<scp>DNA</scp> –Chitosan Nanoparticles for Gene Therapy: Current Knowledge and Future Trends
Bibliographic record
Abstract
Abstract The sections in this article are Introduction Chitosan as a Carrier for Gene Therapy Chitosan Chemistry General Strategies for Chitosan Modification Chitosan– DNA interactions: Transfection Efficacy of Unmodified Chitosan Modified Chitosans: Strategies to Improve the Transfection Efficacy The Effects of Charge Density/Solubility and Degree of Acetylation Improving the Physicochemical Characteristics of the Nanoparticulate Systems: Solubility, Aggregation and RES Uptake Targeting Mediated by Cell Surface Receptors Hydrophobic Modification: Protecting the DNA and Improving the Internalization Process Methods of Preparation of Chitosan Nanoparticles Complex Coacervation Crosslinking Methods Chemical Crosslinking Ionic Crosslinking or Ionic Gelation Emulsion Crosslinking Spray Drying Other Methods DNA Loading into Nano‐ and Microparticles of Chitosan DNA Release and Release Kinetics Preclinical Evidence of Chitosan– DNA Complex Efficacy Potential Clinical Applications of Chitosan– DNA in Gene Therapy Conclusion Acknowledgments
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".