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307.2: Bioprinted Immune-protective Islet-containing Tissues Successfully Regulate Blood Glucose in Rodent Models of Type 1 Diabetes

2021· article· en· W3217712568 on OpenAlexaff
Valerio Russo, Reza B. Jalili, Sheng‐Wei Pan, Navid Hakimi, Rishima Agarwal, Yang Yu, Kamal Hussain Khan, Kaushar Jahan, Shogo Ida, Emily M. Wilts, Cara E. Ellis, Priye Iworima, Elisa Tran, Mei Tang, Timothy J. Kieffer, Simon Beyer, Eric E. Roos, Tamer Mohamed, Spiro Getsios, Sam Wadsworth

Bibliographic record

VenueTransplantation · 2021
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsImmune systemIsletTransplantationPancreatic isletsStreptozotocinCell therapyEmbryonic stem cellViability assayInsulinCell encapsulationCellCell biologyBiologyChemistryImmunologyMedicineDiabetes mellitusStem cellInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Introduction: Type 1 diabetes (T1D) is a disease characterized by elevated blood glucose due to insufficient insulin release from pancreatic β-cells. Transplantation of cadaveric islets demonstrates that cell therapy can fully reverse hyperglycemia. However, limited cell supply, immune rejection of implanted allogeneic cells, and cell survival represent major challenges. Cell encapsulation has great potential to overcome these challenges by blocking immune cell access to the grafts while allowing nutrient exchange and secreted products from the implanted cells to be delivered to the body. In this study, we use a unique microfluidic bioprinting technology to precisely control the placement of cells and biomaterials within 3D tissues with micro-architectures optimized for cellular fitness and immune protection. Methods: Living tissues consisting of fibres with a cell-containing core and immune-protective alginate-based shell were generated using Aspect Biosystems’ RX1 bioprinter technology. Core-shell fibres with reaggregated primary human pancreatic islets or embryonic stem cell-derived β-cells were tested in vitro using viability and functional (glucose-stimulated insulin secretion, GSIS) assays. Bioprinted tissues were then implanted into the IP space or omentum of streptozotocin (STZ)-induced diabetic mice and rats, respectively. Glucose homeostasis, body weight, and human C-peptide secretion were monitored for up to 3 months following implantion. Retrieved grafts were fixed and analyzed by histology (H&E, Masson’s trichrome stain) and immunohistochemistry (α-SMA, CD45) to quantify fibrotic encapsulation and immune cell infiltration. Results: Bioprinted tissues supported viability and dynamic insulin secretion of cells in vitro up to 28 days. When transplanted into immunodeficient and immunocompetent diabetic rodents, bioprinted tissues containing reaggregated human islets successfully regulated blood glucose for up to 3 months (Figure 1), although normoglycemia was only sustained in a subset of immunocompetent animals and associated with variable fibrosis. Post-retrieval viability stain, assessment of GSIS, and histology revealed high viability and functionality of implanted cells, and the absence of leukocyte infiltration through the shell. Discussion: This is the first study showing a fully 3D bioprinted tissue composed of a core/shell fibre can successfully deliver a therapeutic dose of xenogeneic cells into a diabetic animal (Figure 2). The unique features of the microfluidic technology were leveraged to bioprint an implantable and retrievable tissue patch that merges the benefits of a single fibre (cell fitness, access to nutrients, immune-protection, perm-selectivity) with those provided by a 3D structure (retrievability, ease of implant, structural integrity). The promising results obtained in rodent studies warrant further investigation to minimize fibrosis and explore the scaling-up of bioprinted tissues in large animal models of T1D.NRC-IRAP. NSERC. Stem Cell Network. Genome BC.

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 score0.002
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.252
Teacher spread0.235 · 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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Citations1
Published2021
Admission routes1
Has abstractyes

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