Cationic Nanoparticles Assembled from Natural-Based Steroid Lipid for Improved Intracellular Transport of siRNA and pDNA
Bibliographic record
Abstract
Developing new functional biomaterials from biocompatible natural-based resources for gene/drug delivery has attracted increasing attention in recent years. In this work, we prepared a series of cationic nanoparticles (Diosarg-DOPE NPs) by assembly of a natural steroid diosgenin-based cationic lipid (Diosarg) with commercially-available helper lipid 1,2-dioleoyl-sn-glycero-3-phosphorethanolamine (DOPE). These cationic Diosarg-DOPE NPs were able to efficiently bind siRNA and plasmid DNA (pDNA) via electrostatic interactions to form stable, nano-sized cationic lipid nanoparticles instead of lamellar vesicles in aqueous solution. The average particle size, zeta potentials and morphologies of the siRNA and pDNA complexes of the Diosarg-DOPE NPs were examined. The in vitro cytotoxicity of NPs depends on the dose and assembly ratio of the Diosarg and DOPE. Notably, the intracellular transportation efficacy of the exogenesis siRNA and pDNA could be greatly improved by using the Diosarg-DOPE NPs as the cargoes in H1299 cell line. The results demonstrated that the self-assembled Diosarg-DOPE NPs could achieve much higher intracellular transport efficiency for siRNA or pDNA than the cationic lipid Diosarg, indicating that the synergetic effect of different functional lipid components may benefit the development of high efficiency nano-scaled gene carriers. Moreover, it could be noted that the traditional “lysosome localization” involved in the intracellular trafficking of the Diosarg and Diosarg-DOPE NPs, indicating the co-assembly of helper lipid DOPE, might not significantly affect the intracellular localization features of the cationic lipids.
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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".