A scalable device-less biomaterial approach for subcutaneous islet transplantation
Bibliographic record
Abstract
Abstract The subcutaneous space has been shown to be a suitable site for islet transplantation, however an abundance of islets is required to achieve normoglycemia, often requiring multiple donors. The loss of islets is due to the hypoxic conditions islets experience during revascularization, resulting in apoptosis. Therefore, to reduce the therapeutic dosage required to achieve normoglycemia, pre-vascularization of the subcutaneous space has been pursued. In this study, we highlight a biomaterial-based approach using a methacrylic acid copolymer coating to generate a robust pre-vascularized subcutaneous cavity for islet transplantation. We also devised a simple, but not-trivial, procedure for filling the cavity with an islet suspension in collagen. We show that the pre-vascularized site can support a marginal mass of islets to rapidly return streptozotocin-induced diabetic SCID/bg mice to normoglycemia. Furthermore, immunocompetent Sprague Daley rats remained normoglycemia for up to 70 days until they experienced graft destabilization as they outgrew their implants. This work highlights methacrylic acid-based biomaterials as a suitable pre-vascularization strategy for the subcutaneous space that is scalable and doesn’t require exogenous cells or growth factors. Summary In this study methacrylic acid copolymer coated tubes generated a robust vascular response in the subcutaneous space, which was critical to support islet transplantation in a streptozotocin-induced diabetic mouse model. More importantly, the subcutaneous pre-vascularization approach using this copolymer coating was scalable into a larger allogeneic rat model and returned animals to normoglycemia for up to 70 days. This platform highlights the potential of a scalable biomaterial approach for pre-vascularization of the subcutaneous space in larger animal models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".