Ischemia‐reperfusion injury (IRI) is the leading cause of hepatic graft dysfunction, resulting from hepatocyte damage. Nevertheless, given the few specialized therapeutics available in hepatic IRI, additional mechanistic insights into hepatocyte damage are required. Here, the protein solute carrier family 39 member 14 (SLC39A14) is identified as a pro‐ferroptosis target in hepatocytes of human liver allografts through single‐cell RNA sequencing analysis. SLC39A14 knockdown significantly mitigated hepatic IRI by preventing hepatocyte ferroptosis in vivo and in vitro. Mechanistically, the inhibition of SLC39A14 suppressed non‐transferrin‐bound iron (NTBI) uptake by hepatocytes, thereby reducing iron overload and cell ferroptosis. Moreover, human bone marrow‐derived mesenchymal stem cells (hBMSCs) are found to exhibit a notable therapeutic effect on hepatic IRI by downregulating SLC39A14 expression. Exosomes derived from hBMSCs delivered abundant miR‐16‐5p into hepatocytes, which post‐transcriptionally suppressed the expression of SLC39A14 and reduced cell ferroptosis induced by hepatic IRI. In conclusion, SLC39A14 triggers hepatic IRI by mediating NTBI uptake into hepatocytes and inducing hepatocyte ferroptosis. Moreover, hBMSC‐based therapy is promising to reverse this progression of hepatic IRI.

We have developed a versatile oxidation-responsive nanoparticle system that delivers coenzyme Q ₁₀ to sites of tissue injury and achieves potent protection against hepatic ischemia-reperfusion injury.

Early prediction of acute respiratory distress syndrome (ARDS) after liver transplantation (LT) facilitates timely intervention. We aimed to develop a predictor of post-LT ARDS using machine learning (ML) methods. Data from 755 patients in the internal validation set and 115 patients in the external validation set were retrospectively reviewed, covering demographics, etiology, medical history, laboratory results, and perioperative data. According to the area under the receiver operating characteristic curve (AUROC), accuracy, specificity, sensitivity, and F1-value, the prediction performance of seven ML models, including logistic regression (LR), decision tree, random forest (RF), gradient boosting decision tree (GBDT), naïve bayes (NB), light gradient boosting machine (LGBM) and extreme gradient boosting (XGB) were evaluated and compared with acute lung injury prediction scores (LIPS). 234 (30.99%) ARDS patients were diagnosed. The RF model had the best performance, with an AUROC of 0.766 (accuracy: 0.722, sensitivity: 0.617) in the internal validation set and a comparable AUROC of 0.844 (accuracy: 0.809, sensitivity: 0.750) in the external validation set. The performance of all ML models was better than LIPS (AUROC 0.692, 0.776). The predictor variables included the age of the recipient, BMI, MELD score, total bilirubin, prothrombin time, operation time, standard urine volume, total intake volume, and red blood cell infusion volume. We firstly developed a risk predictor of post-LT ARDS based on RF model to ameliorate clinical practice.