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Record W2290850235 · doi:10.1002/cnma.201600038

Nanostructure‐Dependent Ratiometric NIR Fluorescence Enabled by Ordered Dye Aggregation

2016· article· en· W2290850235 on OpenAlexafffund
Danielle M. Charron, Juan Chen, Gang Zheng

Bibliographic record

VenueChemNanoMat · 2016
Typearticle
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 ResearchTerry Fox Research InstitutePrincess Margaret Cancer Foundation
KeywordsNanocarriersFluorescenceNanostructureNanotechnologyBiophysicsChemistryFluorescence-lifetime imaging microscopyMaterials scienceNanoparticleBiologyPhysics

Abstract

fetched live from OpenAlex

Abstract Nanocarriers incorporating therapeutic and imaging agents within a single nanostructure are emerging tools for drug development and treatment planning. Additional information can be provided using activatable fluorescence that dynamically reports nanocarrier disruption and drug release. Dual‐wavelength activation encodes a unique fluorescence signal for each nanocarrier state, enabling use of ratiometric imaging to measure the proportion of intact and disrupted nanocarriers within tissues. Here we investigate dual‐wavelength activation using a single dye whose optical properties are intricately linked to the nanocarrier structure. Natural bacteriochlorophyll incorporated within a compact lipoprotein nanocarrier forms ordered dye aggregates with red‐shifted fluorescence emission from 765 nm to 825 nm. We demonstrate that bacteriochlorophyll‐ordered aggregation is a feasible strategy to distinguish intact and disrupted theranostic nanocarriers by ratiometric fluorescence imaging.

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 score0.004

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)0.0010.000

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 head0.005
GPT teacher head0.182
Teacher spread0.177 · 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

Citations13
Published2016
Admission routes2
Has abstractyes

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