Bibliographic record
Abstract
Ribonucleic acid (RNA) has emerged as one of the most promising therapeutic payloads in the field of gene therapy. There are many unique types of RNA that allow for a range of applications including vaccination, protein replacement therapy, autoimmune disease treatment, gene knockdown and gene editing. However, RNA triggers the host immune system, is vulnerable to degradation and has a low proclivity to enter cells spontaneously. Therefore, a delivery vehicle is required to facilitate the protection and uptake of RNA therapeutics into the desired host cells. Lipid nanoparticles have emerged as one of the only clinically approved vehicles for genetic payloads, including in the COVID-19 messenger RNA vaccines. While lipid nanoparticles have distinct advantages, they also have drawbacks, including strong immune stimulation, complex manufacturing and formulation heterogeneity. In contrast, synthetic polymers are a widely studied group of gene delivery vehicles and boast distinct advantages, including biocompatibility, tunability, inexpensiveness, simple formulation and ease of modification. Some classes of polymers enhance efficient transfection efficiency, and lead to lower stimulation of the host immune system, making them more viable candidates for non-vaccine-related applications of RNA medicines. This review aims to identify the most promising classes of synthetic polymers, summarize recent research aimed at moving them into the clinic and postulate the future steps required for unlocking their full potential.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".