Preparation of Chitosan-Plasmid DNA Nanoparticles Encoding Interleukin-12 and their Expression in CT-26 Colon Carcinoma Cells
Bibliographic record
Abstract
PURPOSE: Interleukin-12 (Il-12) as a cytokine has been proved to possess antitumor effects via stimulating the immune system. Non-viral gene delivery systems exhibit low toxicity and are easier to prepare compared to their viral counterparts. In this study, we aimed to prepare plasmid DNA loaded chitosan nanoparticles for expression of Il-12 and to evaluate their physicochemical characteristics, cytotoxicity and transfection efficiency in Murine CT-26 colon carcinoma cells. METHODS: Nanoparticles were prepared using a complex coacervation process at different N/P ratios and characterized in terms of size, zeta potential, polydispersity index, morphology, encapsulation efficiency and polyplex formation. The cytotoxicities and transfection efficiencies of the prepared polyplexes were evaluated by MTT assay and ELISA (for hIL-12, p70), respectively. RESULTS: Size and zeta potential varied from 76.73 to 867.03 nm and between 5.68 and 16.77 mV, respectively. Strong attachment of the DNA to chitosan was observed after polyplex preparation. Encapsulation efficiencies were high (72.97-94.87%). The transfection efficiencies of the prepared complexes were obviously higher than those of naked pDNA when N/P ratios were between 16 and 60. Maximum level of phIL-12 expression was obtained at (N/P = 16) with mean particle size of 381.83±82.77 nm (polydispersity index=0.44) indicating the improved transfection of pUMVC3-hIL12 about 2.80 times compared to that of the naked pUMVC3-hIL12. Prepared polyplexes were nontoxic to CT-26 cells. CONCLUSIONS: Chitosan-DNA nanoparticles at N/P = 16 with minimal cytotoxicity, can be used as suitable candidate for Il-12 delivery. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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) | 0.000 | 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 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".