Regulating microbubble clusters for improving temporal uniformity of stable cavitation intensity under rapid short-pulse ultrasound
Bibliographic record
Abstract
Stable cavitation induced by rapid short-pulse (RaSP) ultrasound produces more uniform bioeffects in the treatment region than traditional long-pulse sequences. However, temporal non-uniformity of stable cavitation intensity (SCI)-either within a single RaSP or across multiple RaSPs-compromises the efficiency and biosafety of cavitation-based therapies. This study investigates the causes of temporal non-uniformity in SCI and proposes strategies to enhance uniformity. Monodisperse microbubbles, that were generated using a flow-focusing microfluidic device, were exposed to a single RaSP (frequency: 1 MHz; pulse repetition frequency: 1 kHz; peak negative pressure (PNP): 150-250 kPa; pulse length (PL): 20-150 μs; total number of pulses: 100) in a polydimethylsiloxane-gel flowing phantom. Synchronized high-speed microscopic imaging (4000 fps) and cavitation detection systems were used to simultaneously record the bubble population dynamics and SCI evolution. The SCI gradually decayed to a stable level during RaSP ultrasound excitation, with bubble aggregation and clustering progressing exponentially under the earlier pulses, eventually forming stable large clusters. Both the rates of bubble aggregation and SCI decay correlated positively with PNP and PL. Statistical analysis confirmed that cluster formation was the primary cause of SCI decay. Optimizing the PNP and PL only marginally improved the temporal stability of the SCI because cluster formation was not completely suppressed. To address this, an ultrafast feedback controller was developed to regulate the PNP of RaSP in real-time, achieving significantly improved temporal uniformity of SCI. These findings provide fundamental insights into bubble dynamics during RaSP ultrasound and a practical approach for optimizing cavitation-mediated therapies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".