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INVESTIGATION OF A NOVEL MICROCAPSULE MEMBRANE INTEGRATING POLYETHYLENE GLYCOL TO ALGINATE, POLY-L-LYSINE AND CHITOSAN MICROCAPSULES FOR THE APPLICATION OF LIVER CELL TRANSPLANTATION

2004· article· en· W1967003622 on OpenAlexaff
Tasima Haque, H Chen, Weijie Ouyang, Terrence Metz, Christopher Martoni, Satya Prakash

Bibliographic record

VenueTransplantation · 2004
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsMcGill University
Fundersnot available
KeywordsBiocompatibilityChitosanMembranePolyethylene glycolPEG ratioTransplantationTissue engineeringChemistryCell encapsulationMaterials scienceBiomedical engineeringSelf-healing hydrogelsBiochemistryPolymer chemistryMedicineSurgeryOrganic chemistry

Abstract

fetched live from OpenAlex

P668 Aims: Liver transplantation is the main form of therapy for most liver diseases. A major drawback to transplantation is the lack of donors and continuous requirement of immunosuppressants. Microencapsulation is an emerging technology which can be used to entrap isolated hepatocytes for cell transplanation as an alternative treatment to several liver diseases. One of the limiting factors in the progress of such therapy is attaining a biocompatible polymer enabling the long-term entrapment and growth of the hepatocytes without causing adverse host immune responses. Presently the most commonly studied membranes are the alginate-poly-l-lysine-alginate (APA) and alginate-chitosan (AC) microcapsules, however there remain limitations associated with these membranes. In the current study, improvements to the biocompatibility and stability of these membranes are investigated by the addition of a polyethylene glycol (PEG) coating and the potential of a novel membrane combining alginate, poly-l-lysine, chitosan and PEG is studied with the objective of proposing a membrane most suitable for cell entrapment. Methods: Five different microcapsules were prepared including APA, APA with PEG, AC, AC with PEG and the novel alginate-chitosan-PEG-PLL-alginate (ACPPA) microcapsule. Mechanical strength of the capsules were assessed using an osmotic pressure test and a rotational stress test. A fluorescence reader (FLx800) was used to detect permeability of dextran through the membranes. Morphological studies on capsule integrity in serum was observed microscopically. Cytotoxicity tests were perfomed by encapsulating Human HepG2 cells and performing an MTT calorimetric assay for monitroing metabolic activity. Further studies on immuno protection using macrophages and cryopreservation potential are also to be investigated. Results: Microcapsules of approximately 400+/-30μm were prepared. Stability tests, using osmotic pressure techniques, reveal the addition of PEG resulted in an increase in mechanical stability of both APA and AC capsules by over 50%. The rotational stress test indicate the novel membrane formulation to exhibit mechanical strength similar to APA membranes and greater than the AC, ACPEG and APPEG microcapsules. The ACPPA membrane was found to retain integrity in FBS. Cytotoxicity tests using human HepG2 cells indicate low viability of AC membranes however, positive MTT was observed for the remaining 4 membranes studied which implies that the addition of PEG can support cellular growth. Conclusions: Results support previous studies which indicate PEG may improve biocompatibility of polymers. The integration of PEG to microcapsules enhances mechanical strength and supports the proliferation of liver cells. This study confirms that the novel membrane combining PLL, chitosan and PEG shows similar mechanical properties as APA capsules. As previous studies indicate that APA encapsulated cells can result in cell growth and protein adhesion to the membrane surface, the new membrane may be an alternative biomaterial for microencapsulation cell therapy.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.241
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2004
Admission routes1
Has abstractyes

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