Establishing and Validating a Biomolecular Signature of Ischemia/Reperfusion Injury in a Porcine Pancreas Allotransplantation Model
Bibliographic record
Abstract
Background. Despite considerable advancement in surgical and immunological management in pancreas transplantation, graft pancreatitis remains a feared complication after pancreas transplantation. Identification of molecular mechanisms of underlying ischemia/reperfusion injury (IRI) in pancreas transplantation could, therefore, pave the path for targeted therapy to improve surgical outcomes. The aim of the study was to identify and validate the genes differentially expressed in the early period (24 h) of graft reperfusion in pancreas transplantation. Methods. A porcine pancreas allotransplant model (n = 4) was used to identify and validate the genes aberrantly expressed in 60 min postreperfusion tissue samples (phase 1). Trends of expression of selected genes from phase 1 and corresponding protein product levels in serum were validated at defined time points for >24 h in a technically replicated external cohort (n = 3; phase 2). Results. A total of 104 genes were found to be upregulated at 60 min after pancreas graft reperfusion. The most consistently overexpressed genes were IL6, THBS1, and MIR-21 (micro-RNA) mapped to protein kinase and intracellular signaling molecular pathways. Levels of expression of these genes correlated significantly with serum interleukin-6 (R = 0.60–0.81; P < 0.01) and tumor necrosis factor-alpha levels (R = 0.34–41; P > 0.05) at corresponding time points. Conclusions. The results provide new insights into biomolecular pathways (THBS1-IL6-MIR-21 crosstalk and hydroxymethylglutarate coenzyme A reductase–linked nuclear factor kappa B activation) linked to pancreatic IRI in porcine transplantation model. Identification and validation of some novel molecular pathway interactions in human pancreas transplantation could pave the path for potential targeted therapy in alleviating graft injury in the early phases of pancreatic IRI.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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".