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Record W2059066343 · doi:10.1038/bjc.2012.279

Microbubble and ultrasound radioenhancement of bladder cancer

2012· article· en· W2059066343 on OpenAlexafffund
William T. Tran, Sara Iradji, Ervis Sofroni, Anoja Giles, David Eddy, Gregory J. Czarnota

Bibliographic record

VenueBritish Journal of Cancer · 2012
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
FundersTerry Fox Foundation
KeywordsMicrobubblesUltrasoundMedicinePathologyBladder cancerRadiation therapyMechanical indexFibrosisNuclear medicineCancerRadiologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Tumour vasculature is an important component of tumour growth and survival. Recent evidence indicates tumour vasculature also has an important role in tumour radiation response. In this study, we investigated ultrasound and microbubbles to enhance the effects of radiation. METHODS: Human bladder cancer HT-1376 xenografts in severe combined immuno-deficient mice were used. Treatments consisted of no, low and high concentrations of microbubbles and radiation doses of 0, 2 and 8 Gy in short-term and longitudinal studies. Acute response was assessed 24 h after treatment and longitudinal studies monitored tumour response weekly up to 28 days using power Doppler ultrasound imaging for a total of 9 conditions (n=90 animals). RESULTS: Quantitative analysis of ultrasound data revealed reduced blood flow with ultrasound-microbubble treatments alone and further when combined with radiation. Tumours treated with microbubbles and radiation revealed enhanced cell death, vascular normalisation and areas of fibrosis. Longitudinal data demonstrated a reduced normalised vascular index and increased tumour cell death in both low and high microbubble concentrations with radiation. CONCLUSION: Our study demonstrated that ultrasound-mediated microbubble exposure can enhance radiation effects in tumours, and can lead to enhanced tumour cell death.

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 score0.617
Threshold uncertainty score0.745

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)0.0010.000

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 head0.007
GPT teacher head0.235
Teacher spread0.228 · 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

Citations71
Published2012
Admission routes2
Has abstractyes

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