A comprehensive numerical investigation of the potential influence of the bubble size and ultrasound focal pressure in drug delivery enhancement
Bibliographic record
Abstract
Stable Bubble oscillations contribute to a range of bio-effects such as enhanced drug delivery and blood-brain barrier opening. The treatment outcome depends on the bubble oscillation characteristics and the number of bubbles present at the target. Higher concentrations result in more bubbles per unit length of the vessels and, therefore, more uniform effects. At higher bubble concentrations, however, the pre-focal attenuation increases. Moreover, above a concentration threshold, bubble-bubble interactions suppress bubble oscillations. To enhance the treatment outcome, thus, not only the bubble concentration and activity should be optimized, but also the problem of pre-focal attenuation should be tackled. Numerical results show that, using the pressure gradient of focused ultrasound transducers and by taking advantage of the pressure dependent attenuation of size isolated bubbles, pre-focal attenuation can be minimized. The optimal bubble size for maximum propagation depends on the focal pressure. When volume is matched, and at lower pressures, bigger bubbles exhibit stronger radial oscillations, scattered pressure, and microstreaming (RSM). Above a pressure threshold that depend on the bubble size, smaller bubbles exhibit stronger RSM compared to their bigger counterparts. The treatment outcome may be enhanced using an optimal set of size and pressures. These results are in qualitative agreement with experimental case studies using size isolated bubbles.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".