MétaCan
Menu
Back to cohort

Effects of ultrasound pulse parameters on cavitation properties of flowing microbubbles under physiologically relevant conditions

2018· article· en· W2905662609 on OpenAlexaff
Mouwen Cheng, Fan Li, Tao Han, Alfred C. H. Yu, Peng Qin

Bibliographic record

VenueUltrasonics Sonochemistry · 2018
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsUniversity of Waterloo
FundersShanghai Jiao Tong UniversityNational Natural Science Foundation of China
KeywordsMicrobubblesCavitationTransducerMaterials sciencePulse repetition frequencyUltrasoundPulse (music)Intensity (physics)Therapeutic ultrasoundBiomedical engineeringImaging phantomAcousticsPulse durationOpticsPhysicsLaser

Abstract

fetched live from OpenAlex

Acoustic cavitation from ultrasound-driven microbubbles can induce diverse bioeffects that are useful in clinical therapy. However, lack of control over the cavitation activity of flowing microbubbles results in unwanted treatment regions in the targeted tissue, which influences the therapeutic efficacy and bio-safety. The aim of this study is to understand the relationship between the ultrasound pulse parameters and cavitation properties of flowing microbubbles, including the type (and transition between types), threshold, intensity and temporal distribution of cavitation. An in vitro physiological-flow phantom was fabricated, in which the microbubbles had a constant velocity, and were sonicated to a 1-MHz focused transducer at a wide range of peak negative pressures (PNPs) (0.10-1.28 MPa), pulse repetition frequencies (PRFs) (1-200 Hz) and pulse lengths (PLs) (10-400 μs). The signals from the flowing bubbles were passively detected by another 7.5-MHz plane transducer. From detailed time- and frequency-domain analysis, we found 1). The occurrence of stable cavitation (SC) and inertial cavitation (IC) depended on PNP and PL when the PRF was below a critical value (PRF threshold) that related to the fluid velocity and PNP full width at half maximum diameter of the transducer. 2) Below the PRF threshold, the PL had no influence on the temporal distribution of SC intensity; however, above the PRF threshold, the SC properties depended on the PL because of acoustically-driven diffusion. Specifically, at shorter PLs, the SC intensity had a uniform temporal distribution and was independent of the PRF; at longer PLs, the SC intensity correlated negatively with the PRF. 3) Below the PRF threshold, the IC properties were independent of the PRF. Increasing the PRF above the PRF threshold caused the IC intensity to decrease with a non-uniform temporal distribution. These results indicate that the fluid velocity and a pulsed acoustic field influence the number and properties of the replenished bubbles into the targeted region, resulting in the change of cavitation properties. In future therapeutic applications, the physiological fluid conditions must be taken into consideration to design reasonable pulse parameters and achieve desirable cavitation properties.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.210
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), 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

Citations55
Published2018
Admission routes1
Has abstractno

Explore more

Same venueUltrasonics SonochemistrySame topicUltrasound and Hyperthermia ApplicationsFrench-language works237,207