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Investigating the Impact of Nanoparticle Size on Active and Passive Tumor Targeting Efficiency

2014· article· en· 626 citations· W2334426515 on OpenAlex· 10.1021/nn500299p

Why is this work in the frame?

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

Canadian affiliationAn author listed a Canadian institution. This is the only route the usual frame has.
Canadian funderA Canadian agency funded it. The work may carry no Canadian affiliation at all.

Full frame distilled prediction

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.

Candidate categories
none
Consensus categories
none
Domain
Candidate signal: noneConsensus signal: none
Study design
Candidate signal: Bench or experimentalConsensus signal: Bench or experimental
Genre
Candidate signal: EmpiricalConsensus signal: Empirical
Teacher disagreement score
0.004
Threshold uncertainty score
0.422
Validation status
machine_predicted_unvalidated · codex-gemma-dda1882f352a

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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)

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

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.

Opus teacher head0.009
GPT teacher head0.245
Teacher spread
0.236 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Abstract

Understanding the principles governing the design of nanoparticles for tumor targeting is essential for the effective diagnosis and treatment of solid tumors. There is currently a poor understanding of how to rationally engineer nanoparticles for tumor targeting. Here, we engineered different-sized spherical gold nanoparticles to discern the effect of particle diameter on passive (poly(ethylene glycol)-coated) and active (transferrin-coated) targeting of MDA-MB-435 orthotopic tumor xenografts. Tumor accumulation of actively targeted nanoparticles was found to be 5 times faster and approximately 2-fold higher relative to their passive counterparts within the 60 nm diameter range. For 15, 30, and 100 nm, we observed no significant differences. We hypothesize that such enhancements are the result of an increased capacity to penetrate into tumors and preferentially associate with cancer cells. We also use computational modeling to explore the mechanistic parameters that can impact tumor accumulation efficacy. We demonstrate that tumor accumulation can be mediated by high nanoparticle avidity and are weakly dependent on their plasma clearance rate. Such findings suggest that empirical models can be used to rapidly screen novel nanomaterials for relative differences in tumor targeting without the need for animal work. Although our findings are specific to MDA-MB-435 tumor xenografts, our experimental and computational findings help to enrich knowledge of design considerations that will aid in the optimal engineering of spherical gold nanoparticles for cancer applications in the future.

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.

The record

Venue
ACS Nano
Topic
Nanoparticle-Based Drug Delivery
Field
Materials Science
Canadian institutions
University Health NetworkOntario Institute for Cancer ResearchUniversity of Toronto
Funders
Canadian Institutes of Health Research
Keywords
NanoparticleNanotechnologyAvidityMaterials scienceEthylene glycolIn vivoColloidal goldCancer researchBiophysicsChemistryMedicineImmunologyBiologyAntibody
Has abstract in OpenAlex
yes