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Record W4285009896 · doi:10.1021/acs.biomac.2c00386

Changing Surface Polyethylene Glycol Architecture Affects Elongated Nanoparticle Penetration into Multicellular Tumor Spheroids

2022· article· en· W4285009896 on OpenAlexafffund
Megan G. Roberts, Valerie J. Facca, Rachel Keunen, Qing Yu, Raymond M. Reilly, Mitchell A. Winnik

Bibliographic record

VenueBiomacromolecules · 2022
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolyethylene glycolNanoparticlePenetration (warfare)SpheroidPolymerMethacrylatePEG ratioBiophysicsMaterials sciencePolymer chemistryChemistryChemical engineeringNanotechnologyCopolymerComposite materialOrganic chemistryBiochemistryIn vitro

Abstract

fetched live from OpenAlex

Nanoparticles (NPs) designed for biomedical applications are coated with protein-repellent polymers. Here, we examine the penetration of rodlike NPs with narrow size distributions (Ln = 170 nm, wn = 12 nm) into multicellular tumor spheroids prepared from two human cancer cell lines. Two types of NPs with different core materials [polyferrocenylsilane and cellulose nanocrystals (CNC)] were coated with a dense brush of poly(oligoethyleneglycol methacrylate) (POEGMA), while a second CNC NP sample was coated with a linear polyethylene glycol (PEG) brush. While the core material had little influence, the coating material was strikingly important, with POEGMA-coated NPs penetrating much more deeply into the tumor spheroids than the NPs coated with linear PEG. Localization experiments using 111In-labeled POEGMA-coated CNC NPs showed that most of the radioactivity remained in the interstitial space (ca. 78%) with little cell uptake (ca. 6%). Hence, the deep penetration of these nanorods into tumor spheroids is associated with an interstitial diffusion pathway through the extracellular matrix and not cellular transcytosis.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.007
GPT teacher head0.219
Teacher spread0.213 · 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

Citations14
Published2022
Admission routes2
Has abstractyes

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