Single-Walled Carbon Nanotubes Noncovalently Functionalized with Lipid Modified Polyethylenimine for siRNA Delivery <i>in Vitro</i> and <i>in Vivo</i>
Bibliographic record
Abstract
siRNA can downregulate the expression of specific genes. However, delivery to specific cells and tissues in vivo presents significant challenges. Modified carbon nanotubes (CNTs) have been shown to protect siRNA and facilitate its entry into cells. However, simple and efficient methods to functionalize CNTs are needed. Here, noncovalent functionalization of CNTs is performed and shown to effectively deliver siRNA to target cells. Specifically, single-walled CNTs were functionalized by noncovalent association with a lipopolymer. The lipopolymer (DSPE-PEG) was composed of a phospholipid 1,2-distearoyl-sn-glycero-3-phosphoethanolamine (DSPE) and poly(ethylene glycol) (PEG). Three different ratios of polyethylenimine (PEI) to DSPE-PEG were synthesized and characterized and the products were used to disperse CNTs. The resulting materials were used for siRNA delivery in vitro and in vivo. The structural, biophysical, and biological properties of DGI/C and their complexes formed with siRNA were investigated. Cytotoxicity of the materials was low, and effective gene silencing in B16-F10 cells was demonstrated in vitro. In addition, significant uptake of siRNA as well as gene silencing in the liver was found following intravenous injection. This approach provides a new strategy for siRNA delivery and could provide insight for the development of noncovalently functionalized CNTs for siRNA therapy.
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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) | 0.001 | 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".