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Record W2084186494 · doi:10.1021/nn2007496

Effect of Gold Nanoparticle Aggregation on Cell Uptake and Toxicity

2011· article· en· W2084186494 on OpenAlex

Why this work is in the frame

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

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Nano · 2011
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticles: synthesis and applications
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsHeLaIntracellularNanoparticleBiophysicsChemistryCellEndocytosisToxicityA549 cellColloidal goldDispersityNanotechnologyNanotoxicologyCell biologyMaterials scienceBiochemistryBiology

Abstract

fetched live from OpenAlex

Aggregation appears to be a ubiquitous phenomenon among all nanoparticles and its influence in mediating cellular uptake and interactions remain unclear. Here we developed a simple technique to produce transferrin-coated gold nanoparticle aggregates of different sizes and characterized their uptake and toxicity in three different cell lines. While the aggregation did not elicit a unique toxic response, the uptake patterns were different between single and aggregated nanoparticles. There was a 25% decrease in uptake of aggregated nanoparticles with HeLa and A549 cells in comparison to single and monodisperse nanoparticles. However, there was a 2-fold increase in MDA-MB 435 cell uptake for the largest synthesized aggregates. These contrasting results suggest that cell type and the mechanism of interactions may play a significant role. This study highlights the need to investigate the behavior of aggregates with cells on a case-by-case basis and the importance of aggregation in mediating targeting and intracellular trafficking.

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.

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 score0.005
Threshold uncertainty score0.264

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)0.0000.000

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 head0.017
GPT teacher head0.235
Teacher spread0.217 · 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