Control of DNA incorporation into nanoparticles with poly(<scp>L</scp>-lysine) multilayers
Bibliographic record
Abstract
Nanoparticles coated with DNA and poly(L-lysine) (PLL) were prepared using a layer-by-layer assembly technique under various solution conditions. Both the level of DNA loading into the materials, the rate and the extent of DNA released from films upon degradation were controlled by varying the pH of polyelectrolyte assembly solutions. To determine optimal conditions for DNA loading, analogous films assembled on flat surfaces were prepared under various pH conditions. Using UV–vis spectroscopy and thin-film ellipsometry, it was found that the extent of DNA incorporation could be varied by more than a factor of two, and that the highest loading was obtained for films built using a combination of DNA and poly(L-lysine) solutions of pH = 4 and pH = 7, respectively. The layers coated onto the silica nanoparticles permitted the surface charge to be characterized by zeta potential electrophoresis. Furthermore, the acid–base dissociation constant measured for PLL on the outermost layer of the DNA/PLL film showed that the pKa of PLL can be shifted by more than three units. Film degradability was investigated via the exposure of films assembled under different pH conditions to α-chymotrypsin. The fraction of DNA released from degraded films can also be increased by a factor of three when films are built under conditions of pH = 4 for the DNA solution. The resultant effect on the transfection ability of pEGFP-N1/PLL coated particles was then measured, and results suggest that the control achieved over the bulk film properties also extends to a strong influence on cell uptake and transfection.Key words: polyelectrolyte multilayer, DNA incorporation, enzymatic degradation, nanoparticles, gene therapy.
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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) | 0.001 | 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".