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Record W7133009631

Methacrylic Acid-Based Biomaterials for Subcutaneous Pancreatic Islet Transplantation

2024· dissertation· W7133009631 on OpenAlexaff
Krystal Ortaleza

Bibliographic record

VenueTSpace · 2024
Typedissertation
Language
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsImmune systemIsletTransplantationPancreatic isletsIslet cell transplantationDownregulation and upregulationDiabetes mellitus
DOInot available

Abstract

fetched live from OpenAlex

Type 1 diabetes (T1D) is an autoimmune disease that results in the inability to control blood glucose levels, leading to hyperglycemia. Although currently managed by insulin injections, tighter regulation is required. An alternative treatment method is pancreatic islet transplantation. The subcutaneous space offers a promising site for islet transplantation as it is non-invasive and offers retrievability of the graft, however, this site requires external vascularization to support the metabolically demanding cells. Methacrylic acid (MAA) based biomaterials have proven to be an effective method for vascularizing the subcutaneous space and making it a suitable site for transplantation.Here we explore the use of MAA-based biomaterials for islet transplantation and evaluate its effect on the immune and oxygen environment of the subcutaneous space. We evaluated the ability of MAA-induced vascularization to support the viability of alginate-poly-l-lysine-alginate (APA) microencapsulated islets. Despite the lack of direct vessel integration, MAA was able to support microencapsulated islet function in the subcutaneous space resulting in a reversal of hyperglycemia in an immune compromised model. This offers a potential immune mitigation strategy for islet transplantation. To transition the MAA hydrogel to an allogeneic model, we also focused on how the immune cell niche is impacted. The delivery of MAA compared to PEG, resulted in an increased cellular response comprised primarily of neutrophils in an immune competent model. There was also a significant upregulation of Ifn-γ in Balb/c compared to SCID/bg with the delivery of MAA, pointing to a more pro-inflammatory cellular environment. The use of a neutrophil depleting antibody showed promise in addressing this acute response and promoting long-term engraftment of islets. We also explored the effect of MAA on the oxygen environment in the subcutaneous space. We highlighted the importance of hydrogel volume and cell density on islet engraftment and the reversal of hyperglycemia in a diabetic immune compromised mouse. Based on the Krogh Cylinder model which predicts oxygen tension within a cylindrical tissue, more vessels are required for hydrogel transplants of higher cell density to compensate for the increased oxygen demand. In this work we identified that a volume fraction of 0.005 is required for islet function based on vessel density and oxygen tension measurements. This offers a starting point for the scale up of the MAA hydrogel system: a larger hydrogel volume is required to support an increased islet dose in larger animals. Subcutaneous islet transplantation may offer a functional cure for Type 1 diabetes. In this work we demonstrate the potential and translatability of the MAA-based hydrogel as an islet delivery platform.

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)0.0010.000

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 head0.025
GPT teacher head0.347
Teacher spread0.322 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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