An ultrasonically actuated fine-needle creates cavitation in bovine liver
Bibliographic record
Abstract
Ultrasonic cavitation is being used in medical applications as a way to influence matter, such as tissue or drug vehicles, on a micro-scale. Oscillating or collapsing cavitation bubbles provide transient mechanical force fields, which can, e.g., fractionate soft tissue or even disintegrate solid objects, such as calculi. Our recent study demonstrates that an ultrasonically actuated medical needle can create cavitation phenomena inside water. However, the presence and behavior of cavitation and related bioeffects in diagnostic and therapeutic applications with ultrasonically actuated needles are not known. Using simulations, we demonstrate numerically and experimentally the cavitation phenomena near ultrasonically actuated needles. We define the cavitation onset within a liver tissue model with different total acoustic power levels. We directly visualize and quantitatively characterize cavitation events generated by the ultrasonic needle in thin fresh bovine liver sections enabled by high-speed imaging. On a qualitative basis, the numerical and experimental results show a close resemblance in threshold and spatial distribution of cavitation. These findings are crucial for developing new methods and technologies employing ultrasonically actuated fine needles, such as ultrasound-enhanced fine-needle biopsy, drug delivery, and histotripsy.
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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) | 0.000 | 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 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".