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Record W2160375887 · doi:10.7150/thno/v01p0354

Multimodal Bacteriochlorophyll Theranostic Agent

2011· article· en· W2160375887 on OpenAlexafffund
Tracy Liu, Juan Chen, Laura Burgess, Weiguo Cao, Jiyun Shi, Brian C. Wilson, Gang Zheng

Bibliographic record

VenueTheranostics · 2011
Typearticle
Languageen
FieldMedicine
TopicPhotodynamic Therapy Research Studies
Canadian institutionsUniversity Health NetworkOntario Institute for Cancer ResearchUniversity of Toronto
FundersOntario Institute for Cancer ResearchNatural Sciences and Engineering Research Council of CanadaU.S. Department of Defense
KeywordsPhotodynamic therapyFolate receptorSinglet oxygenFluorescence-lifetime imaging microscopyMolecular imagingPhotothermal therapyLinkerNanotechnologyChemistryIn vivoFluorescenceBiophysicsCancer researchCancerCancer cellMaterials scienceComputer scienceMedicineBiology

Abstract

fetched live from OpenAlex

The complimentary ability of different noninvasive imaging technologies with therapeutic modalities can be used in tandem providing high-resolution and highly sensitive imaging of events at the molecular and cellular level providing a means for image-guided therapy. There is increasing interest in using porphyrin-based photosensitizers as theranostics to take advantages of their near-infrared fluorescent properties for imaging and their strong singlet oxygen generation abilities for photodynamic therapy. Here we report a targeted multimodal bacteriochlorophyll theranostic probe. This probe consists of a bacteriochlorophyll derivative, a pharmacokinetics modification peptide linker and folate for targeted delivery to folate receptor expressing cancer cells. We demonstrate its multimodal theranostic capability, its folate receptor targeting ability and its utility for both NIR fluorescence imaging and photodynamic therapy purposes both in vitro and in vivo.

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 score0.002
Threshold uncertainty score0.005

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.293
Teacher spread0.251 · 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

Citations48
Published2011
Admission routes2
Has abstractyes

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