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Record W2164456212 · doi:10.1117/1.jbo.20.10.106012

Monitoring photodynamic therapy with photoacoustic microscopy

2015· article· en· W2164456212 on OpenAlex
Peng Shao, David Chapman, Ronald B. Moore, Roger J. Zemp

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Biomedical Optics · 2015
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesAlberta Innovates - Health SolutionsProstate Cancer Canada
KeywordsPhotodynamic therapyPhotosensitizerVerteporfinPhotoacoustic imaging in biomedicineBiomedical engineeringMicroscopyFluorescence microscopeMaterials scienceChorioallantoic membraneMedicinePathologyFluorescenceCancer researchOpticsChemistrySurgery

Abstract

fetched live from OpenAlex

Abstract. We present our work on examining the feasibility of monitoring photodynamic therapy (PDT)-induced vasculature change with acoustic-resolution photoacoustic microscopy (PAM). Verteporfin, an FDA-approved photosensitizer for clinical PDT, was utilized. With a 60-μm-resolution PAM system, we demonstrated the capability of PAM to monitor PDT-induced vasculature variations in a chick chorioallantoic membrane model with topical application and in a rat ear with intravenous injection of the photosensitizer. We also showed oxygen saturation change in target blood vessels due to PDT. Success of the present approach may potentially lead to the application of PAM imaging in evaluating PDT efficacy, guiding treatment, and predicting responders from nonresponders.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.511
Threshold uncertainty score0.502

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 head0.013
GPT teacher head0.248
Teacher spread0.235 · 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