Glycerol-Based Polymer to Improve the Cellular Uptake of Liposomes
Bibliographic record
Abstract
Nanomedicines modify the pharmacology of pharmaceutical ingredients, but most require cell internalization to deliver their payloads. Hence, modifying the surface properties of nanomedicines can improve their interactions with cells and modulate their pharmacology. Herein, we devised a polymer that increases how nanomedicines are internalized by cells. The alkylated poly(monoglycerol acrylate) (PMGA) polymer was synthesized by reversible addition-fragmentation chain-transfer (RAFT) polymerization with a terminal double 18-carbon moiety that allows its anchoring on the surface of liposomes. PMGA-decorated liposomes are internalized more efficiently in immune cells, compared to formulations without the polymer. Using inhibitors of internalization pathways, we established that PMGA promotes cell entry by the fast endophilin-mediated endocytosis (FEME). In comparison, noncoated control liposomes were mostly internalized by clathrin-mediated endocytosis. This work highlights the potential of PMGA to increase the internalization of nanomedicines by immune cells, and target a novel internalization pathway.
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".