Targeting extracellular vesicle delivery to the lungs by microgel encapsulation
Bibliographic record
Abstract
Abstract Extracellular vesicles (EVs) secreted by stem and progenitor cells have significant potential as cell‐free ‘cellular’ therapeutics. Yet, small EVs (<200 nm) are rapidly cleared after systemic administration, mainly by the liver, presenting challenges targeting EVs to a specific organ or tissue. Microencapsulation using natural nano‐porous hydrogels (microgels) has been shown to enhance engraftment and increase the survival of transplanted cells. We sought to encapsulate EVs within microgels to target their delivery to the lung by virtue of their size‐based retention within the pulmonary microcirculation. Mesenchymal stromal cell (MSC) derived EVs were labelled with the lipophilic dye (DiR) and encapsulated within agarose‐gelatin microgels. Endothelial cells and bone marrow derived macrophages were able to take up EVs encapsulated in microgels in vitro, but less efficiently than the uptake of free EVs. Following intrajugular administration, microgel encapsulated EVs were selectively retained within the lungs for 72h, while free EVs were rapidly cleared by the liver. Furthermore, microgel‐loaded EVs demonstrated greater uptake by lung cells, in particular CD45 + immune cells, as assessed by flow cytometry compared to free EVs. Microencapsulation of EVs may be a novel tool for enhancing the targeted delivery of EVs for future therapeutic 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) | 0.000 | 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".