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Record W4408907343 · doi:10.1021/acs.biomac.5c00169

Lipid-Modified PEI Derivative-Based Binary/Ternary Polyplex Formulations for the Delivery of pDNA and mRNA in Primary Cells

2025· article· en· W4408907343 on OpenAlexaff
Amarnath Praphakar Rajendran, Daniel Nisakar Meenakshi Sundaram, Luis Carlos Morales, Cezary Kucharski, Mohammad Nasrullah, Burcak Bulut, Pavlo Michailo Tsisar, Aislinn D. Maguire, Bradley J. Kerr, Hasan Uludağ

Bibliographic record

VenueBiomacromolecules · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsWomen and Children’s Health Research InstituteUniversity of Alberta
Fundersnot available
KeywordsChemistryTernary operationMessenger RNADerivative (finance)Gene deliveryTransfectionBiophysicsBiochemistryBiologyGene

Abstract

fetched live from OpenAlex

A previous study has demonstrated the benefit of modification of polyethylenimine (PEI1.2k) by lipids through a p-hydroxyphenylacetic acid (PHPA) linker and polyanion (PA), which is now extended in this report to several primary cells. The formulated binary (lipopolymer/NAs) and ternary (lipopolymer/NAs/PA) complexes displayed no significant toxicity (MTT/hemolysis assay). The pDNA/mRNA complexes with PEI1.2k-PHPA-Lin9 and PEI1.2k-PHPA-Lau5-Ole5 lipopolymers showed gene expression levels higher than those of other lipopolymers. The transfection efficiencies of the ternary polyplexes of these lipopolymers possessed higher gene expression than those of the binary polyplexes. The serum-stable ternary polyplexes of PEI1.2k-PHPA-Lau5-Ole5 maintained high levels of mRNA expression in the lungs along with the spleen after intravenous injection. As in in vitro studies, transgene expression was relatively weak with binary complexes in muscle; however, a 10-fold higher efficiency was obtained with ternary complexes. Overall, our results provide improved gene formulations for the transfections of primary cells in vitro, as well as in in vivo animal models.

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 score0.035
Threshold uncertainty score0.500

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 head0.013
GPT teacher head0.259
Teacher spread0.246 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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