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Record W2065252975 · doi:10.1088/0031-9155/46/2/307

A diffusion theory model of spatially resolved fluorescence from depth-dependent fluorophore concentrations

2001· article· en· W2065252975 on OpenAlexaff
Derek E Hyde, Thomas J. Farrell, Michael S. Patterson, Brian C. Wilson

Bibliographic record

VenuePhysics in Medicine and Biology · 2001
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsUniversity of TorontoOntario Institute for Cancer ResearchCancer Care OntarioMcMaster UniversityMcMaster University Medical CentreHamilton Regional Laboratory Medicine ProgramJuravinski Cancer Centre
FundersNational Cancer Institute
KeywordsFluorophorePhotobleachingFluorescenceMonte Carlo methodPhoton diffusionMaterials scienceExcitationDiffusionOpticsPhotonTwo-photon excitation microscopyPhoton transport in biological tissuePhotodynamic therapyBiological systemBiomedical engineeringMolecular physicsChemistryPhysicsLight sourceDirect simulation Monte Carlo

Abstract

fetched live from OpenAlex

A photon diffusion model has been developed to calculate the steady-state spatially resolved fluorescence from pencil beam excitation in layered tissue. The model allows the calculation of both the excitation reflectance and the fluorescence escape for an arbitrary continuous depth distribution of tissue optical properties and fluorophore concentration. The validity of this model was verified by comparison with Monte Carlo simulations and experimental measurements using phantoms with tissue-like optical properties. The potential usefulness of the spatially resolved fluorescence was explored using the model and simulations of realistic drug distributions. It was shown that using this technique it may be possible to quantify the diffusion of a topically administered drug into the skin, or the photobleaching of a sensitizer during photodynamic therapy.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.124
GPT teacher head0.387
Teacher spread0.263 · 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 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

Citations76
Published2001
Admission routes1
Has abstractyes

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