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Record W4390415024 · doi:10.1016/j.pdpdt.2023.103949

Investigating the effects of a temperature dependent photodynamic dose: A numerical study

2023· article· en· W4390415024 on OpenAlexaff
Andrew Effat, Nicholas Bernards, Runjie Bill Shi, Gang Zheng, Brian C. Wilson, Kazuhiro Yasufuku, Robert Weersink

Bibliographic record

VenuePhotodiagnosis and Photodynamic Therapy · 2023
Typearticle
Languageen
FieldMedicine
TopicPhotodynamic Therapy Research Studies
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkUniversity of TorontoToronto General HospitalOntario Institute for Cancer ResearchToronto Rehabilitation Institute
Fundersnot available
KeywordsPhotodynamic therapyPhotosensitizerPhotothermal therapyPhotobleachingDosimetryMaterials scienceRadiation therapyNuclear medicineBiomedical engineeringMedicineChemistryNanotechnologyOpticsInternal medicinePhysicsPhotochemistry

Abstract

fetched live from OpenAlex

SIGNIFICANCE: Photodynamic therapy (PDT) and photothermal therapy (PTT) show promise as cancer treatments, but challenges in generating large ablative volumes for deep-seated tumours persist. Using simulations, this study investigates combined PDT and PTT to increase treatment volumes, including the impact of a temperature-dependent PDT dose on the treatment volume radius. APPROACH: A finite-element model, using the open-source SfePy package, was developed to simulate combined interstitial photothermal and photodynamic treatments. Results compared an additive dose model to a temperature-dependent dose model with enhanced PDT dosimetry and examined typical clinical scenarios for possible synergistic effects. RESULTS: Findings revealed that the temperature-dependent dose model could significantly expand the damage radius compared to the additive model, depending on the tissue and drug properties. CONCLUSIONS: Characterizing synergistic effects of PDT and PTT could enhance treatment planning. Future work is ongoing to implement additional variables, such as photosensitizer photobleaching, and spatial and temporally varying oxygenation.

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 categoriesMeta-epidemiology (narrow)
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 score0.741
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
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 head0.017
GPT teacher head0.314
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.

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

Citations2
Published2023
Admission routes1
Has abstractyes

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