The Effect of Nanobubble Ultrasound Contrast Agent Shell Stiffness and Temperature on Stability and Interactions with Red Blood Cells
Bibliographic record
Abstract
Lipid-shell C3F8 nanobubble (NB) ultrasound contrast agents have demonstrated an extended in vivo lifespan compared to traditional microbubble contrast agents. One potential explanation behind this extended lifespan is non-covalent interactions between NBs and red blood cells (RBCs). This has been observed in vitro where interactions increased contrast intensity and signal stability over time. However, the mechanism of this interaction and the factors influencing it have not been fully elucidated. In this study, we investigate the role of NB shell stiffness and solution temperature on signal stability in human whole blood and PBS in vitro. We used three NB shell stiffnesses and a fourth formulation with the addition of a targeting ligand and assessed extent of RBC interaction using autocorrelation analysis. Results demonstrate a clear dependence of RBC interaction on shell stiffness. The stiffest NB formulation had a decorrelation time ~2x faster than all other formulations and showed no effect on signal change over time. Less stiff NBs resulted in an increase in intensity over time and increased decorrelation time, consistent with prior studies. No such effects were observed in PBS. Furthermore, the presence of a targeting ligand did not appear to play a role in contrast enhancement over time or decorrelation time. Decorrelation time in whole blood decreased with increasing temperature. This work examined the effect of shell stiffness and solution temperature on NB signal stability and interactions with RBCs in human whole blood in vitro. Understanding why NBs interact with RBCs could improve NB formulation optimization for extended circulation time in vivo.
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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".