Background/Objectives: Despite the long-standing history of liver transplantation (LT) in Spain, no multicenter study has reviewed national outcomes for LT in metastatic neuroendocrine tumors (NETs). In the current era of transplant oncology, auditing these results is essential to refine patient selection and improve long-term outcomes. Methods: This retrospective observational study analyzed data from 13 centers, including 91 patients who underwent LT for NET between 1995 and 2024. Patients were stratified into two groups: Milan IN (those meeting the Milan criteria) and Milan OUT (the remainder). Results: Recurrence occurred in 57.1% of cases, and overall mortality was 51.6%. Of the 91 patients, 71 (78.0%) were Milan IN and 20 (22.0%) were Milan OUT. Five-year overall survival was 71.0% in Milan IN and 58.0% in Milan OUT, with a statistically significant difference. The 5-year disease-free survival (DFS) rate was 58.8% in Milan IN and 36.3% in Milan OUT; this difference was not statistically significant. Conclusions: In conclusion, strict adherence to Milan criteria and incorporation of modern prognostic factors are critical to optimize long-term survival in LT for NET. While the overall outcomes in this historical cohort are modest, future improvements are expected through more rigorous selection and the potential use of bridging or downstaging therapies.

This study aimed to analyze next-generation 3D modeling software utility for personalized surgery in the field of liver transplantation. A retrospective cross-sectional pilot study was conducted at two referral centers including cases of liver transplantation-related complications – either preoperative portal vein thrombosis (PVT) or postoperative portal, arterial or biliary strictures – for which high-quality radiologic imaging was available. 3D model diagnostic accuracy was evaluated on arterial complications comparing the thrombosis/stenosis extension described by the virtual model and the actual site of the new arterial anastomosis at retransplantation (gold standard). In addition, the perceived benefit of 3D reconstructions was assessed through a questionnaire submitted to the experienced surgeons of the team (Likert scale, 1 to 5). Thirty-nine patients were included in the study population: 17 with arterial thrombosis/stenosis, 11 with portal vein thrombosis and 11 with biliary strictures diagnoses. The concordance rate between 3D models’ virtual reproduction and intraoperative findings evaluated for arterial complications was 88.2

Background. Several scores have been developed to stratify the risk of graft loss in controlled donation after circulatory death (cDCD). However, their performance is unsatisfactory in the Spanish population, where most cDCD livers are recovered using normothermic regional perfusion (NRP). Consequently, we explored the role of different machine learning-based classifiers as predictive models for graft survival. A risk stratification score integrated with the model of end-stage liver disease score in a donor-recipient (D-R) matching system was developed. Methods. This retrospective multicenter cohort study used 539 D-R pairs of cDCD livers recovered with NRP, including 20 donor, recipient, and NRP variables. The following machine learning-based classifiers were evaluated: logistic regression, ridge classifier, support vector classifier, multilayer perceptron, and random forest. The endpoints were the 3- and 12-mo graft survival rates. A 3- and 12-mo risk score was developed using the best model obtained. Results. Logistic regression yielded the best performance at 3 mo (area under the receiver operating characteristic curve = 0.82) and 12 mo (area under the receiver operating characteristic curve = 0.83). A D-R matching system was proposed on the basis of the current model of end-stage liver disease score and cDCD-NRP risk score. Conclusions. The satisfactory performance of the proposed score within the study population suggests a significant potential to support liver allocation in cDCD-NRP grafts. External validation is challenging, but this methodology may be explored in other regions.

Liver transplantation (LT) is a life-saving treatment for end-stage liver disease, but long-term immunosuppression (IS) is associated with significant side effects. Achieving operational tolerance (OT), where the graft is accepted without IS, remains a critical goal. Biomarkers play a pivotal role in understanding the complex mechanisms of OT, enabling personalized treatment strategies and improving patient outcomes. Additionally, machine learning techniques offer powerful tools for identifying predictive biomarkers and optimizing IS withdrawal protocols. This multicenter trial aimed to investigate the longitudinal evolution of genetic biomarkers during IS withdrawal and validate their predictive value for OT in LT recipients. A prospective, multicenter IS withdrawal trial was conducted with 91 LT patients. Tolerant (TOL) and non-tolerant (non-TOL) patients were compared, and longitudinal blood and liver samples were collected to analyze biomarkers. Generalized Additive Mixed Models (GAMMs) and logistic algorithms were employed to assess biomarker associations and predict OT. Of the 45 patients who completed the trial, 17 (37.8%) achieved OT. Molecular biomarker analysis revealed significant differences between TOL and non-TOL groups. Non-TOL patients exhibited higher baseline methylation of the FOXP3 regulatory T cell-specific demethylated region (TSDR) in whole blood. Longitudinal analysis showed distinct patterns in FOXP3, SENP6, miR31, and miR95 expression between groups. Notably, FOXP3 expression followed a U-shaped trajectory in TOL patients, decreasing during IS withdrawal and increasing post-withdrawal. Machine learning identified several key predictive biomarkers for OT. This study confirms the association between FOXP3 TSDR methylation and OT in LT patients and identifies FEM1C, miR31 and TFRC as promising predictive biomarkers. These findings highlight the potential for personalized IS withdrawal strategies, though further validation in larger cohorts is needed before clinical application.

Liver transplantation is commonly used for end-stage liver disease, but the demand for organs exceeds the supply, leading to the use of expanded criteria donors (ECDs). Organs from ECDs, especially from donors after circulatory death (DCD), encounter challenges like increased ischemia damage. Biomarkers, especially oxidative stress markers, may provide valuable insights for understanding and monitoring post-transplant events. Here, we highlight the unique value of organ preservation solution (OPS) as a non-invasive and early source of redox biomarkers, directly reflecting graft status during critical cold storage. This study investigated oxidative stress in 74 donated livers using OPS samples collected after cold storage, and also liver biopsies obtained before and after storage. We measured lipid peroxidation, protein carbonylation, DNA oxidation, and total antioxidant capacity from OPS, and performed gene expression analysis of liver biopsies. Oxidative stress markers differed based on donation type, with higher lipid peroxidation in DCD samples compared with donation after brain death (18.51 ± 2.77 vs. 11.03 ± 1.31 nmoles malondialdehyde (MDA)/mg protein; p = 0.049). Likewise, oxidative damage markers were associated with clinical outcomes: lipid peroxidation was increased in patients who developed biliary complications (21.86 ± 5.91 vs. 11.97 ± 1.12 nmol MDA/mg protein; p = 0.05), and protein carbonylation was elevated in those experiencing acute rejection (199.6 ± 22.02 vs. 141.6 ± 15.94 nmol carbonyl/mg protein; p = 0.005). Moreover, higher protein carbonylation levels showed a trend toward reduced survival (p = 0.091). Transcriptomic analysis revealed overexpression of genes associated with reactive oxygen species production in DCD livers. A predictive model for acute rejection integrating OPS biomarkers with clinical variables achieved 83% accuracy. Hence, this study underscores the importance of assessing oxidative stress status in preservation fluid as a biomarker for evaluating liver transplant outcomes and highlights the need for validation in larger, independent cohorts.

Cell transplantation is often performed with ultrasonographic guidance for accurate delivery through injection. In such procedures, using ultrasonographic contrast greatly improves target delivery. However, accumulating evidence suggests that exposure to such contrast agents may have negative effects on transplanted cells. No study so far has researched this issue. Stabilized sulfur hexafluoride (SF6) microbubbles are a widely used sonographic contrast agent. Skin hCD55 porcine transgenic fibroblasts and mesenchymal stem cells from human bone marrow (hMSCs) were exposed in vitro to SF6 in concentrations ranging from 1.54 µM to 308 µM. The effects on viability and cell growth were registered using an impedance-based label-free Real-Time Cell Analyzer (RTCA). Data was recorded every 15 min for 50 h of total study time. Both cell lines behave distinctly when exposed to SF6. Porcine fibroblast growth showed relevant alterations only when exposed to higher concentrations. In contrast, hMSCs showed progressive growth decrease in relation to SF6 concentration. Taken together, while SF6-based contrast agents pose no threat to patient safety, our results indicate that exposure of suspended stem cells to the contrast agent could affect the effective dose administered in cell therapy procedures. This prompts specific cell lineage testing, adjusting methods and properly compensating for cell loss, with a potential impact on procedural cost and success rates.

BACKGROUND:Liver transplantation (LT) remains hampered by post-transplant complications. While gut microbiota dysbiosis has been linked to transplant outcomes, the role of the intrahepatic graft's native microbiota remains unexplored. OBJECTIVE:To characterise the microbial profile detected in organ preservation solution (OPS) and determine whether specific microbial taxa are associated with short-term clinical outcomes, and to develop predictive models for risk stratification. DESIGN:We analysed the OPS microbiota-based metataxonomic signature from 110 LT donors (discovery cohort) and an independent validation cohort (n=29) using 16S rRNA sequencing. Microbial DNA signatures associated with clinical outcomes were identified through MaAsLin2-adjusted models, and relevant gene pathways were uncovered via data mining and enrichment analysis. Machine learning (ML) models were developed to predict outcomes based on microbial features, and host-microbiome interactions were validated through RNA sequencing (RNA-seq of matched liver biopsies). RESULTS:OPS-derived microbial DNA signature closely resembled liver/bile microbiomes (Proteobacteria-dominated). Specific genera (eg, Bacillus, Prevotella) were differentially abundant in adverse outcomes (p<0.05): hyperabundant in non-survivors and hepatic artery thrombosis, hypoabundant in acute rejection (AR). Gene mining linked these taxa to immune/metabolic pathways relevant to LT outcomes. RNA-seq validated upregulation of chemokines (CCL/CXCL families) in liver grafts from non-surviving recipients. ML models accurately predicted global survival (area under the curve (AUC)=0.95) and AR (AUC=0.96) based on microbial features, with generalisability confirmed in the validation cohort (AUC=0.85-0.88). CONCLUSION:Donor intrahepatic microbial DNA signature predicts LT outcomes via immune-metabolic modulation. While causality requires further study, these findings position the graft microbiome as a novel biomarker and potential therapeutic target, paving the way for microbiome-informed precision care in transplantation.