Contrast enhanced ultrasound imaging by nature-inspired ultrastable echogenic nanobubbles
Bibliographic record
Abstract
Advancement of ultrasound molecular imaging applications requires not only a reduction in size of the ultrasound contrast agents (UCAs) but also a significant improvement in the in vivo stability of the shell-stabilized gas bubble. The transition from first generation to second generation UCAs was marked by an advancement in stability as air was replaced by a hydrophobic gas, such as perfluoropropane and sulfur hexafluoride. Further improvement can be realized by focusing on how well the UCAs shell can retain the encapsulated gas under extreme mechanical deformations. Here we report the next generation of UCAs for which we engineered the shell structure to impart much better stability under repeated prolonged oscillation due to ultrasound, and large changes in shear and turbulence as it circulates within the body. By adapting an architecture with two layers of contrasting elastic properties similar to bacterial cell envelopes, our ultrastable nanobubbles (NBs) withstand continuous in vitro exposure to ultrasound with minimal signal decay and have a significant delay on the onset of in vivo signal decay in kidney, liver, and tumor. Development of ultrastable NBs can potentially expand the role of ultrasound in molecular imaging, theranostics, and drug delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".