<p>Evidence Supporting the Safety of Pegylated Diethylaminoethyl-Chitosan Polymer as a Nanovector for Gene Therapy Applications</p>
Bibliographic record
Abstract
PURPOSE: Diethylaminoethyl-chitosan (DEAE-CH) is a derivative with excellent potential as a delivery vector for gene therapy applications. The aim of this study is to evaluate its toxicological profile for potential future clinical applications. METHODS: /siRNA). Based on the guidelines from the International Organization for Standardization (ISO), the American Society for Testing and Materials (ASTM), and the Nanotechnology Characterization Laboratory (NCL), we evaluated the effects of the interaction between these nanoparticles and blood components. In vitro screening assays such as hemolysis, hemagglutination, complement activation, platelet aggregation, coagulation times, cytokine production, and reactive species, such as nitric oxide (NO) and reactive oxygen species (ROS), were performed on erythrocytes, plasma, platelets, peripheral blood mononuclear cells (PBMC) and Raw 264.7 macrophages. Moreover, MTS and LDH assays on Raw 264.7 macrophages, PBMC and MG-63 cells were performed. RESULTS: /siRNA nanoparticles falls within the guidelines' thresholds: <1% hemolysis, 2.9% platelet aggregation, no complement activation, and no effect on coagulation times. ROS and NO production levels were comparable to controls. Cytokine secretion (TNF-α, IL-6, IL-4, and IL-10) was not affected by nanoparticles except for IL-1β and IL-8. Nanoparticles showed a slight agglutination. Cell viability was >70% for TPC in all cell types, although LDH levels were statistically significant in Raw 264.7 macrophages and PBMC after 24 and 48 h of incubation. CONCLUSION: /siRNA nanoparticles fulfill the existing ISO, ASTM and NCL guidelines' threshold criteria, and their low toxicity and blood biocompatibility warrant further investigation for potential clinical applications.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".