Long-term survival and induction of operational tolerance to murine islet allografts through the co-transplantation of cyclosporine A eluting microparticles
Bibliographic record
Abstract
Abstract One strategy to prevent islet rejection, is to create a favorable immune-protective local environment at the transplant site. Herein, we utilize localized cyclosporine A (CsA) delivery to islet grafts via poly(lactic-co-glycolic acid) (PLGA) microparticles to attenuate allograft rejection. CsA microparticles alone significantly delayed islet allograft rejection compared to islets alone (p<0.05). Over 50% (6/11) of recipients receiving CsA microparticles and short-term cytotoxic T lymphocyte-associated antigen 4-Ig (CTLA4-Ig) therapy displayed prolonged allograft survival for 214 days, compared to 25% (2/8) receiving CTLA4-Ig alone (p>0.05). CsA microparticles + CTLA4-Ig islet allografts exhibited reduced T-cell (CD4 + and CD8 + cells) and macrophage (CD68 + cells) infiltration compared to islets alone. We observed reduced mRNA expression of proinflammatory cytokines (IL-6, IL-10, INF-γ & TNF-α; p<0.05) and chemokines (CCL2, CCL5, CCL22, and CXCL10; p<0.05) in CsA microparticles + CTLA4-Ig allografts compared to islets alone. Long-term islet allografts contained insulin + and intra-graft FoxP3 + T regulatory cells. Rapid rejection of third-party skin grafts (C3H) in islet allograft recipients suggested that CsA microparticles + CTLA4-Ig therapy induced donor specific operational tolerance. This study demonstrates that localized CsA drug delivery plus short-course systemic immunosuppression promotes an immune protective transplant niche for allogeneic islets. Article Highlights Systemic immunosuppression limits patient inclusion for beta cell replacement therapies Localized islet graft immunosuppression may reduce drug toxicity and improve graft survival Cyclosporine eluting microparticles + CTLA4-Ig therapy induced donor specific operational tolerance Graft localized drug delivery can create an immune protective transplant niche
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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) | 0.000 | 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".