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Record W2101612857 · doi:10.3109/17435390.2010.536268

Effect of sonication and serum proteins on copper release from copper nanoparticles and the toxicity towards lung epithelial cells

2010· article· en· W2101612857 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.

Bibliographic record

VenueNanotoxicology · 2010
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticles: synthesis and applications
Canadian institutionsWorkplace Health, Safety and Compensation Commission
Fundersnot available
KeywordsSonicationNanotoxicologyToxicityNanoparticleViability assayTrypan blueBiophysicsCopperMaterials scienceChemistryChemical engineeringNanotechnologyCellChromatographyBiochemistryBiologyMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Different methodological settings can influence particle characteristics and toxicity in nanotoxicology. The aim of this study was to investigate how serum proteins and sonication of Cu nanoparticle suspensions influence the properties of the nanoparticles and toxicological responses on human lung epithelial cells. This was investigated by using methods for particle characterization (photon correlation spectroscopy and TEM) and Cu release (atomic absorption spectroscopy) in combination with assays for analyzing cell toxicity (MTT-, trypan blue- and Comet assay). The results showed that sonication of Cu nanoparticles caused decreased cell viability and increased Cu release compared to non-sonicated particles. Furthermore, serum in the cell medium resulted in less particle agglomeration and increased Cu release compared with medium without serum, but no clear difference in toxicity was detected. Few cells showed intracellular Cu nanoparticles due to fast release/dissolution processes of Cu. In conclusion; sonication can affect the toxicity of nanoparticles.

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)0.001
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 score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.006
GPT teacher head0.249
Teacher spread0.243 · 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