Polymer Blend Controls Nanoparticles’ Surface Charge for Improved Mucus Penetration and Epithelial Cell Adhesion
Bibliographic record
Abstract
Mucus, a viscoelastic gel composed of dense mucin glycoprotein networks, acts as a major barrier to therapeutic delivery across all epithelial surfaces by trapping nanoparticles (NPs) and preventing access to the underlying cells. To address this, we developed mucus-evading yet cell-sticky (MECS) NPs with tunable surface charge using Flash NanoPrecipitation. These 100-nanometer-diameter MECS NPs incorporate a small amount (5 wt %) of polycationic dimethylaminoethyl methacrylate (PDMAEMA) into a dense, neutral poly(ethylene glycol) (PEG) corona, which enables mucus penetration while also driving epithelial cell adhesion. In vitro cell culture and physiologically relevant gut-on-a-chip organoids demonstrate MECS NPs penetrate mucus as effectively as purely PEGylated control NPs, while exhibiting a 45-fold increase in binding to epithelial cells. This dual functionality represents a generalizable strategy for overcoming the long-standing trade-off between mucodiffusion and cellular uptake in mucosal drug delivery.
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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".