Nanoparticles Codelivering mRNA and SiRNA for Simultaneous Restoration and Silencing of Gene/Protein Expression In Vitro and In Vivo
Bibliographic record
Abstract
RNA-based agents (siRNA, miRNA, and mRNA) can selectively manipulate gene expression/proteins and are set to revolutionize a variety of disease treatments. Nanoparticle (NP) platforms have been developed to deliver functional mRNA or siRNA inside cells to overcome their inherent limitations. Recent studies have focused on siRNA to knock down proteins causing drug resistance or mRNA technology to introduce tumor suppressors. However, cancer needs multitargeted approaches to selectively manipulate multiple gene expressions/proteins. In this proof-of-concept study, we developed NPs containing Luc-mRNA and siRNA-GFP as model agents ((M+S)-NPs) and showed that NPs can simultaneously deliver functional mRNA and siRNA and impact the expression of two genes/proteins in vitro. Additionally, after in vivo administration, (M+S)-NPs successfully knocked down GFP while introducing luciferase into a TNBC mouse model, indicating that our NPs have the potential to develop RNA-based anticancer therapeutics. These studies pave the way to develop RNA-based, multitargeted approaches for complex diseases like cancer.
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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".