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Record W2402490525 · doi:10.1039/c6tb00821f

Magnetic resonance imaging/fluorescence dual modality protocol using designed phosphonate ligands coupled to superparamagnetic iron oxide nanoparticles

2016· article· en· W2402490525 on OpenAlexafffund
Tina Lam, Pramod Avti, Philippe Pouliot, Jean‐Claude Tardif, Éric Rhéaume, Frédéric Lesage, Ashok Kakkar

Bibliographic record

VenueJournal of Materials Chemistry B · 2016
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsMontreal Heart InstituteHEC MontréalPolytechnique MontréalMcGill University
FundersFonds de recherche du Québec – Nature et technologies
KeywordsPhosphonateFluorescenceSuperparamagnetismMaterials scienceNanoparticleNuclear magnetic resonanceNanotechnologyChemistryMagnetizationOpticsOrganic chemistryPhysicsMagnetic field

Abstract

fetched live from OpenAlex

A simple and versatile methodology to tailor the surface of superparamagnetic iron oxide nanoparticles (SPIONs), and render additional fluorescence capability to these contrast agents, is reported. The dual modality imaging protocol was developed by designing multi-functional scaffolds with a combination of orthogonal moieties for aqueous dispersion and stealth, to covalently link them to SPIONs, and carry out post-functionalization of nanoparticles. SPIONs stabilized with ligands incorporating surface-anchoring phosphonate groups, ethylene glycol backbone for aqueous dispersion, and free surface exposed OH moieties were coupled to near-infrared dye Cy5.5A. Our results demonstrate that design of multi-tasking ligands with desired combination and spatial distribution of functions provides an ideal platform to construct highly efficient dual imaging probes with balanced magnetic, optical and cell viability properties.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0010.000
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 head0.012
GPT teacher head0.251
Teacher spread0.239 · 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

Citations9
Published2016
Admission routes2
Has abstractyes

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