Emerging vesicular nanosystems capable of effectively targeting hepatocytes
Bibliographic record
Abstract
Liver indications are increasing in prevalence globally, with nanomedicines emerging as a potential treatment strategy. Hepatocytes play an important role in most liver diseases, making the ability to target these cells an important consideration in the development of therapeutics. Although nanomedicines administered intravenously do tend to accumulate in the liver thanks to its high blood flow, specifically targeting hepatocytes remains non-trivial due to several aspects of liver physiology. For example, hepatocytes are challenging to reach within the liver tissue due to limited fenestrae size in the liver vessels. Disease and age further reduce fenestration, meaning that optimal therapeutic effect is difficult to achieve. To overcome these obstacles, different nanomedicines have been rationally designed to possess optimized size and surface properties, along with hepatocyte-specific targeting ligands. This review discusses developments in vesicular nanotechnologies published in English since 2022, within the context of the unique challenges presented by hepatocyte delivery. The lipid nanoparticles, liposomes, niosomes, and lipid calcium phosphate nanoparticles discussed, from pre-clinical research to clinically approved treatments, represent significant advancements toward this goal. However, further challenges remain, and we expect to continue to see advancements in model development and novel nanosystems tailored for effectiveness in diseased states.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".