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 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".