Orthotopic liver transplantation is an established treatment for end stage liver diseases as well as for some severe metabolic disorders. With increasing number of patients on the waiting list and the ongoing shortage of livers available, domino liver transplantation (DLT) became an option to further expand the organ donor pool. DLT utilizes the explanted liver of one liver transplant recipient as a donor graft in another patient. Despite being a surgically, and logistically demanding procedure, excellent results could be achieved in experienced high-volume transplant centers. In this review we present the current world status of DLT.

Introduction: Pancreas transplantation is currently the only treatment that can re-establish normal endocrine pancreatic function. Despite all efforts, pancreas allograft survival and rejection remain major clinical problems. The purpose of this study was to identify features that could signal patients at risk of pancreas allograft rejection. Methods: We collected 74 features from 79 patients who underwent simultaneous pancreas–kidney transplantation (SPK) and used two widely-applicable classification methods, the Naive Bayesian Classifier and Support Vector Machine, to build predictive models. We used the area under the receiver operating characteristic curve and classification accuracy to evaluate the predictive performance via leave-one-out cross-validation. Results: Rejection events were identified in 13 SPK patients (17.8%). In feature selection approach, it was possible to identify 10 features, namely: previous treatment for diabetes mellitus with long-term Insulin (U/I/day), type of dialysis (peritoneal dialysis, hemodialysis, or pre-emptive), de novo DSA, vPRA_Pre-Transplant (%), donor blood glucose, pancreas donor risk index (pDRI), recipient height, dialysis time (days), warm ischemia (minutes), recipient of intensive care (days). The results showed that the Naive Bayes and Support Vector Machine classifiers prediction performed very well, with an AUROC and classification accuracy of 0.97 and 0.87, respectively, in the first model and 0.96 and 0.94 in the second model. Conclusion: Our results indicated that it is feasible to develop successful classifiers for the prediction of graft rejection. The Naive Bayesian generated nomogram can be used for rejection probability prediction, thus supporting clinical decision making.

Background: Simultaneous pancreas–kidney (SPK) transplantation can restore renal function and insulin independence, but non-technical pancreas graft dysfunction remains difficult to anticipate. Methods: We conducted an exploratory single-centre retrospective biomarker-modelling study to determine whether day-zero recipient serum Fourier-transform infrared (FTIR) spectra are associated with subsequent loss of insulin independence after SPK transplantation. Results: Among 104 screened recipients, 51 met predefined sample-availability, spectral-quality, data-linkage and endpoint-adjudication criteria; 30 maintained pancreas graft function and 21 developed dysfunction. Cases dominated by early technical surgical failure were excluded. Clinical-only, FTIR-only and FTIR–clinical Naïve Bayes models were evaluated using leave-one-out cross-validation with Fast Correlation-Based Filter feature selection. In locked-feature internal validation, the best FTIR-only model used second-derivative spectra with vector normalization and nine selected wavenumbers, achieving AUC 0.997 (95% CI 0.985–1.000) and accuracy 0.961 (95% CI 0.902–1.000). A fixed-feature permutation analysis exceeded label-randomized performance (empirical p = 0.001). The secondary Group 1 versus Group 3 analysis suggested discrimination of pancreas dysfunction despite preserved kidney function (AUC 0.992; accuracy 0.930). Conclusions: Given the small cohort, high-dimensional input, non-nested feature selection, selection-bias risk and absence of external validation, serum FTIR should be considered a candidate risk-enrichment platform requiring prospective multicentre validation.

Background/Objectives: Early transient endocrine dysfunction after simultaneous pancreas-kidney transplantation (SPK) frequently triggers urgent investigations to exclude thrombosis, pancreatitis, or rejection, yet many recipients recover during the index admission. We tested whether pre-transplant day zero (D0) serum Fourier-transform infrared spectroscopy (FTIRS) captures a biochemical fingerprint associated with a Start&Stop trajectory (initial insulin independence followed by transient dysfunction with recovery). Methods: In a single-center retrospective case-control study nested within 104 consecutive SPK recipients with available D0 serum, 12 Start&Stop cases were matched 1:1 to 12 No-Stop controls. Serum FTIR spectra went through structured quality control and standardized preprocessing. A Naïve Bayes classifier with Fast Correlation-Based Filter (FCBF) feature selection was evaluated using leave-one-out cross-validation (LOOCV) and label-permutation analysis. Results: Under LOOCV, the primary FTIRS model (Savitzky-Golay second derivative; 600–900 and 2800–3400 cm⁻¹) achieved excellent discrimination (ROC-AUC 1.00) with accuracy 0.958 and F1 score 0.958. Discrimination collapsed under label permutation (ROC-AUC 0.461), supporting a non-random label-spectrum association. Discriminant information mapped mainly to carbohydrate/glycoprotein-associated bands (~946–1161 cm⁻¹), protein structural contributions near the amide III region (~1300 cm⁻¹), and lipid/protein stretching modes (~2865–3163 cm⁻¹), consistent with a multicomponent systemic biochemical state. Conclusions: In this exploratory matched case-control cohort, pre-transplant D0 serum FTIRS signatures were associated with the subsequent Start&Stop phenotype after SPK. These findings should be interpreted as recipient-side exploratory risk-stratification signals rather than clinically actionable decision tools. Larger multicenter validation in unselected cohorts, with standardized endpoint adjudication, preanalytical control, fully nested model development and inter-instrument harmonization, is required before clinical implementation or population-level risk calibration.