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Record W2135046609 · doi:10.3109/17435390.2012.668570

<i>In vivo</i> toxicity evaluation of gold-dendrimer composite nanodevices with different surface charges

2012· article· en· W2135046609 on OpenAlexaff
Venugopalan Kasturirangan, Bindu M. Nair, Muhammed T. S. Kariapper, Wojciech G. Lesniak, Wei Tan, Remy Bizimungu, Peter M. Kanter, Károly Tóth, Sandra Buitrago, Youcef M. Rustum, Alan D. Hutson, Lajos Balogh, Mohamed K. Khan

Bibliographic record

VenueNanotoxicology · 2012
Typearticle
Languageen
FieldMaterials Science
TopicDendrimers and Hyperbranched Polymers
Canadian institutionsBC Cancer Agency
FundersNational Cancer Institute
KeywordsDendrimerIn vivoNanomaterialsZeta potentialNanotechnologyBiophysicsMaterials sciencePoly(amidoamine)ToxicityChemistryAmidoamineNanoparticlePolymer chemistryBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

Composite nanodevices (CNDs) are multifunctional nanomaterials with potential uses in cancer imaging and therapy. Poly(amidoamine) dendrimer-based composite nanodevices are important members of this group and consist of an organic dendrimer component and an incorporated inorganic component, in this case, gold. This study addresses the short- (14 days) and long-term (78 days) in vivo toxicity of generation-5 (G5; 5 nm) PAMAM dendrimer-based gold-CNDs (Au-CNDs) with varying surface charges (positive, negative and neutral) in C57BL/6J male mice. Detailed toxicological analyses of (1) body weight changes, (2) serum chemistry and (3) histopathological examination of 22 organs showed no evidence of organ injury or organ function compromise. Zeta potential of Au-CNDs showed significant change from their parent dendrimers upon gold incorporation, making the normally lethal positive surface dendrimer biologically safe. Also homeostatic mechanisms in vivo may compensate/repair toxic effects, something not seen with in vitro assays.

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 head0.025
GPT teacher head0.285
Teacher spread0.260 · 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

Citations14
Published2012
Admission routes1
Has abstractyes

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