Enzyme-instructed siRNA Release and Functional Self-assembly of Peptide-based Delivery System
Bibliographic record
Abstract
Cell-penetrating peptide (CPP)-based small interfering RNA (siRNA) delivery is one of the approaches with great potential to achieve RNA interference (RNAi) applied in gene therapy. CPP-based siRNA carriers hold many merits, including biodegradability, high transmembrane efficiency, and capability of endosomal escape. Despite considerably high transfection efficiency achieved, there are still many challenges in further improving the CPP-based siRNA delivery systems. This proposal focuses on two of the challenges: (1) the dissociation of negatively charged siRNA from a positively charged peptide; and (2) minimization of cytotoxicity of CPPs while maintaining capacity in endosomal escape. Herein, we propose to utilize enzyme-catalyzed phosphorylation to induce the transfer of negatively charged phosphate groups onto the cationic CPPs formulated with siRNA; the emergent phosphate groups can facilitate the dissociation of siRNAs from the complex due to electrostatic repulsion between the two negatively charged species, the phosphate groups on the peptide and the siRNA. The presence of phosphate groups also alters the balance between repulsive and attractive forces that govern the self-assembly of the peptide, resulting in conformational changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".