Transfection Efficacy and Cellular Uptake of Lipid-Modified Polyethyleneimine Derivatives for Anionic Nanoparticles as Gene Delivery Vectors
Bibliographic record
Abstract
Cationic polyethylenimine (PEI)-based nonviral gene carriers have been desirable to overcome the limitations of viral vectors in gene therapy. A range of PEI derivatives were designed, synthesized, and evaluated for nonviral delivery applications of plasmid DNA (pDNA). Linolenic acid, lauric acid, and oleic acid were covalently conjugated with low-molecular-weight PEI ( M w ∼ 1200 Da) via two different linkers, gallic acid (GA) and p -hydroxybenzoic acid (PHPA), that allows a differential loading of lipids per modified amine (3 vs 1, respectively). 1 H NMR spectrum confirmed the expected structure of the conjugates as well as the level of lipid substitution. SYBR Green binding assay performed to investigate the 50% binding concentration (BC 50 ) of lipophilic polymers to pDNA revealed increased BC 50 with an increased level of lipid substitution. The particle analysis determined that GA- and PHPA-modified lipopolymers gave pDNA complexes with ∼300 and ∼100 nm in size, respectively. At the polymer/pDNA ratio of 5.0, the ζ-potentials of the complexes were negative (−6.55 to −10.6 mV) unlike the complexes with the native PEI (+11.2 mV). The transfection experiments indicated that the prepared lipopolymers showed higher transfection in attachment-dependent cells than in suspension cells based on the expression of the reporter green fluorescent protein (GFP) gene. When loaded with Cy3-labeled pDNA, the lipopolymers exhibited effective cellular uptake in attachment-dependent cells while the cellular uptake was limited in suspension cells. These results demonstrate the potential of lipid-conjugated PEI via GA and PHPA linkers, which are promising for the modification of anchorage-dependent cells.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".