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Record W3049606037 · doi:10.1371/journal.pone.0237372

Optimization of microbubble enhancement of hyperthermia for cancer therapy in an in vivo breast tumour model

2020· article· en· W3049606037 on OpenAlexaff
Deepa Sharma, Holliday Cartar, Niki Law, Anoja Giles, Golnaz Farhat, Michael L. Oelze, Gregory J. Czarnota

Bibliographic record

VenuePLoS ONE · 2020
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersNational Institutes of Health
KeywordsMicrobubblesHyperthermiaUltrasoundIn vivoMedicineBreast cancerHyperthermia therapyHyperthermia TreatmentCancerNuclear medicinePathologyRadiologyInternal medicineBiology

Abstract

fetched live from OpenAlex

We have demonstrated that exposing human breast tumour xenografts to ultrasound-stimulated microbubbles enhances tumour cell death and vascular disruption resulting from hyperthermia treatment. The aim of this study was to investigate the effect of varying the hyperthermia and ultrasound-stimulated microbubbles treatment parameters in order to optimize treatment bioeffects. Human breast cancer (MDA-MB-231) tumour xenografts in severe combined immunodeficiency (SCID) mice were exposed to varying microbubble concentrations (0%, 0.1%, 1% or 3% v/v) and ultrasound sonication durations (0, 1, 3 or 5 min) at 570 kPa peak negative pressure and central frequency of 500 kHz. Five hours later, tumours were immersed in a 43°C water bath for varying hyperthermia treatment durations (0, 10, 20, 30, 40, 50 or 60 minutes). Results indicated a significant increase in tumour cell death reaching 64 ± 5% with combined treatment compared to 11 ± 3% and 26 ± 5% for untreated and USMB-only treated tumours, respectively. A similar but opposite trend was observed in the vascular density of the tumours receiving the combined treatment. Optimal treatment parameters were found to consist of 40 minutes of heat with low power ultrasound treatment microbubble parameters of 1 minute of sonification and a 1% microbubble concentration.

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)0.000
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 score0.193
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.042
GPT teacher head0.229
Teacher spread0.187 · 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

Citations22
Published2020
Admission routes1
Has abstractyes

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