CD44-Targeted Hyaluronic Acid-Coated Redox-Responsive Hyperbranched Poly(amido amine)/Plasmid DNA Ternary Nanoassemblies for Efficient Gene Delivery
Bibliographic record
Abstract
Hyaluronic acid (HA), which can specifically bind to CD44 receptor, is a specific ligand for targeting to CD44-overexpressing cancer cells. The current study aimed to develop ternary nanoassemblies based on HA-coating for targeted gene delivery to CD44-positive tumors. A novel reducible hyperbranched poly(amido amine) (RHB) was assembled with plasmid DNA (pDNA) to form RHB/pDNA nanoassemblies. HA/RHB/pDNA nanoassemblies were fabricated by coating HA on the surface of the RHB/pDNA nanoassembly core through electrostatic interaction. After optimization, HA/RHB/pDNA nanoassemblies were spherical, core-shell nanoparticles with nanosize (187.6 ± 11.4 nm) and negative charge (-9.1 ± 0.3 mV). The ternary nanoassemblies could efficiently protect the condensed pDNA from enzymatic degradation by DNase I, and HA could significantly improve the stability of nanoassemblies in the sodium heparin solution or serum in vitro. As expected, HA significantly decreased the cytotoxicity of RHB/pDNA nanoassemblies due to the negative surface charges. Moreover, it revealed that HA/RHB/pDNA nanoassemblies showed higher transfection activity than RHB/pDNA nanoassemblies in B16F10 cells, especially in the presence of serum in vitro. Because of the active recognition between HA and CD44 receptor, there was significantly different transfection efficiency between B16F10 (CD44+) and NIH3T3 (CD44-) cells after treatment with HA/RHB/pDNA nanoassemblies. In addition, the cellular targeting and transfection activity of HA/RHB/pDNA nanoassemblies were further evaluated in vivo. The results indicated that the interaction between HA and CD44 receptor dramatically improved the accumulation of HA/RHB/pDNA nanoassemblies in CD44-positive tumor, leading to higher gene expression than RHB/pDNA nanoassemblies. Therefore, HA/RHB/pDNA ternary nanoassemblies may be a potential gene vector for delivery of therapeutic genes to treat CD44-overexpressing tumors in vivo.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".