Nanoparticles co-delivering siRNA and mRNA for simultaneous restoration and silencing of gene/protein expression <i>in vitro</i> and <i>in vivo</i>
Bibliographic record
Abstract
Abstract RNA-based agents such as siRNA, miRNA, and mRNA can selectively manipulate gene expression/proteins and have the potential to revolutionize the current therapeutic strategies for various diseases, including cancer. To address the poor stability and inherent limitations of RNA agents, nanoparticle (NP) platforms have been developed to deliver functional mRNA or siRNA inside the cells. Recent studies have focused on either siRNA to knock down proteins causing drug resistance or mRNA technology to introduce tumor suppressors. However, complex diseases like cancer need multi-targeted approaches to selectively target multiple gene expressions/proteins. In this proof-of-concept study, we developed co-delivery nanoparticles containing Luc-mRNA and siRNA-GFP as model RNA agents ((M+S)-NPs) and assessed their effects in vitro and in vivo . Our studies show that NPs can effectively deliver both functional mRNA and siRNA together, simultaneously impacting the expression of two genes/proteins in vitro . Additionally, after in vivo administration, co-delivery NPs successfully knocked down GFP while introducing luciferase in a TNBC mouse model, indicating our NPs have the potential to develop RNA-based anticancer therapeutics. These studies pave the way to develop RNA-based, multitargeted, multi-delivery approaches for complex diseases like cancer. TOC
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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".