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Record W2056461656 · doi:10.1021/am900357x

Study of Transfection Efficiencies of Cationic Glyconanoparticles of Different Sizes in Human Cell Line

2009· article· en· W2056461656 on OpenAlexaff
Marya Ahmed, Zhicheng Deng, Ravin Narain

Bibliographic record

VenueACS Applied Materials & Interfaces · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsLaurentian UniversityUniversity of Alberta
Fundersnot available
KeywordsBiocompatibilityMaterials scienceHeLaCyanineColloidal goldNanoparticleSurface modificationNanotechnologyCationic polymerizationTransfectionGlycopolymerIn vivoFluorescenceBiophysicsIn vitroCell cultureChemical engineeringPolymerChemistryPolymer chemistryBiochemistryBiologyCopolymerOptics

Abstract

fetched live from OpenAlex

The growing attention toward the synthesis and uses of gold nanoparticles for biomedical applications is based on their biocompatibility, ease of functionalization, and unique optical and electronic properties. Recently, the gold nanoparticles are also found to induce the size-dependent interactions with living tissues. It has been found that gold nanoparticles of different sizes are uptaken by the cells in vitro and by the organs of living specimens in vivo at different rates. Herein, we report the use of gold nanoparticles of different sizes as a gene delivery agent. The gold nanoparticles of 10, 40, and 100 nm diameter were surface functionalized with cationic glycopolymer, and their biocompatibility under physiological conditions was investigated. The stable nanoparticles were then complexed with enhanced cyanine fluorescence protein plasmid (pECFP) and their transfection efficiencies in Hela cell line were studied. It was found that gold nanoparticles of 40 nm core diameter exhibit highest transfection efficiencies compared to the other sizes of nanoparticles studied.

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)0.000
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 score0.007
Threshold uncertainty score0.373

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 head0.012
GPT teacher head0.255
Teacher spread0.244 · 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

Citations44
Published2009
Admission routes1
Has abstractyes

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