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307.4: Subcutaneous Bioabsorption of Nanofibrous Scaffolds Influence the Engraftment and Function of Neonatal Porcine Islets Xenografts in Mice

2021· article· en· W3217270454 on OpenAlexaff
Purushothaman Kuppan, Sandra Kelly, Karen Seeberger, Chelsea Castro, Mandy Rosko, Andrew R. Pepper, Gregory S. Korbutt

Bibliographic record

VenueTransplantation · 2021
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPLGATransplantationSubcutaneous tissueBiomedical engineeringStreptozotocinChemistryMedicineDiabetes mellitusSurgeryIn vitroEndocrinologyBiochemistry

Abstract

fetched live from OpenAlex

Dr. Korbutt and Dr. Pepper Introduction: The subcutaneous space is currently being pursued as an alternative transplant site for human pancreatic islets, stem cell and xenogeneic derived-islets, due to its retrievability, minimally invasiveness, accommodation of large transplant volumes, and potential for monitoring graft function. However, transplantation of islets into an unmodified subcutaneous niche fails to reverse diabetes due to a lack of adequate blood supply. Biomaterial nanofibrous scaffolds can be functionalized to enhance the host tissues integration, provide a platform for the delivery of pro-engraftment growth factors, and immunomodulatory agents. Method: Herein, we utilize poly (ε-caprolactone) (PCL) and poly (lactic-co-glycolic acid) (PLGA) polymers to make nanofibrous scaffolds and functionalized with bioactive peptides, to prime the subcutaneous space into a more suitable environment. We implanted the nylon catheter (2 cm length, 6 French diameter) (deviceless space, DL), and nanofibrous scaffolds (scaffold was wrapped around the nylon catheter) such as PCL, PCL+RGD+VEGF (PCL+R+V), PCL+RGD+Laminin (PCL+R+L), PLGA and PLGA+Gelatin (PLGA+G) into the subcutaneous space of immunodeficient B6.129S7-Rag1tm1Mom/J mice for four weeks to create a prevascularized space. After 4 weeks, DL and scaffolds implanted mice and those intended for kidney capsule (KC) implantation were rendered diabetic by intraperitoneal injection of 180 mg/kg streptozotocin. Subsequently, neonatal porcine islets (3000 NPI) were transplanted under the KC or within the subcutaneous DL and scaffolds. Graft function was evaluated by monitoring non-fasting blood glucose, stimulated porcine insulin measurement, intraperitoneal glucose tolerance test, and histochemical analysis. Results: Our preliminary scaffolds implantation (no cells) study demonstrates that PCL, PCL+R+V and PCL+R+L scaffolds did not absorb and partially integrated with the host tissues (PCL based scaffolds remain intact at the implanted site) whereas PLGA and PLGA+G scaffolds were completely absorbed and integrated with the host tissues (no remnants of PLGA based scaffolds at the implanted site), and we also observed that there were numerous blood vessels innervated in and around these scaffolds. Compared with peptide functionalized PCL scaffolds, PLGA and PLGA+G fibrous scaffolds with NPI resulted in 86% and 100% euglycemia (*p< 0.05, **p<0.01 respectively), superior glucose clearance (*p<0.05) and greater stimulated porcine insulin secretion (*p<0.05). Moreover, PLGA and PLGA+G scaffolds exhibited comparable graft functions with the positive controls (DL and KC)(p>0.05). Conclusion: Our study demonstrates that PLGA based fibrous scaffolds facilitates the engraftment and function of NPIs in the subcutaneous space of diabetic mice. These collective data emphasize the support of biomaterial implants on cellular graft function in the subcutaneous space for clinical islet xenotransplant applications in a near future.

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 score0.003
Threshold uncertainty score0.008

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.007
GPT teacher head0.222
Teacher spread0.215 · 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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Citations0
Published2021
Admission routes1
Has abstractyes

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