Fabrication and Optimization of Linear PEI-Modified Crystal Nanocellulose as an Efficient Non-Viral Vector for In-Vitro Gene Delivery
Bibliographic record
Abstract
Background: One of the major challenges in gene therapy is producing gene carriers that possess high transfection efficiency and low cytotoxicity (1).To achieve this purpose, crystal nanocellulose (CNC) -based nanoparticles grafted with polyethylenimine (PEI) have been developed as an alternative to traditional viral vectors to eliminate potential toxicity and immunogenicity.Methods: In this study, CNC-PEI10kDa (CNCP) nanoparticles were synthetized and their transfection efficiency was evaluated and compared with linear cationic PEI10kDa (PEI) polymer in HEK293T (HEK) cells.Synthetized nanoparticles were characterized with AFM, FTIR, DLS, and gel retardation assays.In-vitro gene delivery efficiency by nano-complexes and their effects on cell viability were determined with fluorescent microscopy and flow cytometry.Results: Prepared CNC was oxidized with sodium periodate and its surface cationized with linear PEI.The new CNCP nano-complex showed different transfection efficiencies at different nanoparticle/plasmid ratios, which were greater than those of PEI polymer.CNPC and Lipofectamine were similar in their transfection efficiencies and effect on cell viability after transfection.Conclusions: CNCP nanoparticles are appropriate candidates for gene delivery.This result highlights CNC as an attractive biomaterial and demonstrates how its different cationized forms may be applied in designing gene delivery systems.
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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) | 0.000 | 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".