Microscale interactions between ultrasound stimulated microbubbles and the fibrin networks of clots
Bibliographic record
Abstract
While the concept of microbubble mediated sonothrombolysis is now well established, a detailed mechanistic understanding of this process remains both elusive as well as necessary in order to facilitate the development of improved exposure methods. As lytic effects may ultimately arise from microscale bubble-clot interactions, we have employed high-speed microscopy and two-photon microscopy to examine these interactions first in (transparent, fluorescently tagged) fibrin clots, and then in blood clots. Bubble “population” studies in fibrin clots show the prominent role of primary and secondary radiation forces: bubbles are first directed toward the clot boundary, where their concentrations increase and interactions such as clustering and coalescence occur frequently. A subset of bubbles penetrate into the clots, disrupting the fibrin network structure along their paths. Once initiated, the resulting tunnels act in subsequent exposures as conduits for bubbles to enter and access deeper points within the clot. Using optical tweezers, individual bubble experiments reveal that bubble entry into the clots, along with the accompanying network damage and fluid uptake, are a function of the network pore size, bubble size, and the exposure scheme. With blood clots, the erosion surface evolves in a complex manner, involving the ejection of erythrocytes and the development and progression of a cell depleted fibrin network zone. The characteristics of the erosion front are highly dependent on exposure conditions.
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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 | 0.000 |
| 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".