Normothermic Ex Vivo Machine Perfusion Mitigates Apoptosis in a Porcine Model of Pancreas Transplantation
Bibliographic record
Abstract
BACKGROUND: Pancreas transplantation is one of the most effective treatment options for individuals diagnosed with complicated diabetes. However, the pancreas has one of the strictest acceptance criteria and the highest discard rate of any organ after retrieval. Normothermic ex vivo perfusion (NEVP) has emerged as a promising strategy to evaluate and potentially improve the quality of pancreatic grafts before transplantation. METHODS: Using a porcine model of pancreas transplantation, we compared 5 h of static cold storage (SCS; n = 4) with 2 h of SCS followed by 3 h of NEVP (n = 4). Parameters such as graft hemodynamics, blood biochemistry, and histopathology were evaluated. The animals were followed up for 3 d after transplantation. RESULTS: A glucose tolerance test performed on day 3 was comparable between the 2 groups ( P = 0.71). The NEVP group exhibited a significantly lower number of terminal deoxynucleotidyl transferase dUTP nick end labeling-positive cells compared with the SCS group ( P = 0.01). Additionally, plasma and tissue levels of 8-hydroxy-2-deoxyguanosine were significantly lower in the NEVP group on postoperative day 3 compared with the SCS group ( P = 0.01). However, within-group comparisons did not show statistically significant changes over time. CONCLUSIONS: This study demonstrates that the addition of NEVP significantly reduces apoptosis after reperfusion and may help stabilize oxidative stress levels. These findings suggest that NEVP could be a valuable approach for improving the quality and viability of pancreatic grafts before transplantation, but further research is needed to confirm.
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 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) | 0.001 | 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".