MétaCan
Menu
Back to cohort
Record W2163869065 · doi:10.1073/pnas.1200053109

Tumor radiation response enhancement by acoustical stimulation of the vasculature

2012· article· en· W2163869065 on OpenAlexaff
Gregory J. Czarnota, Raffi Karshafian, Peter N. Burns, C. Shun Wong, Azza Al Mahrouki, Justin W. Lee, Amanda Caissie, William T. Tran, Christina Kim, Melissa Furukawa, Emily Wong, Anoja Giles

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2012
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsToronto Metropolitan UniversityHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsIn vivoApoptosisCancer researchUltrasoundStimulationRadiation therapyMedicineRadiobiologyChemistryPathologyBiologyInternal medicineRadiologyBiochemistry

Abstract

fetched live from OpenAlex

We have discovered that ultrasound-mediated microbubble vascular disruption can enhance tumor responses to radiation in vivo. We demonstrate this effect using a human PC3 prostate cancer xenograft model. Results indicate a synergistic effect in vivo with combined single treatments of ultrasound-stimulated microbubble vascular perturbation and radiation inducing an over 10-fold greater cell kill with combined treatments. We further demonstrate with experiments in vivo that induction of ceramide-related endothelial cell apoptosis, leading to vascular disruption, is a causative mechanism. In vivo experiments with ultrasound and bubbles permit radiation doses to be decreased significantly for comparable effect. We envisage this unique combined ultrasound-based vascular perturbation and radiation treatment method being used to enhance the effects of radiation in a tumor, leading to greater tumor eradication.

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)0.000
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.017
GPT teacher head0.265
Teacher spread0.249 · 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

Citations221
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicUltrasound and Hyperthermia ApplicationsFrench-language works237,207