Mesenchymal stromal cell encapsulation in uniform chitosan beads using microchannel emulsification
Bibliographic record
Abstract
ABSTRACT Mesenchymal stromal/stem cells hold potential in repairing damaged tissue through paracrine effects. Their delivery though injectable biodegradable microbeads can improve cell retention and survival at the infusion site. A stirred emulsion process was previously implemented to immobilize these cells in injectable chitosan microbeads for cell therapy applications, but this process leads to broad bead size distribution (coefficient of variation > 40 %). Polydisperse beads may negatively affect the viability of the entrapped cells through oxygen limitations, damage to larger beads during injection, and reduced control over the cell payload and treatment reproducibility. The objective of this work was to modify a microchannel emulsification system initially designed for alginate-based encapsulation to immobilize mesenchymal stromal/stem cells in monodisperse chitosan microbeads. The main factors (e.g., microchannel geometry, chitosan solution viscosity, interfacial tension and flow rate) affecting droplet generation and diameter were investigated. The adapted process enabled the production of monodisperse chitosan microbeads with controlled sizes ranging from 600 µm – 1500 µm in diameter at a coefficient of variation less than 10 %. In a single pass through a 21 G syringe needle (ID: 513 µm), the fraction of ruptured beads was significantly reduced for microchannel-generated vs stirred emulsion-generated beads with matching volume-weighed bead diameter (D[4,3]). The viability of the immobilized cells immediately after the process was 95 % ± 2 % and no significant difference in cell survival and growth factor secretion was observed between microchannel and stirred emulsion-generated beads over 3 days of culture. Future directions include channel multiplexing to increase throughput for clinical applications. Although the device was developed for cell encapsulation, this process could be implemented for encapsulation of other biomolecules, bioactive or living cell agents for applications in the food and drug industry.
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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 | 0.000 | 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".