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Record W2808181737 · doi:10.1002/anie.201805246

Advanced Photosensitizer Activation Strategies for Smarter Photodynamic Therapy Beacons

2018· review· en· W2808181737 on OpenAlex

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

VenueAngewandte Chemie International Edition · 2018
Typereview
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsPhotosensitizerPhotodynamic therapyBeaconChemistryPhotochemistryMedicineComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Photodynamic therapy (PDT) is a clinical treatment in which a light-absorbing drug called a photosensitizer (PS) is combined with light and molecular oxygen to generate cytotoxic singlet oxygen. PDT provides additional tissue selectivity compared to conventional chemotherapy as singlet oxygen is generated only in areas in which PS accumulates and that are simultaneously illuminated by a light source with sufficient irradiance and dose. Early PDT beacons built on this concept by adding an analyte-responsive element that simultaneously turns on PDT and fluorescence, providing both an additional layer of selectivity and real-time feedback of the PS's activation state. More recent PDT beacons have expanded this idea, with new methods now available for sensing analytes, generating singlet oxygen, and reporting treatment status. In this Minireview, we consider developments in advanced activation strategies implemented in therapeutic and theranostic beacons.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.792
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.026
GPT teacher head0.295
Teacher spread0.269 · 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