Cell Membrane-Derived Nanoparticles as Biomimetic Nanotherapeutics to Alleviate Fatty Liver Disease
Bibliographic record
Abstract
The prevalence of metabolic dysfunction associated-steatotic liver disease (MASLD) (formerly known as nonalcoholic fatty liver disease; NAFLD) is estimated at around 32% of the world's population, resulting in a major healthcare concern in recent times. Current pharmaceutical methods lack efficacy for the treatment of the disease because of suboptimal pharmacokinetic parameters including poor bioavailability, short half-life, and premature clearance. Designing an efficient drug delivery system that provides a protective environment is critical for addressing these challenges. Such a system should aim to enhance the cellular uptake of drugs, improve their bioavailability, and reduce the chances of rapid clearance. Here, we developed nanoengineered natural cell membrane-derived nanoparticles (CMNs) incorporated with a model drug, rosuvastatin, in the bilayer assembly of CMNs to reduce the accumulation of lipids in hepatocytes, a hallmark of MASLD. We used a cell extrusion technique to develop self-assembled CMNs with precise size control compared to the cell shearing method. Interestingly, the prepared CMNs were found to be nonphagocytic, representing around 1.13% of phosphatidylserine receptors on healthy cells, which allows the possibility of their use as stealth nanoparticles for drug delivery. Furthermore, CMNs exhibit higher drug-loading efficiency, excellent cytocompatibility, and enhanced cellular internalization capabilities. Moreover, we show that the delivery of rosuvastatin-loaded CMNs in the in vitro MASLD model efficiently reduced hepatocyte lipid accumulation, including total cholesterol (26.8 ± 3.1%) and triglycerides (11.8 ± 0.8%), compared to the negative control. Taken together, the nanoengineered biomimetic CMNs enhance the drug's bioactivity in hepatic cells, establishing a foundation for further investigation of this drug delivery system in treating MASLD.
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 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) | 0.002 | 0.007 |
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; both teacher heads agree on what is shown here.
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".