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425.5: Achieving Localized Immunosuppression Through Ex Vivo Engineering of Organ Blood Vessels

2022· article· en· W4296418304 on OpenAlexaffabout
Daniel Luo, Erika M. J. Siren, Winnie Enns, Lyann Sim, Franklin Tam, Javairia Rahim, Caigan Du, Dicken S.C. Ko, Steve Withers, Jonathan C. Choy, Jayachandran N. Kizhakkedathu

Bibliographic record

VenueTransplantation · 2022
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
Fundersnot available
KeywordsImmunosuppressionEx vivoImmune systemTransplantationEndotheliumIn vivoImmunologyOrgan transplantationMedicineBiologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

Introduction: Classic immunosuppressants lead to systemic immune shutdown, though necessary to mediate transplant rejection, it may lead to various complications. To reduce off-target immunosuppression while retaining increased organ survival, we propose direct modification of the endothelium of vascular transplants ex vivo to achieve localized immunomodulation. The glycocalyx (eGcx), made up of membrane-bound glycoproteins, is of particular interest due to its ability to control cell to cell communication and the activation of immune response through cell recognition. During organ transplantation, inflammation and oxidative damage occurs and can lead to the shedding and damage of the eGcx layer; this has been linked to organ failure and rejection. We developed an enzymatic approach to modify the surface of the endothelium with immunosuppressive polymers to induce immunomodulatory effects locally. We tested the efficacy of this approach in murine transplants.Method: We developed a method using tissue transglutaminase (tTGase) as the surface immobilizing enzyme and polyglycerol polymers containing sialic acid or sulfate moieties that is compatible with UW organ preservation solution at 4°C. In vitro mechanistic studies were performed using EaHy.926 cells to replicate the endothelium and PBMCs and CAR-T cells were used to test immune cytotoxicity. In vivo efficacy of graft rejection was assessed through aortic vessel or renal grafts from BALB/c donor mice into C57BL/6 recipient mice. Polymer treated and untreated grafts were assessed by serology and histology at various timepoints (day 2, 15 and 42 for vessel grafts and day 30 for renal grafts). Results: In vitro, polymer modified endothelial cells were able to evade CAR-T cell induced cytoxicity and reduced oxidative stress. Moreover, polymer treatment reduced TNF release in M1 macrophages. In vivo, modified grafts showed reduced medial thickening and leukocyte infiltration in vessel transplants; further confirmed in the reduction of pro-inflammatory cytokines in serum. In 42 day studies, donor-specific antibody was reduced in polymer-treated grafts compared to untreated. Finally, histological analysis of polymer-treated renal grafts revealed less infiltration and mesangial expansion relating to a healthier graft after 30 days.Conclusion: Here, the use of a polymer-mediated organ engineering approach leads to vascular protection that prevents immune-mediated rejection of organ transplants. Ex-vivo delivery of these immune cloaking polymers that engineer the blood vessel lumen allow for localized immune protection, making this an enticing and viable strategy for the reduction in the use of broad-active immunosuppressants post-transplantation. The protocol remains simple and easy to deliver, thereby enhancing its potential clinical applicability. To further validate this novel approach, studies in larger animal models that more closely replicate human transplant conditions are being planned. Canadian Institutes of Health Research. Natural Sciences and Engineering Council of Canada. Heart and Stroke Foundation of Canada. Canadian Glycomics Network, GlycoNet.

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 score0.006

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.236
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".

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Citations1
Published2022
Admission routes2
Has abstractyes

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