ObjectiveTo investigate the mechanism by which Wumeiwan suppresses the development and progression of colorectal cancer（CRC） through the regulation of fatty acid metabolic reprogramming， thereby providing new experimental evidence for the prevention and treatment of CRC. MethodsA total of 120 C57BL/6 mice were randomly divided into the blank group， model group， Wumeiwan high-， medium-， and low-dose groups（54， 27， 13.5 g·kg^-1）， and the mesalazine group（0.01 g·kg^-1）， with 20 mice in each group. Except for the blank group， all mice were subjected to azoxymethane（AOM）/dextran sulfate sodium（DSS） treatment to establish an inflammation-associated CRC model. One week after AOM injection， mice in the treatment groups received intragastric administration of the designated drugs， while the blank and model groups received an equal volume of purified water， continuing until 20 d after the intervention endpoint. Hematoxylin-eosin（HE） staining was used to observe colonic histopathological alterations， and immunohistochemistry for vascular endothelial growth factor（VEGF） was performed to evaluate neovascularization and tumor invasion. Metabolomics combined with Kyoto Encyclopedia of Genes and Genomes（KEGG） and metabolite set enrichment analysis（MSEA） was applied to identify key CRC-related metabolic pathways， which were further validated by transcriptomic Gene Ontology（GO） enrichment and gene heatmap analysis. Subsequently， Western blot was performed to determine the expression levels of core proteins in these pathways， and immunofluorescence was used to analyze their localization and co-expression patterns in tissues， thereby elucidating the mechanism of Wumeiwan from multiple biological dimensions. ResultsCompared with the blank group， mice in the model group exhibited a significant decrease in body weight and a significant increase in the disease activity index（DAI） score（P<0.05）， with pronounced colonic mucosal damage accompanied by aggravated tumor invasion. Compared with the model group， Wumeiwan intervention markedly improved body weight loss and reduced DAI score， attenuated mucosal injury， and significantly decreased VEGF expression level（P<0.05）. Multi-omics analysis revealed that differential metabolites and genes across groups were commonly enriched in fatty acid metabolism， fatty acid biosynthesis， and other lipid-related pathways. Relative to the blank group， the model group showed significant upregulation levels of fatty acid synthesis-related genes， including sterol regulatory element-binding protein 1（SREBP1）， fatty acid synthase（FASN）， stearoyl-CoA desaturase 1（SCD1）， as well as saturated fatty acids（P<0.05）. Compared with the model group， treatment with Wumeiwan significantly reduced the expression of key genes involved in fatty acid metabolic pathways， including SREBP1， FASN， and SCD1（P<0.05）. Western blot results further confirmed that proteins in this pathway were significantly elevated in the model group， whereas they were markedly downregulated following Wumeiwan treatment（P<0.05）. Immunofluorescence analysis demonstrated enhanced co-localization of SREBP1 with the cancer-associated fibroblast（CAF） marker α-smooth muscle actin（SMA） in the model group， whereas this co-localization signal was attenuated after Wumeiwan intervention（P<0.05）. ConclusionWumeiwan can improve survival outcomes and alleviate colonic pathological damage in CRC mice， its therapeutic mechanism may be closely associated with the regulation of fatty acid metabolic reprogramming mediated by the SREBP1/FASN/SCD1 signaling pathway.

Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Kupffer cells(KC),an important subset of immune cells in the liver,are essential for maintaining tissue homeostasis and responding quickly to liver damage.The complement receptor of the immuno-globulin superfamily(CRIg)is a receptor protein on the KC membrane.CRIg can not only capture pathogens in the blood flowing through the liver by complement binding but also mediate immune responses by regulating im-mune cells in the liver.Recent studies have confirmed the role of CRIg in regulating liver immunity.This article reviews the main modes of action of CRIg and the research progress of CRIg in regulating liver immunity.

Objective:To explore the role of ductular reaction in assessing the efficacy of liver transplantation.Method:From January 2015 to December 2020, he relevant clinical data were retrospectively reviewed for 100 recipients and their corresponding donors at Shulan (Hangzhou) Hospital. They were assigned into two groups of hepatic steatosis (HS group, 65 cases) and non-hepatic steatosis (non-HS group, 35 cases) according to whether or not receiving steatosis donated liver. Furthermore, based upon the occurrence of early allograft dysfunction (EAD), the participants were categorized into two groups of EAD (33 cases) and non-EAD (67 cases). The degree of bile duct reaction ductular reaction was defined by the percentage of staining area occupied by cytokeratin 19 (CK19) -positive bile duct cells in immunohistochemical-stained specimens. Donor of ductular reaction were compared between HS/non-HS and EAD/non-EAD groups. The risk factors for EAD were identified by univariate and multivariate Logistic regression analysis. Subgroup analysis was conducted based upon the level of ductular reaction (DR number) in donors (DR=0.4 as a threshold) and whether or not donors exhibited steatosis. The impact of DR was examined on the incidence of EAD and survival post-liver transplantation in steatosis donors.Result:The level of DR was higher in steatosis donor than that in non-steatosis donor [ (0.59%±0.385%) vs. (0.32%±0.194%), P<0.01]. And it was higher in EAD group than that in non-EAD group [ (0.72%±0.449%) vs. (0.38%±0.226%), P<0.01]. Multivariate logistic regression analysis showed that a high level of ductular reaction was an independent risk factor for EAD post-liver transplantation in donor. Subgroup analysis revealed that receiving a steatosis donor with low ductular reaction (DR<0.4%) had comparable levels of EAD occurrence and overall survival rate to receiving a non-steatosis donor. Conclusion:Steatosis with low ductular reaction donor may be safely applied for liver transplantation. And assessing donor injury based upon ductular reaction can effectively expand the clinical application of steatosis donors.

Idiopathic pulmonary fibrosis(IPF)is a chronic progressive interstitial lung disease of unknown etiology,with a rapid disease course,poor prognosis,and the absence of effective therapeutic drugs.Mitochondrial dys-function is one of the crucial causes of inducing IPF.Silent information regulator 3(SIRT3)can restore mitochondrial ho-meostasis by inhibiting mitochondrial oxidative stress,repairing mitochondrial DNA damage,and ameliorating abnormal mitochondrial lipid metabolism.This paper summarizes the role and mechanism of SIRT3 in attenuating mitochondrial dys-function based on delineating the relationship between mitochondrial dysfunction and IPF,aiming to provide references for finding effective treatment methods for IPF.

Background and aims:Inflammatory factors play significant roles in the development and occurrence of hepatocellular carcinoma(HCC).However,the tumor-protective functions of growth differentiation factors(GDFs)in HCC are yet to be clarified.In this study,we aimed to evaluate the expression levels of 10 GDFs in tumor and paratumor tissues from patients with HCC and perform in vitro and in vivo ex-periments to elucidate the role of GDF7 in regulating the proliferation and metastasis of HCC.Methods:The gene expression of 10 GDFs was compared between HCC and paratumors using The Cancer Genome Atlas dataset and patient-derived tissues.A tumor microarray containing 108 HCC tissue samples was used to explore the prognostic value of GDF7 expression.Loss-of-function experiments were also performed in vitro and in vivo to investigate the role of GDF7 in HCC.Results:The mRNA and protein levels of GDF7 were significantly lower in HCC tumors than in para-tumors(P＜0.001).Kaplan-Meier analysis showed that decreased GDF7 expression in HCC was asso-ciated with worse overall survival(5-year rate:61.8％vs.27.5％,P＜0.001)and increased recurrence risk(P＜0.001).Multivariate Cox regression analysis demonstrated that low GDF7 expression,the presence of microvascular invasion,and elevated alpha-fetoprotein(AFP)levels were independent risk factors for tumor recurrence and poor survival.Downregulation of GDF7 also increased the tumor growth in HCC cells and in an HCC xenograft model.GDF7 knockdown promoted migration and invasion via epithelial-mesenchymal transition.Meanwhile,a negative correlation between JunB proto-oncogene(JUNB)and GDF7 was observed in HCC tissues.Modulating JUNB levels altered GDF7 protein expression.Conclusions:GDF7 is a potential biomarker for predicting superior outcomes in patients with HCC.GDF7 amplification is a potential therapeutic option for HCC.

Idiopathic pulmonary fibrosis(IPF)is a common interstitial fibrosis disease with poor prognosis and lack of effec-tive treatment to prolong the survival time of patients.Cell senescence can induce tissue inflammation and accelerate the patho-logical process of fibrosis.Excessive oxidative stress,nuclear DNA damage and mitochondrial dysfunction in the pathological en-vironment of IPF are the main causes of cell senescence.Nicotinamide adenine dinucleotide(NAD+)is an important coenzyme in redox reaction,which can regulate a series of biological functions by blocking cell senescence.Its active expression is closely re-lated to the occurrence and development of IPF.Therefore,to explore the mechanism of NAD+regulating cell senescence under pathological condition is of great significance for the prevention and treatment of IPF.

Idiopathic pulmonary fibrosis(IPF)is a common interstitial fibrosis disease with poor prognosis and lack of effec-tive treatment to prolong the survival time of patients.Cell senescence can induce tissue inflammation and accelerate the patho-logical process of fibrosis.Excessive oxidative stress,nuclear DNA damage and mitochondrial dysfunction in the pathological en-vironment of IPF are the main causes of cell senescence.Nicotinamide adenine dinucleotide(NAD+)is an important coenzyme in redox reaction,which can regulate a series of biological functions by blocking cell senescence.Its active expression is closely re-lated to the occurrence and development of IPF.Therefore,to explore the mechanism of NAD+regulating cell senescence under pathological condition is of great significance for the prevention and treatment of IPF.