Additive nanocomplexes of cationic lipopolymers for improved non-viral gene delivery to mesenchymal stem cells
Bibliographic record
Abstract
It has been challenging to modify primary cells with non-viral gene delivery. Herein, we developed a ternary nano-formulation for gene delivery to umbilical cord blood and bone marrow derived mesenchymal stem cells (MSC) by using lipid-modified small (1.2 kDa) molecular weight polyethylenimine (PEI1.2). Linoleic acid (LA) was end-capped with carboxyl functionality by coupling with mercaptopropionic acid through thio-ester linkage, and then grafted onto PEI1.2 via N-acylation. The thio-ester LA grafted PEI1.2 (PEI-tLA) displayed a significantly lower (up to 6-fold) DNA binding capability and a higher propensity to dissociate upon polyanionic challenge. The dissociation ability of the complexes was further enhanced by incorporating hyaluronic acid (HA) into plasmid DNA (pDNA) complexes of PEI-tLA. The HA incorporation influenced the surface charge of complexes more so than the hydrodynamic size, but it clearly increased the propensity for dissociation upon a polyanionic challenge. The PEI-tLAs were less toxic on MSC and displayed significantly higher transgene expression in MSC than conventional PEI-LA. Ternary complexes of with HA (pDNA/HA = 2, w/w) further enhanced the efficiency of PEI-tLAs of low (∼2 lipid/PEI) lipid substitution, which was comparable to or higher than commercial transfection reagents. We conclude that PEI-tLA of low lipid substitution can be employed as a gene carrier to design supersensitive nano-formulations.
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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) | 0.001 | 0.001 |
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".