MétaCan
Menu
Back to cohort

P.160: Immune Response to Vascularizing Subcutaneous Engineered Islet Grafts

2021· article· en· W3215828699 on OpenAlexaffabout
Krystal Ortaleza, Sean M. Kinney, So‐Yoon Won, Ilana Talior‐Volodarsky, Michael V. Sefton

Bibliographic record

VenueTransplantation · 2021
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsImmune systemIsletTransplantationImmunologyFlow cytometryImmune toleranceCancer researchMedicineChemistryInternal medicineEndocrinologyDiabetes mellitus

Abstract

fetched live from OpenAlex

Introduction: The subcutaneous space is a promising islet transplant site as it has the necessary large transplant volume and largely avoids the blood-mediated inflammatory response, but it requires further vascularization. Moreover, the accessibility of this site allows for monitoring and retrieval of the transplant. Our lab’s methacrylic acid (MAA)-containing biomaterials induce vascularization, in part by polarizing macrophages to a pro-regenerative “M2” type state. We have shown that rodent islets injected in an MAA-poly(ethylene glycol) (MAA-PEG) hydrogel, survive and engraft in the subcutaneous space of immune compromised SCID/Bg mice. It is expected that the modulation of host macrophages plays an important role in subcutaneous islet engraftment, through an effect on vessel formation and IGF1 signaling, but the extent to which this polarization affects the local immune environment is still unknown. We expect that the unique immune environment of the skin together with MAA-modulated vascularization will affect the efficacy of peri-operative immunosuppressants commonly used for allogeneic transplants. In this work we aim to understand the immune environment of subcutaneously implanted, vascularizing islet allografts to allow for the development of a targeted short-term immune mitigation strategy. Methods: 200 mouse islet equivalents (IEQ) were isolated from C57Bl/6J mice and injected in 100 µl of MAA-PEG hydrogel into Balb/c or SCID/bg mice. Islet grafts were removed on days 1, 3, and 7 for analysis of the immune environment by flow cytometry or qPCR. Results: Granulocyte and monocyte recruitment differed between allografts in BALB/c mice and an immunocompromised SCID/bg “immunological control” at day 1. Of note was a significant difference in an apparent granulocyte myeloid derived suppressor cell (MDSC) population (CD11b+CD11c-Ly6G+Ly6Clow-med F4/80-SSClow-med). Differences in IFNy, but not TNFα or TGFβ, expression were also observed. Discussion/Conclusion: We have identified differences in innate cell recruitment between immune competent and incompetent models, particularly the MDSC populations. MDSCs are key players in allograft acceptance because of their mediation of vascularization-associated inflammation. A better understanding of this difference will help determine if these cells are required for proper islet engraftment in a vascularized subcutaneous site. Characterization of the implant also revealed an upregulation of IFN-γ expression. This upregulation highlights that local macrophages or natural killer cells may have been activated by the graft. Work is ongoing to further explore these results and evaluate the efficacy of common immunosuppressants on the response to a subcutaneous allograft. A greater understanding of the immune response to a vascularizing, subcutaneous islet graft will allow us to better develop strategies for subcutaneous islet transplantation as a regenerative treatment option for T1D.CFREF/Medicine by Design. Canadian Institutes of Health Research.

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 score0.004
Threshold uncertainty score0.012

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)0.0040.001

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 head0.006
GPT teacher head0.232
Teacher spread0.226 · 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
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueTransplantationSame topicElectrospun Nanofibers in Biomedical ApplicationsFrench-language works237,207