Superior Cell Delivery Features of Poly(ethylene glycol) Incorporated Alginate, Chitosan, and Poly-<scp>l</scp>-lysine Microcapsules
Bibliographic record
Abstract
Microencapsulation is an emerging technology in the development of bioartificial organs for drug, protein, and delivery systems. One of the advancements in establishing an appropriate membrane material for live cell and tissue encapsulation is the incorporation of poly(ethylene glycol) (PEG) to the widely studied alginate microcapsules. The current study investigates the properties of integrating PEG to microcapsules coated with poly-L-lysine (PLL) and chitosan as well as a novel microcapsule membrane which combines both PLL and chitosan. Results show that microcapsules containing PEG can support cell viability and protein secretion. The addition of PEG to PLL and chitosan-coated microcapsules improves the stability of microcapsules when exposed to a hypotonic solution. We also compared the novel microcapsule with two other previously used microcapsules including alginate-chitosan-PEG and alginate-PLL-PEG-alginate. Results show that all three membranes are capable of providing immunoprotection to the cells and have the potential for long-term storage at -80 degrees C. The novel membrane containing PEG, chitosan, and PLL, however, revealed the highest cell viability and mechanical strength when exposed to external rotational force, but it was unable to sustain osmotic pressure. The study revealed the potential of using PEG-incorporated alginate, chitosan, and PLL microcapsules for encapsulating live cells producing proteins and hormones for therapy.
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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) | 0.000 | 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 | 0.001 | 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".