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Record W2317875723 · doi:10.1364/biomed.2014.bt3a.54

Porphyrin-based Nanostructure-dependent Phototherapy: a Closed Loop Between Photodynamic and Photothermal Therapy

2014· article· en· W2317875723 on OpenAlexaff
Cheng Jin, Juan Chen, Gang Zheng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsUniversity of TorontoOntario Institute for Cancer Research
Fundersnot available
KeywordsPhotodynamic therapyPorphyrinSinglet oxygenPhotothermal therapyNanostructurePhotosensitizerMaterials sciencePhotochemistryQuenching (fluorescence)NanotechnologyChemistryFluorescenceOxygenOpticsPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Porphyrin is the most common photosensitizer for photodynamic therapy (PDT) where its interaction with light and oxygen generates cytotoxic singlet oxygen species. We recently discovered a unique porphyrin nanostructure, called porphysome, which converts the singlet oxygen generating PDT mechanism of porphyrin molecules to a completely thermal mechanism for efficient photothermal therapy (PTT). This nanostructure-driven PDT to PTT conversion is based on the extremely high porphyrin packing density (>80,000/particle) in porphysome that induces super light absorption and super-quenching of photodynamic activity. Here we explored a reverse process by using receptor-mediated endocytosis to facilitate the disruption of the nanostructure inside cells, thus rapidly switching the PTT mechanism of porphysome to the PDT effect of porphyrin molecules. Therefore, we are closing the loop between PDT and PTT by tailoring the nanostructure-dependent activation mechanism.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.195
Teacher spread0.188 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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