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Record W4306658711 · doi:10.3390/ijtm2040039

Characterization of Engineered Cerium Oxide Nanoparticles and Their Effects on Lung and Macrophage Cells

2022· article· en· W4306658711 on OpenAlexafffund
Michael E. Bushell, Filip Kunc, Xiaomei Du, Andre Zborowski, Linda J. Johnston, David C. Kennedy

Bibliographic record

VenueInternational Journal of Translational Medicine · 2022
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Nanomaterials in Catalysis
Canadian institutionsNational Research Council Canada
FundersNational Research Council CanadaHealth Canada
KeywordsCerium oxideNanoparticleCeriumCytotoxicityMaterials scienceNanotechnologyCharacterization (materials science)OxideChemistryChemical engineeringMetallurgyBiochemistryIn vitro

Abstract

fetched live from OpenAlex

Cerium oxide nanoparticles are promising materials as novel nanoscale therapeutics and are commonly used materials in industrial processes. Most cytotoxicity studies on cerium oxide nanoparticles are made from in-lab prepared materials making comparison between studies challenging, especially when performed on unique cell lines under non-standard conditions. Using commercially available nanoparticles we show that particle stability/agglomeration may be critical in determining the cytotoxicity in some cell lines, while in other cell lines, larger sized primary particles are linked to higher cytotoxicity, contrasting what has been reported in the literature for smaller cerium nanoparticles. To accelerate the development of cerium oxide enabled commercial processes and biomedical innovations, a clearer understanding of the interactions between cerium oxide nanoparticles and cells is needed to better understand their fate in and impact on biological systems.

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 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 score0.010
Threshold uncertainty score0.338

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 studies0.0000.000
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 head0.006
GPT teacher head0.239
Teacher spread0.233 · 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.

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

Citations7
Published2022
Admission routes2
Has abstractyes

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