HDL-Chitosan Nanoparticles for siRNA Delivery as an SR-B1 ReceptorTargeted System
Bibliographic record
Abstract
AIMS: High-Density Lipoprotein (HDL) is a complex structure unique to the human body. ApoA-1 protein is a significant structural/functional protein of HDL and provides a natural interaction with the SR-B1 receptors on the cell membrane. The overexpression of the SR-B1 receptor in the membrane of malignant cells suggests that targeting cancer cells can be possible using HDL. The objective of this study was to prepare HDL-conjugated chitosan nanoparticles containing a genetic material that can be used for liver cancer. METHODS: HDL used in the preparation of the formulations have been obtained by isolating from blood samples taken from healthy volunteers. Bcl-2 siRNA inhibiting BCL-2 oncogene was selected as the genetic material. Chitosan nanoparticles were prepared using the ionic gelation method utilizing low molecular weight chitosan. Physicochemical properties of formulations, transfection efficacy, and cytotoxicity of them on 3T3 and HepG2 cell lines were examined. RESULTS: The average diameters of the selected formulations were below 250 nm with a positive zeta potential value between +36 ± 0.1 and +34 ± 0.5 mV. All formulations protected Bcl-2 siRNA from enzymatic degradation in the presence of serum. Cellular uptake ratios of particles by HepG2 cells were found to be between 76% and 98%. HDL/chitosan nanoparticles/Bcl-2 siRNA complex was found to be more toxic when compared to chitosan nanoparticles/Bcl-2 siRNA complex and naked Bcl-2 siRNA. CONCLUSION: According to attained results, the HDL-conjugated chitosan nanoparticles can bring advantages for targeted siRNA delivery to malignant cells that overexpress SR-B1 receptors, such as HepG2.
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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 | 0.001 | 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".