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Record W2741021117 · doi:10.1039/c7nr02558k

Viral nanoparticles decorated with novel EGFL7 ligands enable intravital imaging of tumor neovasculature

2017· article· en· W2741021117 on OpenAlexafffund
Choi‐Fong Cho, Lihai Yu, Tienabe K. Nsiama, Alisha Kadam, Arun Raturi, Sourabh Shukla, Giulio A. Amadei, Nicole F. Steinmetz, Leonard G. Luyt, John D. Lewis

Bibliographic record

VenueNanoscale · 2017
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsWestern UniversityCancer Care OntarioAlberta Hospital EdmontonUniversity of Alberta
FundersDivision of Civil, Mechanical and Manufacturing InnovationUniversity of AlbertaNational Institute of Biomedical Imaging and BioengineeringProstate Cancer CanadaNational Institutes of HealthCanadian Institutes of Health ResearchNational Science FoundationBrigham and Women's HospitalCase Western Reserve UniversityOntario Institute for Cancer ResearchAlberta Cancer FoundationLawson Health Research Institute
KeywordsIntravital microscopyNanoparticleNanotechnologyChemistryBiophysicsMaterials scienceMedicineBiologyMicrocirculationRadiology

Abstract

fetched live from OpenAlex

Angiogenesis is a dynamic process fundamental to the development of solid tumors. Epidermal growth factor-like domain 7 (EGFL7) is a protein whose expression is restricted to endothelial cells undergoing active remodeling that has emerged as a key mediator of this process. EGFL7 expression is associated with poor outcome in several cancers, making it a promising target for imaging or therapeutic strategies. Here, EGFL7 is explored as a molecular target for active neovascularization. Using a combinatorial peptide screening approach, we describe the discovery and characterization of a novel high affinity EGFL7-binding peptide, E7p72, that specifically targets human endothelial cells. Viral nanoparticles decorated with E7p72 peptides specifically target tumor-associated neovasculature with high specificity as assessed by intravital imaging. This work highlights the value of EGFL7 as a target for angiogenic vessels and opens the door for novel targeted therapeutic approaches.

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 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 score0.004
Threshold uncertainty score0.771

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 communication0.0000.001
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 head0.006
GPT teacher head0.205
Teacher spread0.198 · 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.

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

Citations27
Published2017
Admission routes2
Has abstractyes

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