Microcapsules combining alginate, chitosan, poly-l-lysine and polyethyelene glycol for liver cell transplant and cell therapy applications
Bibliographic record
Abstract
Liver diseases are the eighth leading cause of death in North America. Currently, liver transplant is the available treatment for patients with liver failure. However, the shortage of donors and the requirement of immunosuppressant remain a disadvantage. Microencapsulation of living cells is an emerging technology which may serve as an alternative therapy for patients requiring organ transplants. One of the limiting factors in the progress of such therapy is attaining a biocompatible and mechanically stable polymer. In the following thesis, a novel microcapsules combining alginate, poly-l-lysine, chitosan and polyethylene glycol (ACPPA) was designed and evaluated for its use in the treatment of liver failure. In vitro studies were also conducted to compare the novel membrane, with other microcapsules, including the widely studied APA microcapsules as well as alginate coated with chitosan (AC), APA with PEG (APPA) and AC with PEG (ACP). Results show that the novel membrane can support liver cell proliferation and function and is capable of providing cell immuno-protection. The study reveals that chitosan and PEG containing microcapsules can be an alternate material for cell microencapsulation to be used for live cell delivery and other biomedical applications. Further in-vivo studies are recommended to evaluate the full potentials.
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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) | 0.006 | 0.002 |
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".