The Interplay of Endosomal Escape and RNA Release from Polymeric Nanoparticles
Bibliographic record
Abstract
; however, only a small percentage of RNA endocytosed by a cell is delivered to the cytosolic machinery, minimizing its effect. RNA nanocarriers face two major obstacles after endocytosis: endosomal escape and RNA release. Overcoming both obstacles simultaneously is challenging because endosomal escape is usually achieved by using high positive charge to disrupt the endosomal membrane. However, this high positive charge typically also inhibits RNA release because anionic RNA is strongly bound to the nanocarrier by electrostatic interactions. Many nanocarriers address one over the other despite a growing body of evidence demonstrating that both are crucial for RNA transfection. In this review, we survey the various strategies that have been employed to accomplish both endosomal escape and RNA release with a focus on polymeric nanomaterials. We first consider the various requirements a nanocarrier must achieve for RNA delivery including protection from degradation, cellular internalization, endosomal escape, and RNA release. We then discuss current polymers used for RNA delivery and examine the strategies for achieving both endosomal escape and RNA release. Finally, we review various stimuli-responsive strategies for RNA release. While RNA release continues to be a challenge in achieving efficient RNA transfection, many new innovations in polymeric materials have elucidated promising strategies.
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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".