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Record W2396555174 · doi:10.1007/978-1-62703-751-8_16

Molecular Targeted Viral Nanoparticles as Tools for Imaging Cancer

2013· article· en· W2396555174 on OpenAlexaff
Choi-Fong Cho, Sourabh Shukla, Emily J. Simpson, Nicole F. Steinmetz, Leonard G. Luyt, John D. Lewis

Bibliographic record

VenueMethods in molecular biology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsWestern UniversityProstate Cancer CanadaUniversity of Alberta
FundersNational Center for Advancing Translational SciencesNational Institute of Biomedical Imaging and Bioengineering
KeywordsPolyethylene glycolPEG ratioCowpea mosaic virusMolecular imagingChemistryFluorescence-lifetime imaging microscopyIn vivoPreclinical imagingBiophysicsNanoparticleNanotechnologyFluorescenceBiochemistryVirusBiologyPlant virusVirologyMaterials scienceBiotechnology

Abstract

fetched live from OpenAlex

Viral nanoparticles (VNPs) are a novel class of bionanomaterials that harness the natural biocompatibility of viruses for the development of therapeutics, vaccines, and imaging tools. The plant virus, cowpea mosaic virus (CPMV), has been successfully engineered to create novel cancer-targeted imaging agents by incorporating fluorescent dyes, polyethylene glycol (PEG) polymers, and targeting moieties. Using straightforward conjugation strategies, VNPs with high selectivity for cancer-specific molecular targets can be synthesized for in vivo imaging of tumors. Here we describe the synthesis and purification of CPMV-based VNPs, the functionalization of these VNPs using click chemistry, and their use for imaging xenograft tumors in animal models. VNPs decorated with fluorescent dyes, PEG, and targeting ligands can be synthesized in one day, and imaging studies can be performed over hours, days, or weeks, depending on the application.

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 categoriesInsufficient 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: none
Teacher disagreement score0.275
Threshold uncertainty score0.999

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)0.0020.000

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 head0.015
GPT teacher head0.374
Teacher spread0.358 · 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

Citations43
Published2013
Admission routes1
Has abstractyes

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