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Record W3189990959 · doi:10.1021/acsami.1c10618

Uptake of Upconverting Nanoparticles by Breast Cancer Cells: Surface Coating versus the Protein Corona

2021· article· en· W3189990959 on OpenAlexafffund
Evelina Voronovič, Artiom Skripka, Greta Jarockytė, Marija Ger, Dalius Kučiauskas, Algirdas Kaupinis, Mindaugas Valius, Ričardas Rotomskis, Fiorenzo Vetrone, Vitalijus Karabanovas

Bibliographic record

VenueACS Applied Materials & Interfaces · 2021
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsInstitut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologiesCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaLietuvos Mokslo TarybaCanada Foundation for Innovation
KeywordsEndocytosisBiocompatibilityMaterials scienceNanoparticleNanotechnologyDrug deliveryBiophysicsNanomedicineCancer cellSurface modificationProtein adsorptionEndocytic cyclePhospholipidCellCancerChemistryBiochemistryBiologyPolymer

Abstract

fetched live from OpenAlex

-coated RENPs is larger and highly dynamic. Moreover, biocompatibility, accumulation, and cytotoxicity of various RENPs in cancer cells have been evaluated. On the basis of the cellular imaging, supported by the inhibition studies, it was revealed that RENPs are internalized by endocytosis and that specific endocytic routes are PC composition dependent. Overall, these results are essential to fill the gaps in the fundamental understanding of the nano-biointeractions of RENPs, pertinent for their envisioned application in biomedicine.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.016
GPT teacher head0.242
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), 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

Citations44
Published2021
Admission routes2
Has abstractyes

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