Optimizing the production of alginate microbeads for animal cell aggregate encapsulation using microchannel emulsification
Bibliographic record
Abstract
Cell encapsulation is a versatile and impactful tool in the biomedical field, with applications in cell therapy, regenerative medicine, drug delivery, and tissue engineering.Microchannel emulsification is a promising technique for water-in-oil droplet production and cell encapsulation.This technique was previously applied to encapsulate mammalian cells using alginate internal gelation biopolymer beads, as well as thermoresponsive chitosan beads.The aim of this work was to apply the technique to encapsulate cellular aggregates including insulin-producing MIN6 cells and human islets of Langerhans.Two main challenges were anticipated: (1) cell viability and death due to acid exposure and (2) increased aggregate settling times which reduces aggregate encapsulation efficiency over time.Aggregate cell viability was a concern since the gelation time and acid concentration was originally designed for beads that were 3 to 4 times larger in diameter than the ones currently produced.The shortening of the bead residence time in acidified oil from 10 minutes to 1 minute made gelled beads with viable cell aggregates.Aggregate sedimentation was an important limiting factor during the process.Beta cell aggregates settled significantly within the alginate solution which resulted in no significant aggregates presence in alginate beads after 15 min in a 60 min production process.To address this challenge, several parameters impacting aggregate settling behaviour were modified: aggregate diameter, dispersed phase density and mechanical stirring.After systematic optimization and analysis, it was shown that the shortening of operating time, and combining optimal conditions had the best impact on increasing cell aggregate encapsulation efficiency.The optimal conditions achieved an average of one cell aggregate per bead.A single encapsulation run at 0.02 mL/min over 60 minutes produced approximately 23,000 beads, with 61% containing at least one cell aggregate.Encapsulated cells remained viable 48 hours post-encapsulation, showing no evidence of dead cores.Overall, this thesis seeks to be the first to successfully encapsulate animal cell aggregates using microchannel emulsification in alginate internal gelation microbeads.I would like the thank my supervisor, Dr. Corinne Hoesli for proposing this project and guiding me these past 2 years.Despite the project having numerous unforeseen difficulties and her rapidly growing lab, she makes time to help me whenever I needed it.Her responses are quick and insightful and always amazing me.Thank you for giving me this opportunity.I would also like to thank Richard Leask for letting me use his biosafety cabinet, fridge, incubator and many more equipment for my experiments.I would also like to thank Richard, Gerald and Austin for repairing the BSC.I would like to thank Sam for machining the pieces for my device modifications and helping with any problems in the designs I made
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".