Leveraging nature’s nanocarriers: Translating insights from extracellular vesicles to biomimetic synthetic vesicles for biomedical applications
Bibliographic record
Abstract
Naturally occurring extracellular vesicles (EVs) and synthetic nanoparticles like liposomes have revolutionized precision diagnostics and medicine. EVs excel in biocompatibility and cell targeting, while liposomes offer enhanced drug loading capacity and scalability. The clinical translation of EVs is hindered by challenges including low yield and heterogeneity, whereas liposomes face rapid immune clearance and limited targeting efficiency. To bridge these gaps, biomimetic synthetic vesicles (SVs) have emerged as innovative platforms, combining the advantageous properties of EVs and liposomes. This review emphasizes critical aspects of EV biology, such as mechanisms of EV-cell interaction and source-dependent functionalities in targeting, immune modulation, and tissue regeneration, informing biomimetic SV engineering. We reviewed a broad array of biomimetic SVs, with a focus on lipid bilayered vesicles functionalized with proteins. These include cell-derived nanovesicles, protein-functionalized liposomes, and hybrid vesicles. By addressing current challenges and highlighting opportunities, this review aims to advance biomimetic SVs for transformative biomedical applications.
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 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) | 0.001 | 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".