<i>In Situ</i> Gelling Liquid Crystalline System as Local siRNA Delivery System
Bibliographic record
Abstract
An effective short interfering RNA (siRNA) delivery system protects the siRNA from degradation, facilitates its cellular uptake, and promotes its release into the cytoplasm. Local administration of siRNA presents advantages over systemic administration, such as the possibility to use lower doses and allow local and sustained release. In this context, in situ solidifying organogels based on monoglycerides (MO), polyethylenimine (PEI), propylene glycol (PG) and tris buffer are an attractive strategy for intratumoral delivery of siRNA. In this study, precursor fluid formulation (PFF) composed of MO/PEI/PG/tris buffer at 7.85:0.65:76.5:15 (w/w/w/w) was used to deliver siRNA to tumor cells. The internal structure of the gel obtained from PFF was characterized using small angle X-ray scattering (SAXS). In addition, its ability to complex siRNA, protect it from degradation, and functionally deliver it to tumor cells was investigated. Moreover, in vivo gel formation following intratumoral injection was evaluated. The gel formed in excess water from PFF was found to comprise a mixture of hexagonal and cubic phases. The system was able to complex high amounts of siRNA, protect it from degradation, promote siRNA internalization, and induce gene silencing in vitro in a variety of tumor cell lines. Moreover, a gel formed in situ following intratumoral injection in a murine xenograft model. In conclusion, PFF is a potential delivery system for local and sustained delivery of siRNA to tumor tissue after intratumoral administration.
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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 | 0.001 |
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".