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Record W2753537102 · doi:10.1117/12.2283904

Two-photon photodynamic therapy: photobleaching rates

2017· article· en· W2753537102 on OpenAlexaff
Rebecca L. Goyan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicPhotodynamic Therapy Research Studies
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsPhotobleachingPhotosensitizerPhotodynamic therapyTwo-photon excitation microscopySubcellular localizationPolarity (international relations)ExcitationFluorescence recovery after photobleachingBiophysicsChemistryPhotonMaterials scienceCellFluorescencePhotochemistryOpticsPhysicsBiologyBiochemistry

Abstract

fetched live from OpenAlex

A promising development in photodynamic therapy (PDT) is the use of two-photon excitation (TPE). The confinement of the excitation volume leads to the possibility of subcellular PDT. Thus, in order to design a treatment protocol, one must be aware of where a photosensitizer localizes in a cell and the individual differences, both between cellular localization sites and individual cells. One way to determine the subcellular location is to observe photobleaching dynamics in cells and compare the rate constants to those observed in solvents which have been chosen to model different characteristics of environments, such as polarity. We have observed the photobleaching behaviour of Verteporfm (VP) in a variety of solvents and single cells and have been able to correlate subcellular position with environmental polarity.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.034
GPT teacher head0.377
Teacher spread0.343 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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