Development of polymeric–cationic peptide composite nanoparticles, a nanoparticle-in-nanoparticle system for controlled gene delivery
Bibliographic record
Abstract
We report the formulation of novel composite nanoparticles that combine the high transfection efficiency of cationic peptide-DNA nanoparticles with the biocompatibility and prolonged delivery of polylactic acid-polyethylene glycol (PLA-PEG). The cationic cell-penetrating peptide RALA was used to condense DNA into nanoparticles that were encapsulated within a range of PLA-PEG copolymers. The composite nanoparticles produced exhibited excellent physicochemical properties including size <200 nm and encapsulation efficiency >80%. Images of the composite nanoparticles obtained with a new transmission electron microscopy staining method revealed the peptide-DNA nanoparticles within the PLA-PEG matrix. Varying the copolymers modulated the DNA release rate >6 weeks in vitro. The best formulation was selected and was able to transfect cells while maintaining viability. The effect of transferrin-appended composite nanoparticles was also studied. Thus, we have demonstrated the manufacture of composite nanoparticles for the controlled delivery of DNA.
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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 | 0.001 | 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".