Protection of Porcine Islet Xenografts in Mice Using Sertoli Cells and Monoclonal Antibodies
Bibliographic record
Abstract
BACKGROUND: To remedy the shortage of human donor islets, xenotransplantation of neonatal porcine islets (NPI) provides an attractive alternative source of donor tissue so long as graft rejection can be circumvented. Thus, in this study, we sought to determine whether cotransplantation of NPI with Sertoli cells (SC) combined with a short-course treatment of monoclonal antibody (mAb) could provide long-term islet xenograft survival. METHODS: NPI alone or NPI cotransplanted with neonatal porcine SC were transplanted into diabetic C57BL/6 mice. These mice were left untreated or were treated with a short course of antileukocyte function associated antigen-1 (LFA-1), anti-CD154, or anti-CD45RB mAb. Blood glucose levels were monitored twice a week to assess graft function. At more than 100 days posttransplantation or on the day of rejection, graft-bearing kidneys were collected for characterization using immunohistochemistry. RESULTS: None of the untreated control mice transplanted with NPI alone (0/5) or NPI cotransplanted with SC (0/8) achieved normoglycemia. However, of the mice receiving NPI alone, 3 of 7 treated with anti-LFA-1 mAb, 2 of 7 treated with anti-CD154 mAb, and 1 of 7 treated with anti-CD45RB mAb achieved long-term graft survival (>100 days). These proportions improved considerably when NPI were cotransplanted with SC, as 15 of 15 mice treated with anti-LFA-1 mAb, 7 of 8 mice treated with anti-CD154 mAb, and 4 of 9 mice treated with anti-CD45RB mAb achieved long-term graft survival. CONCLUSIONS: These results show that transient administration of anti-LFA-1 mAb or anti-CD154 mAb is efficacious in prolonging NPI xenograft survival when islets are cotransplanted with SC. Interleukin-4 and Serpina3n may be important mediators of protection observed in this model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".