Improved Gene Transfer with Functionalized Hollow Mesoporous Silica Nanoparticles of Reduced Cytotoxicity
Bibliographic record
Abstract
Gene therapy is a promising strategy for treatment of genetically caused diseases. Successful gene delivery requires an efficient carrier to transfer the desired gene into host cells. Recently, mesoporous silica nanoparticles (MSNs) functionalized with 25 kD polyethyleneimine (PEI) were extensively used as gene delivery carriers. However, 25 kD PEI could significantly reduce the safety of the modified MSNs although it is efficient for intracellular delivery of nucleic acids. In addition, limited drug loading remains a challenge for conventional MSNs drug carriers. Hollow mesoporous silica nanoparticles (HMSNs) with high pore volume, tunable pore size, and excellent biocompatibility are attractive alternatives. To make them more efficient, a less toxic 1.8 kD PEI polymer was used to functionalize the HMSNs which have large pore size (~10 nm) and form PEI-HMSNs. Scanning and transmission electron microscopic images showed that HMSNs were spherical in shape and approximately 270 nm in diameter with uniform hollow nanostructures. The maximum loading capacity of green fluorescent protein labeled DNA (GFP-DNA) in PEI-HMSNs was found to be 37.98 mg/g. The loading capacity of PEI-HMSNs was nearly three-fold higher than those of PEI modified solid nanoparticles, indicating that both hollow and large pores contributed to the increase in DNA adsorption. The transfection of GFP-DNA plasmid loaded in PEI-HMSNs was increased two-fold in comparison to that of 25 kD PEI. MTT assays in Lovo cells showed that the cell viability was more than 85% when the concentration of PEI-HMSNs was 120 µg/mL, whereas the cell viability was less than 20% when the 25 kD PEI was used at the same concentration. These results indicated that PEI-HMSNs could be used as a delivery system for nucleic acids due to good biocompatibility, high gene loading capacity, and enhanced gene transfer efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".