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Record W4390043022 · doi:10.1507/endocrj.ej23-0474

Encapsulated human islets in alginate fiber maintain long-term functionality

2023· article· en· W4390043022 on OpenAlexaffabout
Junko Nishida, Takahiro Tsuno, Shigeharu G. Yabe, Tatsuya Kin, Satsuki Fukuda, Fujie Takeda, Jun Shirakawa, Hitoshi Okochi

Bibliographic record

VenueEndocrine Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of Alberta HospitalAlberta Hospital Edmonton
Fundersnot available
KeywordsIsletSecretionIn vitroTransplantationInternal medicinePeptideCell cultureCell biologyChemistryEndocrinologyBiologyInsulinBiochemistryMedicine

Abstract

fetched live from OpenAlex

Maintenance of islet function after in vitro culture is crucial for both transplantation and research. Here we evaluated the effects of encapsulation in alginate fiber on the function of human islets which were distributed by the Alberta Islet Distribution Program. Encapsulated human islets from 15 deceased donors were cultured under 5.5 or 25 mM glucose conditions in vitro. The amounts of C-peptide and glucagon secreted from encapsulated islets into the culture media were measured periodically, and immunohistochemical studies were performed. Encapsulated islets maintained C-peptide and glucagon secretion for more than 75 days in 5 cases; in two cases, their secretion was also successfully detected even on day 180. α- and β-cell composition and β-cell survival in islets were unaltered in the fiber after 75 or 180 days of culture. The encapsulated islets cultured with 5.5 mM glucose, but not those with 25 mM glucose, exhibited glucose responsiveness of C-peptide secretion until day 180. We demonstrate that alginate encapsulation enabled human islets to maintain their viability and glucose responsiveness of C-peptide secretion after long-term in vitro culture, potentially for more than for 180 days.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score1.000

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)0.0090.001

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 head0.028
GPT teacher head0.324
Teacher spread0.297 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations4
Published2023
Admission routes2
Has abstractyes

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