Improving the Pancreatic Islet Graft Structure and Function: Application of a Novel Bioengineered Composite Scaffold
Notice bibliographique
Résumé
Introduction: Transplantation of the insulin-producing islet cells of the pancreas has been a promising strategy for management of type 1 diabetes. However, shortage of islet donors, and poor survival of the islets after transplantation are some of the main challenges of this procedure. Enzymatic digestion of the pancreas in the process of islet isolation results in the loss of peri-insular extracellular matrix (ECM) and peri-vascular basement membrane of the islets that can significantly disrupt the ECM-mediated interactions and result in apoptosis. Naturally a collagen matrix provides for a viable environment in which both stromal and islet cells can survive. However this matrix is mainly biodegradable and subject to gradual disintegration and contraction after transplantation. We have previously shown that a fibroblast populated collagen matrix (FPCM) significantly improves islet cell viability and glucose responsiveness and reduces the marginal islet mass required for hyperglycemia reversal in diabetic mice. Fibroblasts were incorporated to provide a favorable support for islets by producing various growth and angiogenic factors, while maintaining the integrity of the collagen matrix. Although this composite was able to promote the islet graft survival and function, it was still prone to gradual biodegradation. For this reason, we designed a novel bioengineered crosslinked-interpenetrating network of glycosaminoglycan (GAG) and type I collagen (Collagen-GAG matrix, CGM). When populated with fibroblasts (FP-CGM), it would provide an optimal matrix biomimetic with reduced contraction and enhanced mechanical strength for the transplanted islets. Methods: Pancreatic islets and dermal fibroblasts were obtained from Balb/c mice. Islets (50 islets per well in 48-well plates) were cultured in medium (2D), acellular collagen matrix (CM), FPCM, acellular CGM and FP-CGM composites. In order to prepare the FPCM and FP-CGM composites, 200,000 fibroblasts per 100μl of the gel were used. The viability of the islets within composites was assessed by a Live/Dead assay kit on days 1, 15 and 30 post culture. Islets were retrieved on days 1, 15 and 30 post culture and paraffin-embedded sections were stained for insulin, glucagon, caspase-3 and Ki67 and the β-cell proliferation rate was calculated. Retrieved islets were evaluated for glucose-stimulated insulin secretion. Finally, the expression of the islet key specific genes including insulin, PDX1, GLUT2, Pax4, and Pax6 were examined before and after culture at different conditions using reverse transcriptase-PCR analysis. Results: Our preliminary findings have demonstrated that our matrix is non-toxic to both fibroblasts and islets and able to withstand contraction in vitro: Live and Dead assay showed that islets perfectly survive within the new composite matrix. Histology revealed reductions in graft contraction and fibroblast cellularity, increased insulin and glucagon production, and decreased apoptosis rate compared to naked islets in 2D system. Islet morphology remained intact within the composite FP-CGM. PCR analysis showed high expression of islets key genes when islets are embedded within the proposed matrix. Conclusions: We have identified that the use of a crosslinked collagen-GAG graft has the potential to improve graft survival, improving the post-surgical outcome of the transplanted islets.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».