Acoustic radiation force induced accumulation and dynamics of microbubbles on compliant surfaces
Bibliographic record
Abstract
Ultrasound stimulated microbubbles have been shown to be capable of breaking up blood clots through micro-scale interactions occurring near the clot surface. However, only a small fraction of bubbles circulating in the bloodstream will be in close proximity to such boundaries, where they must be to elicit therapeutic effects. Here, the accumulation and subsequent behavior of microbubbles displaced from an overlying flow channel to a boundary under radiation forces were examined. Experimental data were acquired using a novel high speed microscopy configuration and simulations were conducted to provide insight into the accumulation process. There was broad agreement between experiments and simulations, both indicating that the size distribution and number of bubbles arriving at the boundary depended on channel flow rate, applied pressure, and bubble concentration. For example, higher flow rates and lower pressures favored the accumulation of larger bubbles relative to the native agent distribution. Moreover, bubble dynamics were dependent on the surface type, exhibiting rapid translation along agarose gel surfaces whereas on fibrin surfaces, they accumulated in localized regions inducing repetitive strain cycles. The results indicate that the process of bringing bubbles from within a vessel to a boundary is complex and should be an important consideration in the development of therapeutic applications such as sonothrombolysis.
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 | 0.001 |
| 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".