Impact of donor and prolonged cold ischemia time of neonatal pig pancreas on neonatal pig islet transplant outcome
Bibliographic record
Abstract
BACKGROUND: Genetically modified pigs (GMP) have been developed to alleviate the shortage of donors in human islet transplantation and rejection. In this study, we characterized and compared the islets from GalTKO, GalTKO/hCD46, GalTKO/hCD46/hCD39, and wild-type (WT) neonatal pigs. METHODS: Islets were isolated from GMP and WT pig pancreases that have been packaged with ice pack for at least 24 hours. The difference in gene expression and function of islets were evaluated by microarray analysis and transplantation of islets under the kidney capsule of streptozotocin-induced diabetic immune-deficient mice, respectively. Blood glucose levels of these mice were monitored weekly post-transplantation for >100 days, and islet grafts were collected and evaluated for the presence of endocrine cells. RESULTS: The genes involved in extracellular components, cell adhesion, glucose metabolism, and inflammatory response are differentially expressed between GMP and WT pig islets. Variation in the ability of pig islets in correcting the diabetic state of the mouse recipients appears to be dependent on the pig donor. In addition, prolonged cold ischemia time had a negative effect on the transplant outcome. All normoglycemic mice were able to respond well to glucose challenge despite the initial differences in the ability of islet transplants to reverse their diabetic state. Islet xenografts of normoglycemic mice contained abundant insulin- and glucagon-positive cells. CONCLUSION: The effect of GMP and WT neonatal pig islet transplants on hyperglycemia in mice appears to be dependent on the pig donor, and prolonged cold ischemia time negatively affects the neonatal pig islet transplant outcome.
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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 | 0.001 | 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) | 0.002 | 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".