Xenotransplantation of tannic acid‐encapsulated neonatal porcine islets decreases proinflammatory innate immune responses
Bibliographic record
Abstract
Abstract Background Islet transplantation with neonatal porcine islets (NPIs) is a promising treatment for type 1 diabetes (T1D), but immune rejection poses a major hurdle for clinical use. Innate immune‐derived reactive oxygen species (ROS) synthesis can facilitate islet xenograft destruction and enhance adaptive immune responses. Methods To suppress ROS‐mediated xenograft destruction, we utilized nanothin encapsulation materials composed of multilayers of tannic acid (TA), an antioxidant, and a neutral polymer, poly(N‐vinylpyrrolidone) (PVPON). We hypothesized that (PVPON/TA)‐encapsulated NPIs will maintain euglycemia and dampen proinflammatory innate immune responses following xenotransplantation. Results (PVPON/TA)‐encapsulated NPIs were viable and glucose‐responsive similar to non‐encapsulated NPIs. Transplantation of (PVPON/TA)‐encapsulated NPIs into hyperglycemic C57BL/6.Rag or NOD.Rag mice restored euglycemia, exhibited glucose tolerance, and maintained islet‐specific transcription factor levels similar to non‐encapsulated NPIs. Gene expression analysis of (PVPON/TA)‐encapsulated grafts post‐transplantation displayed reduced proinflammatory Ccl5, Cxcl10, Tnf, and Stat1 while enhancing alternatively activated macrophage Retnla, Arg1, and Stat6 mRNA accumulation compared with controls. Flow cytometry analysis demonstrated significantly reduced innate immune infiltration, MHC‐II, co‐stimulatory molecule, and TNF expression with concomitant increases in arginase‐1+ macrophages and dendritic cells. Similar alterations in immune responses were observed following xenotransplantation into immunocompetent NOD mice. Conclusion Our data suggest that (PVPON/TA) encapsulation of NPIs is an effective strategy to decrease inflammatory innate immune signals involved in NPI xenograft responses through STAT1/6 modulation without compromising islet function.
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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".