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Record W2127711678 · doi:10.7150/thno.10694

Nano-Enabled SERS Reporting Photosensitizers

2015· article· en· W2127711678 on OpenAlexafffund
Arash Farhadi, Áron Roxin, Brian C. Wilson, Gang Zheng

Bibliographic record

VenueTheranostics · 2015
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsPrincess Margaret Cancer Centre
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchPrincess Margaret Cancer Foundation
KeywordsPhotosensitizerPhotodynamic therapyFluorescenceNanotechnologyChemistryMaterials sciencePhotochemistryOptics

Abstract

fetched live from OpenAlex

To impart effective cellular damage via photodynamic therapy (PDT), it is vital to deliver the appropriate light dose and photosensitizer concentration, and to monitor the PDT dose delivered at the site of interest. In vivo monitoring of photosensitizers has in large part relied on their fluorescence emission. Palladium-containing photosensitizers have shown promising clinical results by demonstrating near full conversion of light to PDT activity at the cost of having undetectable fluorescence. We demonstrate that, through the coupling of plasmonic nanoparticles with palladium-photosensitizers, surface-enhanced Raman scattering (SERS) provides both reporting and monitoring capability to otherwise quiescent molecules. Nano-enabled SERS reporting of photosensitizers allows for the decoupling of the therapeutic and imaging mechanisms so that both phenomena can be optimized independently. Most importantly, the design enables the use of the same laser wavelength to stimulate both the PDT and imaging features, opening the potential for real-time dosimetry of photosensitizer concentration and PDT dose delivery by SERS monitoring.

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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.033
GPT teacher head0.239
Teacher spread0.206 · 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 teacher head, not a consensus.

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

Citations40
Published2015
Admission routes2
Has abstractyes

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