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Record W3182181559 · doi:10.1121/10.0010534

An ultrasonically actuated fine-needle creates cavitation in bovine liver

2022· article· en· W3182181559 on OpenAlexaff
Emanuele Perra, Nick Hayward, Kenneth P. H. Pritzker, Heikki J. Nieminen

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2022
Typearticle
Languageen
FieldMaterials Science
TopicUltrasound and Cavitation Phenomena
Canadian institutionsUniversity of Toronto
FundersAalto-YliopistoAcademy of FinlandHelsingin Yliopisto
KeywordsCavitationMaterials scienceUltrasoundTherapeutic ultrasoundUltrasonic sensorLiver tissueBiomedical engineeringAcousticsDrug deliveryNanotechnologyPhysicsMedicine

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.779
Threshold uncertainty score0.860

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.244
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicUltrasound and Cavitation PhenomenaFrench-language works237,207