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Record W2193980290 · doi:10.1177/039139880502800612

Investigation of a New Microcapsule Membrane Combining Alginate, Chitosan, Polyethylene Glycol and Poly-L-Lysine for Cell Transplantation Applications

2005· article· en· W2193980290 on OpenAlexaff
Tasima Haque, Huilan Chen, Weijie Ouyang, Christopher Martoni, Bisi Lawuyi, A. Urbańska, Satya Prakash

Bibliographic record

VenueThe International Journal of Artificial Organs · 2005
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsMcGill University
Fundersnot available
KeywordsPolyethylene glycolChitosanMembraneLysinePEG ratioTransplantationTissue engineeringPolymerCellChemistryBiocompatible materialMaterials scienceBiomedical engineeringBiochemistryMedicineSurgeryOrganic chemistryAmino acid

Abstract

fetched live from OpenAlex

Microencapsulation of living cells 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. The current study investigates the potential of a novel membrane combining alginate, chitosan, polyethylene glycol (PEG) and poly-L-lysine (PLL) with the objective of proposing a membrane suitable for cell entrapment that may overcome some of the shortcomings of the widely studied alginate-poly-L-lysine-alginate (APA) capsules. The novel microcapsule was formulated using a 1.5% alginate solution coated with 0.05% chitosan, 0.1% PEG and 0.05% poly-L-lysine with a final layer of 0.1% alginate. Microcapsules having a diameter of 450 +/- 30 microm were prepared. Upon citrate treatment, the membrane remained intact and retained its spherical structure. The membrane was able to support liver cell proliferation and the encapsulated cells were capable of secreting proteins. The study demonstrated that the new membrane can be used for cell entrapment. However, further investigations are needed to assess its potential for long term transplantation and usage in the development of bioartificial organs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.029
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.018
GPT teacher head0.262
Teacher spread0.244 · 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 teacher head, 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

Citations37
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueThe International Journal of Artificial OrgansSame topicPancreatic function and diabetesFrench-language works237,207