Mechanical decellularization of tissue volumes using boiling histotripsy
Bibliographic record
Abstract
High intensity focused ultrasound (HIFU) is rapidly advancing as an alternative therapy for non-invasively treating specific cancers and other pathological tissues through thermal ablation. A new type of HIFU therapy-boiling histotripsy (BH)-aims at mechanical fractionation of into subcellular fragments, with a range of accompanying thermal effects that can be tuned from none to substantial depending on the requirements of the application. The degree of mechanical tissue damage induced by BH has been shown to depend on the tissue type, with collagenous structures being most resistant, and cellular structures being most sensitive. This has been reported for single BH lesions, but has not been replicated in large volumes. Such tissue selectivity effect has potential uses involving tissue decellularization for biofabrication technologies as well as mechanical ablation by BH while sparing critical structures. The goal of this study was to investigate tissue decellularization effect in larger, clinically relevant liquefied volumes of tissue, and to evaluate the accumulated thermal effect in the volumetric lesions under different exposure parameters. All BH exposures were performed with a 256-element 1.2 MHz array of a magnetic resonance imaging-guided HIFU (MR-HIFU) clinical system (Sonalleve V1, Profound Medical Inc, Mississauga, Canada). The volumetric BH lesions were produced in degassed ex vivo bovine liver using 1-10 ms long pulses with in situ shock amplitudes of 75-100 MPa at the focus and pulse repetition frequencies (PRFs) of 1-10 Hz covering a range of effects from pure mechanical homogenization to thermal ablation. Multimodal analysis of the lesions was then performed, including microstructure (histological), ultrastructure (electron microscopy), and molecular (biochemistry) methods. Results show a range of tissue effects in terms of the degree of tissue selectivity and the amount of heat generated in large BH lesions, thereby demonstrating potential for treatments tailored to different clinical applications.
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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".