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Record W1992584891 · doi:10.2147/ijn.s47413

Linear polyethylenimine produced by partial acid hydrolysis of poly(2-ethyl-2-oxazoline) for DNA and siRNA delivery in vitro

2013· article· en· W1992584891 on OpenAlexafffund
Qin Shi, Julio Fernandes, Jian‐Ding Qiu, Xiaoling Zhang, Dai, Mohamed Benderdour, Françoise M. Winnik

Bibliographic record

VenueInternational Journal of Nanomedicine · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsUniversité de MontréalHôpital du Sacré-Cœur de Montréal
FundersCanadian Institutes of Health ResearchScience and Technology Commission of Shanghai Municipality
KeywordsPolyethylenimineSmall interfering RNATransfectionGene deliveryGene knockdownRNADNAGene silencingChemistryMolecular biologyBiophysicsBiochemistryBiologyGene

Abstract

fetched live from OpenAlex

Polyethylenimines (PEIs) are the most efficient synthetic vectors for gene delivery available to date. With its high charge density and strong proton-buffering effect, PEI has an ability to condense DNA and small interfering RNA at physiologic pH. However, the polymer suffers from the disadvantage of high cellular toxicity. To reduce its cellular toxicity, we synthesized linear PEIs by partial hydrolysis of poly(2-ethyl-2-oxazoline). Three linear PEIs with different hydrolysis percentages (30%, 70%, and 96%, respectively) were produced as PEI30, PEI70, and PEI96. PEI30 and PEI96 cannot be considered as suitable transfection agents because of low transfection efficiency (PEI30) or high cellular toxicity (PEI96). PEI70 displayed very weak cell toxicity. The charge density of this polymer (PEI70) was strong enough to condense DNA and small interfering RNA at a physiologic pH of 7.4. Our results also show that PEI70 was highly efficient in DNA delivery and small interfering RNA-mediated knockdown of target genes. Thus, polymers such as PEI70 appear to be very promising vectors for gene delivery.

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 score0.014
Threshold uncertainty score0.429

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 head0.008
GPT teacher head0.253
Teacher spread0.245 · 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

Citations34
Published2013
Admission routes2
Has abstractyes

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