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Record W2124242809 · doi:10.1039/c4bm00202d

Cellulose-based polyelectrolyte complex nanoparticles for DNA vaccine delivery

2014· article· en· W2124242809 on OpenAlexafffund
Yongbo Song, Ying Zhou, Sylvia van Drunen Littel‐van den Hurk, Lingyun Chen

Bibliographic record

VenueBiomaterials Science · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsUniversity of SaskatchewanUniversity of Alberta
FundersAlberta Livestock and Meat AgencyAlberta Crop Industry Development Fund
KeywordsLipofectamineTransfectionDNANanoparticlePolyelectrolyteChemistryCarboxymethyl celluloseGene deliveryCelluloseConjugateBiophysicsDNA vaccinationPlasmidCombinatorial chemistryNanotechnologyBiochemistryMaterials sciencePolymerSodiumOrganic chemistryBiologyVector (molecular biology)Gene

Abstract

fetched live from OpenAlex

Cellulose-based nanoparticles were prepared from oppositely charged carboxymethyl cellulose (CMC) and quaternized cellulose (QC) in an aqueous medium. The DNA complexing capacity of CMC-QC nanoparticles and the transfection efficiency of the DNA-loaded nanoparticles in COS-7 cells were first investigated using pEGFP-N1-a plasmid DNA encoding the enhanced green fluorescent protein (EGFP)-as a model, and then with a candidate DNA vaccine, pMASIA-tPAs-tE2.2, that has been developed against infection with bovine viral diarrhea virus (BVDV). The results revealed that CMC-QC nanoparticles could bind DNA efficiently, and the optimized DNA-loaded nanoparticles mediated very effective transfection in COS-7 cells, which was comparable to that achieved with Lipofectamine 2000. The novel CMC-QC nanoparticles show promise as a delivery system for DNA vaccines.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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 head0.017
GPT teacher head0.257
Teacher spread0.240 · 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

Citations35
Published2014
Admission routes2
Has abstractyes

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