Design and Development of Light-Sensitive Chitosan-Based Nanocarriers for Gene Delivery
Bibliographic record
Abstract
In this work, we report on the development of a multifunctional and photo-inducible nanoplex made of chitosan (Ch) and hyaluronic acid (HA) for delivery of nucleic acid. Self-assembled Ch/HA nanoparticles were attached to ortho-nitrobenzyl (o-NB) photo-labile molecules (PL)-gold nanoparticles via thiol groups and to QDs-conjugate ssDNA through amide bond linkage to form nanoplexes (Ch:HA:AuPL:QD-DNA). The composition of DNA nanocarriers was validated by nuclear magnetic resonance, transmission electronic microscopy, energy dispersive x-ray spectroscopy, gel electrophoresis and spectrophotometry. The change in zeta potential (34 ± 11 to -26 ± 11 mV) and the loss of the o-NB characteristic peaks in nuclear magnetic resonance spectra, after the exposure of the PL molecule to ultraviolet light, both confirmed the photo-labile properties of the system. The potential of the nanoplexes to induce high cell transfection was assessed by flow cytometry and fluorescent microscopy imaging. Over 30% transfection of HEK-293 was obtained with the nanoplexes after a one-minute exposure of cells to UV light. This corresponds to a 15% increase in the transfection efficiency compared to unexposed Ch:HA:AuPL:QD-DNA nanocarriers. This high transfection efficiency was associated with the unique design of the carrier system and its photo-responsiveness feature for facilitating the DNA release.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".