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Record W4413010698 · doi:10.1109/tuffc.2025.3596530

Development of an Endoscopic Ultrasound Device for Delivering Microbubble-Mediated Cavitation in the Pancreas: Characterization and Preclinical In-Vivo Results

2025· article· en· W4413010698 on OpenAlexaff
Adrien Rohfritsch, Robert Andrew Drainville, Birane Beye, Gilles Renault, Jessica M. Gannon, Jeffrey Woodacre, Yao Chen, Laura Barrot, Stéphan Lagonnet, Maxime Lafond, Frédéric Prat, Cyril Lafon

Bibliographic record

VenueIEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · 2025
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsEricsson (Canada)
FundersEurostarsInstitut National de la Santé et de la Recherche Médicale
KeywordsIn vivoCavitationUltrasoundUltrasonic imagingBiomedical engineeringMaterials scienceEndoscopic ultrasoundMicrobubblesMedical physicsRadiologyMedicineAcousticsPhysicsBiology

Abstract

fetched live from OpenAlex

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis and limited treatment options. Focused ultrasound (FUS) has shown potential for improving PDAC treatment outcomes by enhancing drug delivery through acoustic cavitation. In this article, we present the development of a prototype endoscopic ultrasound (EUS) device capable of producing microbubble-mediated cavitation with ultrasound imaging for treatment guidance. The performance of the therapy array, composed of 64 piezoelectric elements, was characterized up to voltages of 60 V peak, achieving negative pressures of 6.55 MPa in water for a focal distance of 20 mm. High image quality as well as the feasibility of generating cavitation activity in the pancreatic parenchyma were demonstrated in vivo in a porcine model. Future work will focus on demonstrating its potential as a potentiator of chemotherapeutic treatment for PDAC, paving the way for a new minimally invasive approach to PDAC treatment.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.248
Teacher spread0.235 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Ultrasonics Ferroelectrics and Frequency ControlSame topicUltrasound and Hyperthermia ApplicationsFrench-language works237,207