Phospholipid‐Free Small Unilamellar Vesicles for Drug Targeting to Cells in the Liver
Bibliographic record
Abstract
It is reported that cholesterol (Chol) and TWEEN 80 at a molar ratio of 5:1 can form small unilamellar vesicles (SUVs) using a staggered herringbone micromixer. These phospholipid-free SUVs (PFSUVs) can be actively loaded with a model drug for targeting hepatocytes via the endogenous apolipoprotein mechanism. PFSUVs particles with compositions of Chol:TWEEN 80 ranging between 1.5:1 and 5:1 (mol/mol) can be produced with a mean diameter of ≈80 nm, but only the high-Chol formulations (3:1 and 5:1) can retain a transmembrane gradient of ammonium sulfate for active loading of doxorubicin (DOX). Under cryo-transmission electron microscopy, PFSUVs-DOX displays a unilamellar bilayer structure with DOX molecules forming spindle-shape aggregates inside the aqueous core. Relative to PEGylated liposomal doxorubicin (PLD) that exhibits little interaction with cells in various conditions, the cellular uptake of PFSUVs-DOX is dependent on the presence of serum and enhanced with an increased concentration of apolipoproteins. After intravenous injection, the vast majority of PFSUVs-DOX accumulates in the liver and DOX is detected in all liver cells (predominantly the hepatocytes), while PLD is captured only by the sinusoidal cells (i.e., macrophages). This report discloses an innovative lipid bilayer vesicle for highly efficient and selective hepatocyte targeting.
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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) | 0.000 | 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 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".