Effect of Integrating Polyethylene Glycol to Alginate-Poly-L-Lysine and Alginate Chitosan Microcapsules for Oral Delivery of Live Cells and Cell Transplant for Therapy
Bibliographic record
Abstract
Microencapsulation is an emerging technology that has a wide range of applications ranging from drug delivery to tissue engineering. The primary advantages of microencapsulation are that the technology can be used to orally administer drugs directly to the gastrointestinal system and it may eliminate the requirement of immunosuppressant drugs when used in cell therapy procedures. For the technology to be implemented, it is necessary to obtain an appropriate membrane material. Presently, the most commonly used membrane for cell encapsulation is alginate coated with poly-l-lysine, however, there still remains limitations associated with this membrane. The current study investigates the advantages of adding polyethylene glycol (PEG) to the conventionally studied alginate-poly-l-lysine-alginate (APA) and alginate-chitosan (AC) membrane microcapsules. Stability tests using osmotic pressure reveal the addition of PEG to improve mechanical stability of both APA and AC capsules by over 50% which was determined by a decrease in the number of broken capsules when subjected to osmotic pressure. Morphological studies performed in subjecting the capsules to simulated gastrointestinal fluids show that the addition of polyethylene glycol (PEG) prolongs the stability of APA capsules significantly. APA microcapsules disintegrate within 2 hours compared to 24 hours for capsules integrating PEG. Cytotoxicity tests using human HepG2 cells indicate positive MTT showing the membranes containing PEG can support cellular growth. This study implies that incorporating PEG to alginate microcapsules may lead to improvements in the membrane properties for use in oral delivery or transplantation.
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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 | 0.000 | 0.000 |
| 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".