter counter for microbubbles and nanoparticle tracking analysis (NTA) for nanoscale bubbles. However, while these methods have been validated for the analysis of liquid or solid particles, their application in bubble analysis has not been rigorously studied. We have previously shown that resonant mass measurement (RMM) may be a better-suited technique for sub-micron bubble analysis, as it can measure both buoyant and non-buoyant particle size and concentration. Here, we provide validation of RMM bubble analysis by using headspace gas chromatography/mass spectrometry (GC/MS) to experimentally measure the gas volume of the bubble samples. This measurement was then used as ground truth to test the accuracy of theoretical gas volume predictions based on RMM, NTA (for nanobubbles), and Coulter counter (for microbubbles) measurements. The results show that the headspace GC/MS gas volume measurements agreed well with the theoretical predictions for the RMM of nanobubbles but not NTA. For nanobubbles, the theoretical gas volume using RMM was 10% lower than the experimental GC/MS measurements; meanwhile, using NTA resulted in an 82% lower predicted gas volume. For microbubbles, the experimental gas volume from the GC/MS measurements was 27% lower compared to RMM and 72% less compared to the Coulter counter results. This study demonstrates that the gas volume of nanobubbles and microbubbles can be reliably measured using headspace GC/MS to validate bubble size measurement techniques. We also conclude that the accuracy of theoretical predictions is highly dependent on proper size and concentration measurements.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".