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Record W2020606203 · doi:10.1111/php.12320

Validation and Application of a Model of Oxygen Consumption and Diffusion During Photodynamic Therapy <i>In Vitro</i>

2014· article· en· W2020606203 on OpenAlexaff
Mark A. Weston, Michael S. Patterson

Bibliographic record

VenuePhotochemistry and Photobiology · 2014
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsMcMaster UniversityJuravinski Cancer Centre
Fundersnot available
KeywordsPhotodynamic therapyPhotosensitizerOxygenMonolayerSpheroidDiffusionChemistryBiophysicsSinglet oxygenCellIn vitroPhotochemistryBiochemistryBiologyOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

The photophysical parameters for the photosensitizer Pd(II) meso-Tetra(4-carboxyphenyl) porphine (PdT790) acquired in a previous study were incorporated into the PDT oxygen diffusion models for cell suspensions and cell monolayers. The time-dependent phosphorescence signals generated by the diffusion models are shown to match signals previously measured by M.A.W. and M.S.P. when reasonable physical and photophysical parameters are used. Simulations were performed to investigate the effects of metabolic and photodynamic oxygen consumption rates on the PDT dose in each of the treatment geometries. It was found that in cell suspensions of <1 million cells per mL, PDT should not be inhibited by hypoxia if the photodynamic consumption rate is <1 mm s(-1). For cell monolayers the optimal photodynamic oxygen consumption rate was found to depend on the metabolic rate of oxygen consumption. If cells remained well oxygenated in the absence of PDT, then maximum PDT dose was delivered with the lowest practical photodynamic oxygen consumption rate. Simulations of PDT treatments for multicell tumor spheroids showed that large anoxic cores develop within the spheroids and, as a consequence, less PDT dose is delivered in comparison with similar treatments in cell suspensions and cell monolayers.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.404

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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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