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The IL-1 Receptor Antagonist Anakinra Enhances Survival and Function of Human Islets during Culture: Implications in Clinical Islet Transplantation

2012· article· en· W2331953810 on OpenAlexaff
Ao Zhang, Y. -J. Park, Y. Zhang, Mark Meloche, Garth L. Warnock, Lucy Marzban

Bibliographic record

VenueTransplantation · 2012
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIsletAnakinraTransplantationBeta cellMedicineEndocrinologyInternal medicineInterleukin 1 receptor antagonistAllotransplantationImmunologyReceptor antagonistReceptorDiabetes mellitusAntagonist

Abstract

fetched live from OpenAlex

Beta-cell replacement by human islet transplantation is a promising approach for treatment of type 1 diabetes (T1D). However, it is currently limited by low number of available pancreatic donors and loss of islets during isolation, pre-transplant culture, and following transplantation. Cytokines such as IL-1β have been implicated in beta-cell apoptosis in both T1D and T2D. Anakinra (Kineret) is an interleukin-1 (IL-1) receptor antagonist that competitively inhibits the binding of IL-1 proteins to their membrane receptor and thus blocks the biologic activity of naturally produced IL-1α and IL-1β. Anakinra is currently in clinical use for treatment of IL-1β-mediated inflammation in rheumatoid arthritis. Interestingly, recent studies have shown that Anakinra improves glycemic control in animal models of T2D. In this study, we tested whether treatment with Anakinra could enhance survival and function of isolated human islets during pre-transplant culture as a potential approach to improve quality and/or quantity of isolated islets for transplantation. Freshly isolated islets from five cadaveric human donors were cultured in CMRL (5 mM glucose, 37°C, 10% fetal bovine serum) without or with Anakinra (10 μg/ml) for 2 or 4 days. Beta-cell apoptosis, mass, function, and islet beta/alpha cell ratio were assessed in human islets before and following culture. As expected, culture of human islets resulted in an increase in the proportion of apoptotic (TUNEL-positive) islet beta cells in a time-dependent manner as assessed by immunostaining for insulin and TUNEL (d0: 2.2 ± 1.3%; d2: 4.3 ± 1.5; d4: 6.1 ± 2.0, P< 0.05). Treatment with Anakinra markedly reduced beta-cell apoptosis in cultured human islets (+An (d2): 2.4 ± 1.2; +An (d4): 3.1 ± 1.6, P< 0.05). Reduced beta-cell apoptosis was associated with a decrease in caspase-3 activation in beta cells in Anakinra-treated islets compared to non-treated cultured islets. This decrease in beta-cell apoptosis in Anakinra-treated human islets resulted in an increase in the islet beta/alpha cell ratio (-An (d4): 1.8 ± 0.2 vs +An (d4): 2.1 ± 0.2; P< 0.05) and beta-cell mass (beta-cell mass (% d0): -An (d4): 57 ± 3.4% vs +An (d4): 69 ± 3.8%, P< 0.05). Furthermore, Anakinra-treated islets had markedly enhanced beta-cell function compared to non-treated cultured islets manifested as increased insulin response to elevated glucose (-An (d2): 2.4 ± 0.2 vs +An (d2): 3.5 ± 0.4 fold; -An (d4): 2.1 ± 0.2 vs +An (d4): 3.3 ± 0.2 fold, P< 0.05) and increased islet insulin content. In summary, these studies suggest that blocking IL-1β receptor in isolated human islets reduces beta-cell death, preserves beta-cell mass, and enhances beta-cell function during pre-transplant islet culture. Treatment with IL-1 receptor antagonists may therefore provide a new approach to enhance islet beta-cell survival and function during pre-transplant culture thereby increase the success rate of clinical islet transplantation.

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)0.001
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.325
Teacher spread0.296 · 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".

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Citations0
Published2012
Admission routes1
Has abstractyes

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