Miniature histotripsy device to treat human pathologies
Bibliographic record
Abstract
A high-frequency (6 MHz), miniature histotripsy device was developed to treat human pathologies using bubble-based focused ultrasound therapy to mechanically ablate tissue. The goal of this study was to determine the effect of pulses per treatment point on bubble formation and subsequent tissue ablation area using tissue-mimicking phantoms. These phantoms were designed to closely simulate the mechanical and acoustic properties of human tissues. Fibrinogen and thrombin, along with 5% fetal bovine serum, were added to form a fibrin gel incubated at 37 °C for 1 h to ensure fibrin formation. Subsequently, human MC38 cells were co-cultured with the fibrin gel for at least 1 day prior to treatment. Histotripsy pulses above the cavitation threshold were applied ranging from 500 to 5000 pulses. Bubble formation and dynamics were monitored in real-time using ultrasound imaging to detect the extent of ablation and was compared to post hoc optic imaging. The results indicated that the number of pulses applied, and ultrasound pressure influenced bubble dynamics, which in turn affected the ablation area. Increased number of pulses applied was correlated with larger ablation areas, highlighting the importance of optimizing bubble detection under real-time imaging to maximize histotripsy treatment efficacy.
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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) | 0.002 | 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".