Chitosan Microbeads Produced by One-Step Scalable Stirred Emulsification: A Promising Process for Cell Therapy Applications
Bibliographic record
Abstract
Cell microencapsulation is a promising approach to improve cell therapy outcomes by protecting injected cells from rapid dispersion and allowing bidirectional diffusion of nutrients, oxygen, and waste that promote cell survival in the target tissues. Here, we describe a simple and scalable emulsification method to encapsulate animal cells in chitosan microbeads using thermosensitive gel formulations without any chemical modification and cross-linker. The process consists of a water-in-oil emulsion where the aqueous phase droplets contain cells (L929 fibroblasts or human mesenchymal stromal cells), chitosan acidic solution and gelling agents (sodium hydrogen carbonate and phosphate buffer or beta-glycerophosphate). The oil temperature is maintained at 37 °C, allowing rapid physical gelation of the microbeads. Alginate beads prepared with the same method were used as a control. Microbeads with a diameter of 300-450 μm were successfully produced. Chitosan and alginate (2% w/v) microbeads presented similar rigidity in compression, but chitosan microbeads endured >80% strain without rupture, while alginate microbeads presented fragile breakage at <50% strain. High cell viability and metabolic activity were observed after up to 7 days in culture for encapsulated cells. Mesenchymal stromal cells encapsulated in chitosan microbeads released higher amounts of the vascular endothelial growth factor after 24 h compared to the cells encapsulated in manually cast macrogels. Moreover, microbeads were injectable through 23G needles without significant deformation or rupture. The emulsion-generated chitosan microbeads are a promising delivery vehicle for therapeutic cells because of their cytocompatibility, biodegradation, mechanical strength, and injectability. Clinical-scale encapsulation of therapeutic cells such as mesenchymal stromal cells in chitosan microbeads can readily be achieved using this simple and scalable emulsion-based process.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".