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 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".