Extracellular Vesicle Production Loaded with Nanoparticles and Drugs in a Trade‐off between Loading, Yield and Purity: Towards a Personalized Drug Delivery System
Bibliographic record
Abstract
Extracellular vesicles (EVs) released by cells and circulating in body fluids are recognized as potent vectors of intercellular self-communication. Due to their cellular origin, EVs hold promise as naturally targeted "personalized" drug delivery system insofar as they can be engineered with drugs or theranostic nanoparticles. However, technical hurdles related to their production, drug loading, purification, and characterization restrain the translation of self-derived EVs into a clinical drug delivery system. Herein, different methods are compared to generate and to purify EVs encapsulating iron oxide nanoparticles and a clinical photosensitizer drug (Foscan) as biocamouflaged agents for photodynamic therapy, magnetic resonance imaging, magnetic manipulation, and hyperthermia. Theranostic EVs are produced from drug- and nanoparticle-loaded endothelial cells either by spontaneous release in complete medium, by starvation in serum-free medium or by mechanical stress in a microfluidic chip mimicking vessel shear stress, and purified by ultracentrifugation or magnetic sorting. The impact of the production and purification protocols is investigated on EV yield and size, nanoparticle and drug cargo, and finally on their therapeutic efficacy. EV production by starvation combined with purification by ultracentrifugation may be considered a reasonable trade-off between loading, yield, and purity for biogeneration of theranostic EVs.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".