mRNA delivery enabled by metal–organic nanoparticles
Bibliographic record
Abstract
mRNA therapeutics are set to revolutionize disease prevention and treatment, inspiring the development of platforms for safe and effective mRNA delivery. However, current mRNA delivery platforms face some challenges, including limited organ tropism for nonvaccine applications and inflammation induced by cationic nanoparticle components. Herein, we address these challenges through a versatile, noncationic nanoparticle platform whereby mRNA is assembled into a poly(ethylene glycol)-polyphenol network stabilized by metal ions. Screening a range of components and relative compositional ratios affords a library of stable, noncationic, and highly biocompatible metal-organic nanoparticles with robust mRNA transfection in vitro and in mice. Intravenous administration of the lead mRNA-containing metal-organic nanoparticles enables predominant protein expression and gene editing in the brain, liver, and kidney, while organ tropism is tuned by varying nanoparticle composition. This study opens an avenue for realizing metal-organic nanoparticle-enabled mRNA delivery, offering a modular approach to assembling mRNA therapeutics for health applications.
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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".