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Record W4393216683 · doi:10.1039/d4lc00032c

Brachytherapy on-a-chip: a clinically-relevant approach for radiotherapy testing in 3d biology

2024· article· en· W4393216683 on OpenAlexafffund
Rodin Chermat, Elena Refet-Mollof, Yuji Kamio, Jean‐François Carrier, Philip Wong, Thomas Gervais

Bibliographic record

VenueLab on a Chip · 2024
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversité de MontréalPolytechnique MontréalUniversity Health NetworkCentre Hospitalier de l’Université de MontréalPrincess Margaret Cancer CentreMontreal Heart Institute
FundersMEDTEQ+Fonds de recherche du Québec – Nature et technologiesCanada First Research Excellence FundInstitut TransMedTechNatural Sciences and Engineering Research Council of CanadaInstitut Du Cancer de MontréalMitacs
KeywordsBrachytherapyMedical physicsRadiation therapyPreclinical testingMedicineRadiology

Abstract

fetched live from OpenAlex

testing of brachytherapy (BT), with applications in translational cancer research. Our PDMS-made BT-on-chip system allows highly precise manual insertion of clinical BT seeds, reliable dose calculation using standard clinically-used TG-43 formalism and easy culture of naturally hypoxic spheroids in less than 3 days, thereby increasing the translational potential of the device. As the BT-on-chip platform is designed to be versatile, we showcase three different gold-standard post-irradiation bioassays and recapitulate, for the first time on-chip, key clinical observations such as dose rate effect and hypoxia-induced radioresistance. Our results suggest that BT-on-chip can be used to safely and efficiently integrate BT and radiotherapy to translational research and drug development pipelines, without expensive equipment or complex workflows.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.941
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.054
GPT teacher head0.352
Teacher spread0.298 · 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 designSimulation or modeling
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

Citations8
Published2024
Admission routes2
Has abstractyes

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