Long-term Stable Cationic Solid Lipid Nanoparticles for the Enhanced Intracellular Delivery of SMAD3 Antisense Oligonucleotides in Activated Murine Macrophages
Bibliographic record
Abstract
PURPOSE: Long-term stable cationic solid lipid nanoparticles (cSLNs) were formulated to transfer SMAD3 antisense oligonucleotides (ASOs) into the cells to enhance the intracellular activity of the ASOs. The SMAD3 ASOs were designed to block the inflammatory processes linked to TGFβ/SMAD3 pathway. METHODS: The cSLN formulation was prepared by high-pressure homogenization method composed of 1,2-dioleoyl-3-trimethylammonium propane (DOTAP), dioleoylphosphoethanolamine (DOPE), Tween 20, and tricaprin as a solid lipid core (1:1:1:1.67, w/w). The size and the zeta potential of the prepared cSLNs were measured by light scattering. The cSLN/ASO complexes were generated and introduced into the murine macrophage cells. After the treatment of the complexes, the cellular uptake of the complexes was determined by flow cytometry and the intracellular activity of SMAD3 ASOs from the complexes was evaluated by western blotting of SMAD3. In addition, TGFβ1, an upstream molecule of TGFβ/SMAD3 pathway, was monitored by ELISA. RESULTS: The nano-scale sized cSLNs were positively charged and physically stable at 4oC during the storage up to 24 months. The uptake efficiency of the cSLN/ASO complexes into macrophage cells was enhanced up to 80% without cytotoxicity. After the treatment of the cSLN/ASO complexes, SMAD3 as well as TGFβ1 was significantly suppressed based on the SMAD3 ASO activity in the macrophage cells. In addition, the cSLN/ASO complexes prevented the morphological change to dendritic shape in the activated macrophage cells. CONCLUSION: These results suggest that the cSLNs have a potential to deliver the SMAD3 ASOs to intracellular compartments for the anti-inflammatory effect. The development of this strategy might lead to anti-inflammatory and anti-fibrotic therapies in immunological disorders.
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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 | 0.000 | 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 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".