Poly-<scp>l</scp>-lysine Functionalized Large Pore Cubic Mesostructured Silica Nanoparticles as Biocompatible Carriers for Gene Delivery
Bibliographic record
Abstract
Large pore mesoporous silica nanoparticles (LP-MSNs) functionalized with poly-L-lysine (PLL) were designed as a new carrier material for gene delivery applications. The synthesized LP-MSNs are 100-200 nm in diameter and are composed of cage-like pores organized in a cubic mesostructure. The size of the cavities is about 28 nm with an entrance size of 13.4 nm. Successful grafting of PLL onto the silica surface through covalent immobilization was confirmed by X-ray photoelectron spectroscopy, solid-state (13)C magic-angle spinning nuclear magnetic resonance, Fourier transformed infrared, and thermogravimetric analysis. As a result of the particle modification with PLL, a significant increase of the nanoparticle binding capacity for oligo-DNAs was observed compared to the native unmodified silica particles. Consequently, PLL-functionalized nanoparticles exhibited a strong ability to deliver oligo DNA-Cy3 (a model for siRNA) to Hela cells. Furthermore, PLL-functionalized nanoparticles were proven to be superior as gene carriers compared to amino-functionalized nanoparticles and the native nanoparticles. The system was tested to deliver functional siRNA against minibrain-related kinase and polo-like kinase 1 in osteosarcoma cancer cells. Here, the functionalized particles demonstrated great potential for efficient gene transfer into cancer cells as a decrease of the cellular viability of the osteosarcoma cancer cells was induced. Moreover, the PLL-modified silica nanoparticles also exhibit a high biocompatibility, with low cytotoxicity observed up to 100 μg/mL.
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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".