Background and Aims: The aims of the present study were to characterize the PANoptosis signature in intrahepatic cholangiocarcinoma (ICC) patients, construct a novel model to guide clinical diagnosis and treatment, and further explore the associated molecular mechanisms of drug resistance. Approach and Results: In total, 85 PANoptosis-related genes that possess both PANoptosis and multi-omics features were, respectively, screened from transcriptomic data from the OEP001105 public cohort and from transcriptomic and proteomic sequencing data from The First Affiliated Hospital of Chongqing Medical University. A novel framework integrating Cox regression analysis and 5 machine learning algorithms was developed to identify the 5 hub genes (POSTN, SFN, MYOF, HOGA1, and PECR). The subsequently constructed PANoptosis risk score demonstrates outstanding performance in predicting prognosis and clinical translation across multicenter cohorts with multi-omics profiling. Bulk and single-cell transcriptome profiling were used to investigate the tumor microenvironment, emphasizing the crucial role of macrophages in the tumor microenvironment of ICCs. Moreover, a positive spatial correlation of cancer-associated fibroblasts–derived POSTN expression with tumor-associated macrophages infiltration and PD-L1/PD-L2 expression in ICC patients was observed, suggesting that overexpression of POSTN may lead to resistance to immune checkpoint blockade therapy in ICC patients. Conclusions: The present study identified a precise prognostic and treatment strategy for ICC patients prone to PANoptosis, investigated the molecular mechanisms of PANoptosis in ICC cells, and highlighted the potential clinical relevance of the PANoptosis risk score in predicting prognosis and therapy response. These findings will help guide clinical treatment strategies for ICC.

Hepatic ischemia-reperfusion injury is an unavoidable surgical complication of liver transplantation and the leading cause of poor graft function and increased mortality post-transplantation. Multiple mechanisms have been implicated in ischemia-reperfusion injury; however, the characteristic changes at the transcriptional and metabolic levels in the early, intermediate, and late phases of ischemia-reperfusion injury remain unclear. In the study, mice underwent laparotomy following anesthesia, and the blood vessels of the liver were clipped using a vascular clamp to form 70% warm ischemia of the liver. Mouse liver sections and serum samples were collected and divided into the Sham, I1R12, I1R24, and I1R48 groups. Transcriptomics and metabolomics analyses were performed to study characteristic alterations during the early, intermediate, and late phases of ischemia-reperfusion injury. Quantitative real-time PCR was used to validate the critical differentially expressed genes. The differentially expressed genes and metabolites were identified by transcriptomics and metabolomics analyses. Moreover, GO and KEGG enrichment analyses indicated that glucose metabolism remodeling, inflammatory response activation, and lipid metabolism remodeling were characteristic changes in the early, intermediate, and late phases of ischemia-reperfusion injury, respectively. In summary, our study revealed the importance of glucolipid metabolism in ischemia-reperfusion injury and provided potential therapeutic intervention targets and a new perspective to explore the underlying mechanisms of ischemia-reperfusion injury.

Background: Clinically relevant postoperative pancreatic fistula (CR-POPF) represents a significant complication after pancreaticoduodenectomy (PD). Therefore, the early prediction of CR-POPF is of paramount importance. Based on above, this study sought to develop a CR-POPF prediction model that amalgamates radiomics and clinical features to predict CR-POPF, utilizing Shapley Additive explanations (SHAP) for visualization.Methods: Extensive radiomics features were extracted from preoperative enhanced Computed Tomography (CT) images of patients scheduled for PD. Subsequently, feature selection was performed using Least Absolute Shrinkage and Selection Operator (Lasso) regression and random forest (RF) algorithm to select pertinent radiomics and clinical features. Last, 15 CR-POPF prediction models were developed using five distinct machine learning (ML) predictors, based on selected radiomics features, selected clinical features, and a combination of both. Model performance was compared using DeLong’s test for the area under the receiver operating characteristic curve (AUC) differences.Results: The CR-POPF prediction model based on the XGBoost predictor with the combination of the radiomics and clinical features selected by Lasso regression and RF exhibited superior performance among these 15 CR-POPF prediction models, achieving an accuracy of 0.85, an AUC of 0.93. DeLong’s test showed statistically significant differences (P < 0.05) when compared to the radiomics-only and clinical-only models, with recall of 0.63, precision of 0.65, and F1 score of 0.64.Conclusion: The proposed CR-POPF prediction model based on the XGBoost predictor with the combination of the radiomics and clinical features selected by Lasso regression and RF can effectively predicting the CR-POPF and may provide strong support for early clinical management of CR-POPF.

Acute rejection (AR) is an important factor that leads to poor prognosis after liver transplantation (LT). Macrophage M1-polarization is an important mechanism in AR development. MicroRNAs play vital roles in disease regulation; however, their effects on macrophages and AR remain unclear. In this study, rat models of AR were established following LT, and macrophages and peripheral blood mononuclear cells were isolated from rats and humans, respectively. We found miR-449a expression to be significantly reduced in macrophages and peripheral blood mononuclear cells. Overexpression of miR-449a not only inhibited the M1-polarization of macrophages in vitro but also improved the AR of transplant in vivo. The mechanism involved inhibiting the noncanonical nuclear factor-kappaB (NF-κB) pathway. We identified procollagen-lysine1,2-oxoglutarate5-dioxygenase 1 (PLOD1) as a target gene of miR-449a, which could reverse miR-449a's inhibition of macrophage M1-polarization, amelioration of AR, and inhibition of the NF-κB pathway. Overall, miR-449a inhibited the NF-κB pathway in macrophages through PLOD1 and also inhibited the M1-polarization of macrophages, thus attenuating AR after LT. In conclusion, miR-449a and PLOD1 may be new targets for the prevention and mitigation of AR.

Background: Ethylene oxide, a reactive epoxy compound, has been widely used in various industries for many years. However, evidence of the combined toxic effects of ethylene oxide exposure on the liver is still lacking. Methods: We analyzed the merged data from the National Health and Nutrition Examination Survey (NHANES) from 2013 to 2016. Ultimately, 4141 adults aged 18 and over were selected as the sample. We used linear regression to explore the association between blood ethylene oxide and LFT indicators. Results: The weighted linear regression model showed that HbEO is positively correlated with ALP (β = 2.61, 95% CI 1.97, 3.24, p < 0.0001), GGT (β = 5.75, 95% CI 4.46, 7/05, p < 0.0001), ALT (β = 0.50, 95% CI 0.09, 0.90, p = 0.0158), and AST (β = 0.71, 95% CI 0.44, 0.98, p < 0.0001) and negatively correlated with TBIL (β = −0.30, 95% CI −0.43, −0.16, p < 0.0001). Conclusions: Ethylene oxide exposure is significantly associated with changes in liver function indicators among adults in the United States. Future work should further examine these relationships.

In recent years, research on nanoparticles (NPs) has brought a new phase in the development of medicine. The term “NPs” refers to nanoscale particles, typically between 10 and 1,000 nm in size, that are within the range of cellular and molecular structures. Compared with traditional ophthalmic drugs, NPs can carry drugs to overcome obstacles, target drug delivery, prolong drug release, reduce drug toxicity, and can be used in gene therapy. After a brief introduction to the classification of NPs, we will focus on the potential applications of different NPs in the diagnosis and treatment of different retinal diseases and introduce innovative methods derived from NPs.