Engineered lipid nanoparticles with synergistic dendritic cell targeting and enhanced endosomal escape for boosted mRNA cancer vaccines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Lipid nanoparticles (LNPs) have emerged as a pivotal carriers for enhancing mRNA therapeutics, particularly in antitumor therapy. However, achieving robust antigen expression remains a major challenge due to limitations in precise targeted delivery and inefficient endosomal escape. In this study, we constructed an ambidextrous LNP to achieve robust tumor-specific antigen expression through targeted cell delivery and enhanced endosomal escape of mRNA-LNPs. To improve endosomal escape, we synthesized a novel pH-responsive PEGylated lipid designed to synergistically enhance membrane fusion effect with ionizable lipids, thereby optimizing the translation of the desired antigen. This is accomplished by promoting the early endosomal escape and endosomal recycling transport. For precise delivery of mRNA-LNPs to dendritic cells, we employed a mannose-modified PEGylated lipid that targets mannose receptors. Our results demonstrated that the combination of mannose-directed targeting and pH-mediated endosomal escape significantly enhances antigen translation and expression, leading to a vigorous immune response, as validated by both in vitro and in vivo experiments. This ambidextrous strategy advances the formulation of LNPs for precise mRNA delivery and effective antigen encoding, facilitating the development of mRNA vaccines in the field of antitumor immunology.
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.
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 it