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Record W2810239357 · doi:10.1007/s00125-018-4672-5

Bioengineered human pseudoislets form efficiently from donated tissue, compare favourably with native islets in vitro and restore normoglycaemia in mice

2018· article· en· W2810239357 on OpenAlexafffund
Yang Yu, Anissa Gamble, Rena Pawlick, Andrew R. Pepper, Bassem Salama, Derek Toms, Golsa Razian, Cara E. Ellis, Antonio Bruni, Boris Gala-López, Jia Lü, Heather Vovko, Cecilia Chiu, Shaaban Abdo, Tatsuya Kin, Greg Korbutt, A. M. James Shapiro, Mark Ungrin

Bibliographic record

VenueDiabetologia · 2018
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsAlberta Children's HospitalUniversity of CalgaryUniversity of Alberta
FundersCanadian Institutes of Health ResearchUniversity of Alberta
KeywordsIsletTransplantationRenal capsuleInternal medicineIn vivoEndocrinologyInsulinPancreatic isletsBiologyMedicine

Abstract

fetched live from OpenAlex

Islet transplantation is a treatment option that can help individuals with type 1 diabetes become insulin independent, but inefficient oxygen and nutrient delivery can hamper islet survival and engraftment due to the size of the islets and loss of the native microvasculature. We hypothesised that size-controlled pseudoislets engineered via centrifugal-forced-aggregation (CFA-PI) in a platform we previously developed would compare favourably with native islets, even after taking into account cell loss during the process. Human islets were dissociated and reaggregated into uniform, size-controlled CFA-PI in our microwell system. Their performance was assessed in vitro and in vivo over a range of sizes, and compared with that of unmodified native islets, as well as islet cell clusters formed by a conventional spontaneous aggregation approach (in which dissociated islet cells are cultured on ultra-low-attachment plates). In vitro studies included assays for membrane integrity, apoptosis, glucose-stimulated insulin secretion assay and total DNA content. In vivo efficacy was determined by transplantation under the kidney capsule of streptozotocin-treated Rag1 −/− mice, with non-fasting blood glucose monitoring three times per week and IPGTT at day 60 for glucose response. A recovery nephrectomy, removing the graft, was conducted to confirm efficacy after completing the IPGTT. Architecture and composition were analysed by histological assessment via insulin, glucagon, pancreatic polypeptide, somatostatin, CD31 and von Willebrand factor staining. CFA-PI exhibit markedly increased uniformity over native islets, as well as substantially improved glucose-stimulated insulin secretion (8.8-fold to 11.1-fold, even after taking cell loss into account) and hypoxia tolerance. In vivo, CFA-PI function similarly to (and potentially better than) native islets in reversing hyperglycaemia (55.6% for CFA-PI vs 20.0% for native islets at 500 islet equivalents [IEQ], and 77.8% for CFA-PI vs 55.6% for native islets at 1000 IEQ), and significantly better than spontaneously aggregated control cells (55.6% for CFA-PI vs 0% for spontaneous aggregation at 500 IEQ, and 77.8% CFA-PI vs 33.4% for spontaneous aggregation at 1000 IEQ; p < 0.05). Glucose clearance in the CFA-PI groups was improved over that in the native islet groups (CFA-PI 18.1 mmol/l vs native islets 29.7 mmol/l at 60 min; p < 0.05) to the point where they were comparable with the non-transplanted naive normoglycaemic control mice at a low IEQ of 500 IEQ (17.2 mmol/l at 60 min). The ability to efficiently reformat dissociated islet cells into engineered pseudoislets with improved properties has high potential for both research and therapeutic applications.

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 score0.004

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.013
GPT teacher head0.260
Teacher spread0.247 · 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

Citations54
Published2018
Admission routes2
Has abstractyes

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