Polyethylenimine600-β-cyclodextrin: a promising nanopolymer for nonviral gene delivery of primary mesenchymal stem cells
Bibliographic record
Abstract
Genetically modified mesenchymal stem cells (MSCs) have great potential in the application of regenerative medicine and molecular therapy. In the present manuscript, we introduce a nanopolymer, polyethylenimine600-β-cyclodextrin (PEI600-β-CyD), as an efficient polyplex-forming plasmid delivery agent with low toxicity and ideal transfection efficiency on primary MSCs. PEI600-β-CyD causes significantly less cytotoxicity and apoptosis on MSCs than 25 kDa high-molecular-weight PEI (PEI25kDa). PEI600-β-CyD also exhibits similar transfection efficiency as PEI25kDa on MSCs, which is higher than that of PEI600Da. Quantum dot-labeled plasmids show that PEI600-β-CyD or PEI25kDa delivers the plasmids in a more scattered manner than PEI600Da does. Further study shows that PEI600-β-CyD and PEI25kDa are more capable of delivering plasmids into the cell lysosome and nucleus than PEI600Da, which correlates well with the results of their transfection-efficiency assay. Moreover, among the three vectors, PEI600-β-CyD has the most capacity of enhancing the alkaline phosphatase activity of MSCs by transfecting bone morphogenetic protein 2, 7, or special AT-rich sequence-binding protein 2. These results clearly indicate that PEI600-β-CyD is a safe and effective candidate for the nonviral gene delivery of MSCs because of its ideal inclusion ability and proton sponge effect, and the application of this nanopolymer warrants further investigation.
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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".