Neutrophil depletion for early allogeneic islet survival in a methacrylic acid (MAA) copolymer-induced, vascularized subcutaneous space
Bibliographic record
Abstract
Islet transplantation is a promising treatment for type I diabetes (T1D). Despite the high loss of islets during transplantation, current islet transplant protocols continue to rely on portal vein infusion and intrahepatic engraftment. Because of the risk of portal vein thrombosis and the loss of islets to instant blood mediated inflammatory reaction (IBMIR), other transplantation sites like the subcutaneous space have been pursued for its large transplant volume, accessibility, and amenability for retrieval. To overcome the minimal vasculature of the subcutaneous space, prevascularization approaches or vascularizing biomaterials have been used to subcutaneously deliver islets into diabetic mice to return them to normoglycemia. Previous vascularization methods have relied on a 4 to 6 week prevascularization timeframe. Here we show that a vascularizing MAA-coated silicone tube can generate sufficient vasculature in 2 to 3 weeks to support a therapeutic dose of islets in mice. In order to fully harness the potential of this prevascularized site, we characterize the unique, subcutaneous immune response to allogeneic islets in the first 7 days following transplantation, a critical stage in successful engraftment. We identify neutrophils as a specific cellular target, a previously overlooked cell in the context of subcutaneous allogeneic islet transplantation. By perioperatively depleting neutrophils, we show that neutrophils are a key, innate immune cell target for successful early engraftment of allogeneic islets in a prevascularized subcutaneous site.
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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 | 0.000 |
| 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 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".