The Effect of a Lipid Surface Coating on the Permeation of Upconverting Nanoparticles through a 3D Human Lung Epithelial Model
Bibliographic record
Abstract
Abstract Preclinical studies of nanoparticles for pulmonary therapeutics are often performed on 2D cell cultures or in vitro models that do not include a mucus barrier. However, the mucus layer lining the lungs is an essential barrier for drugs to permeate in order to exert a therapeutic effect. Herein, the role of surface coating of lanthanide‐doped upconverting nanoparticles (UCNPs) and their interaction with the mucus barrier are explored using a patient‐derived 3D cell culture model. The upconverted emissions from the UCNPs are used to track them throughout the 3D model and study their localization as a function of administration time and mucus thickness. Positively charged, ligand‐free, and negatively charged, supported lipid bilayer‐coated UCNPs are evaluated. A substantial difference in the residence time in mucus and mucociliary clearance of each type of UCNP is observed in a realistic and relevant model. As such, these results underscore the need for preclinical investigations in tissue models, especially with respect to the surface properties of the nanoparticles under study.
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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 | 0.001 | 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".