Intracellular Delivery of DNA and Enzyme in Active Form Using Degradable Carbohydrate-Based Nanogels
Bibliographic record
Abstract
The facile encapsulation of biomolecules along with efficient formulation and storage makes nanogels ideal candidates for drug and gene delivery. So far, nanogels have not been used for the codelivery of plasmid DNA and proteins due to several limitations, including low encapsulation efficacy of biomolecule of similar charges and the size of cargo materials. In this study, temperature and pH sensitive carbohydrate-based nanogels are synthesized via reversible addition-fragmentation chain transfer (RAFT) polymerization technique and are studied in detail for their capacity to encapsulate and codeliver plasmid DNA and proteins. The temperature sensitive property of nanogels allows the facile encapsulation of biomaterials, while its acid-degradable profile allows the burst release of biomolecules in endosomes. Hence these materials are expected to serve as efficient vectors to deliver biomolecules of choice either alone or as codelivery system. The nanogels produced are relatively monodisperse and are around 30-40 nm in diameter at 37 °C. DNA condensation efficacy of the nanogels is dependent on the hydrophobic property of the core of the nanogels. The DNA-nanogel complexes are formed by the interaction of carbohydrate residues of nanogels with the DNA, and complexes are further stabilized with linear cationic glycopolymers. The DNA-nanogels complexes are also studied for their protein loading capacity. The degradation of the nanogels and the controlled release of DNA and proteins are then studied in vitro. Furthermore, the addition of a nontoxic, cationic glycopolymer to the nanogel-DNA complexes is found to improve the cellular uptake and hence to improve gene expression.
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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".