Bioabsorption of Subcutaneous Nanofibrous Scaffolds Influences the Engraftment and Function of Neonatal Porcine Islets
Bibliographic record
Abstract
The subcutaneous space is currently being pursued as an alternative transplant site for ß-cell replacement therapies due to its retrievability, minimally invasive procedure and potential for graft imaging. However, implantation of ß-cells into an unmodified subcutaneous niche fails to reverse diabetes due to a lack of adequate blood supply. Herein, poly (ε-caprolactone) (PCL) and poly (lactic-co-glycolic acid) (PLGA) polymers were used to make scaffolds and were functionalized with peptides (RGD (Arginine-glycine-aspartate), VEGF (Vascular endothelial growth factor), laminin) or gelatin to augment engraftment. PCL, PCL + RGD + VEGF (PCL + R + V), PCL + RGD + Laminin (PCL + R + L), PLGA and PLGA + Gelatin (PLGA + G) scaffolds were implanted into the subcutaneous space of immunodeficient Rag mice. After four weeks, neonatal porcine islets (NPIs) were transplanted within the lumen of the scaffolds or under the kidney capsule (KC). Graft function was evaluated by blood glucose, serum porcine insulin, glucose tolerance tests, graft cellular insulin content and histologically. PLGA and PLGA + G scaffold recipients achieved significantly superior euglycemia rates (86% and 100%, respectively) compared to PCL scaffold recipients (0% euglycemic) (* p < 0.05, ** p < 0.01, respectively). PLGA scaffolds exhibited superior glucose tolerance (* p < 0.05) and serum porcine insulin secretion (* p < 0.05) compared to PCL scaffolds. Functionalized PLGA + G scaffold recipients exhibited higher total cellular insulin contents compared to PLGA-only recipients (* p < 0.05). This study demonstrates that the bioabsorption of PLGA-based fibrous scaffolds is a key factor that facilitates the function of NPIs transplanted subcutaneously in diabetic mice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".