The objective of this study was to investigate whether Stenotrophomonas maltophilia(S.maltophilia)induces ferroptosis,a form of iron-dependent cell death,leading to an inflamma-tory response in RAW 264.7 macrophages by elevating oxidative stress levels.RAW 264.7 cells were stimulated with varying concentrations of S.maltophilia.The concentrations of TNF-αand IL-1β were quantified using ELISA kits to assess the impact of S.maltophilia on the inflammatory response in RAW 264.7 cells.The activities of glutathione(GSH)and malondialdehyde(MDA)levels were measured using GSH and MDA assay kits to evaluate changes in oxidative stress.West-ern blot analysis was employed to detect the expression levels of COX-2,xCT,GPX4,and other proteins involved in ferroptosis signaling pathways,thereby investigating the effect of S.malto-philia on ferroptosis in RAW 264.7 cells.The results demonstrated that S.maltophilia induced concentration-dependent increases in inflammation and oxidative stress in RAW 264.7 cells,up-regulated the expression of COX-2 protein and down-regulated the expression of xCT and GPX4.Pretreatment with the ROS inhibitor N-acetylcysteine(NAC)significantly mitigated the S.malto-philia-induced oxidative stress and ferroptosis signaling activation,thereby alleviating the inflam-matory response.Furthermore,treatment with the ferroptosis inhibitor Fer-1 directly suppressed the activation of the ferroptosis signaling pathway and reversed the inflammation induced by S.maltophilia.These findings suggest that S.maltophilia triggers inflammation in RAW 264.7 cells by activating the ferroptosis signaling pathway via an increase in oxidative stress levels.

The objective of this study was to investigate whether Stenotrophomonas maltophilia(S.maltophilia)induces ferroptosis,a form of iron-dependent cell death,leading to an inflamma-tory response in RAW 264.7 macrophages by elevating oxidative stress levels.RAW 264.7 cells were stimulated with varying concentrations of S.maltophilia.The concentrations of TNF-αand IL-1β were quantified using ELISA kits to assess the impact of S.maltophilia on the inflammatory response in RAW 264.7 cells.The activities of glutathione(GSH)and malondialdehyde(MDA)levels were measured using GSH and MDA assay kits to evaluate changes in oxidative stress.West-ern blot analysis was employed to detect the expression levels of COX-2,xCT,GPX4,and other proteins involved in ferroptosis signaling pathways,thereby investigating the effect of S.malto-philia on ferroptosis in RAW 264.7 cells.The results demonstrated that S.maltophilia induced concentration-dependent increases in inflammation and oxidative stress in RAW 264.7 cells,up-regulated the expression of COX-2 protein and down-regulated the expression of xCT and GPX4.Pretreatment with the ROS inhibitor N-acetylcysteine(NAC)significantly mitigated the S.malto-philia-induced oxidative stress and ferroptosis signaling activation,thereby alleviating the inflam-matory response.Furthermore,treatment with the ferroptosis inhibitor Fer-1 directly suppressed the activation of the ferroptosis signaling pathway and reversed the inflammation induced by S.maltophilia.These findings suggest that S.maltophilia triggers inflammation in RAW 264.7 cells by activating the ferroptosis signaling pathway via an increase in oxidative stress levels.

Background/Aims: Liver transplantation is the most effective treatment for the sickest patients with acute-on-chronic liver failure (ACLF). However, the influence of donor age on liver transplantation, especially in ACLF patients, is still unclear. Methods: In this study, we used the data of the Scientific Registry of Transplant Recipients. We included patients with ACLF who received liver transplantation from January 1, 2007, to December 31, 2017, and the total number was 13,857. We allocated the ACLF recipients by age intogroup I (donor age ≤17 years, n=647); group II (donor age 18–59 years, n=11,423); and group III (donor age ≥60 years, n=1,787). Overall survival (OS), graft survival, and mortality were com-pared among the three age groups and the four ACLF grades. Cox regression was also analyzed. Results: The 1-, 3-, and 5-year OS rates were 89.6%, 85.5%, and 82.0% in group I; 89.4%, 83.4%, and 78.2% in group II; and 86.8%, 78.4%, and 71.4% in group III, respectively (p<0.001).When we analyzed the different effects of donor age on OS with different ACLF grades, in groupsII and III, we observed statistical differences. Finally, the cubic spline curve told us that the relative death rate changed linearly with increasing donor age. Conclusions Donor age is related to OS and graft survival of ACLF patients after transplanta-tion, and poorer results were associated with elderly donors. In addition, different donor ages have different effects on recipients with different ACLF grades.

Background/Aims: Liver transplantation is the most effective treatment for the sickest patients with acute-on-chronic liver failure (ACLF). However, the influence of donor age on liver transplantation, especially in ACLF patients, is still unclear. Methods: In this study, we used the data of the Scientific Registry of Transplant Recipients. We included patients with ACLF who received liver transplantation from January 1, 2007, to December 31, 2017, and the total number was 13,857. We allocated the ACLF recipients by age intogroup I (donor age ≤17 years, n=647); group II (donor age 18–59 years, n=11,423); and group III (donor age ≥60 years, n=1,787). Overall survival (OS), graft survival, and mortality were com-pared among the three age groups and the four ACLF grades. Cox regression was also analyzed. Results: The 1-, 3-, and 5-year OS rates were 89.6%, 85.5%, and 82.0% in group I; 89.4%, 83.4%, and 78.2% in group II; and 86.8%, 78.4%, and 71.4% in group III, respectively (p<0.001).When we analyzed the different effects of donor age on OS with different ACLF grades, in groupsII and III, we observed statistical differences. Finally, the cubic spline curve told us that the relative death rate changed linearly with increasing donor age. Conclusions Donor age is related to OS and graft survival of ACLF patients after transplanta-tion, and poorer results were associated with elderly donors. In addition, different donor ages have different effects on recipients with different ACLF grades.

Background/Aims: Liver transplantation is the most effective treatment for the sickest patients with acute-on-chronic liver failure (ACLF). However, the influence of donor age on liver transplantation, especially in ACLF patients, is still unclear. Methods: In this study, we used the data of the Scientific Registry of Transplant Recipients. We included patients with ACLF who received liver transplantation from January 1, 2007, to December 31, 2017, and the total number was 13,857. We allocated the ACLF recipients by age intogroup I (donor age ≤17 years, n=647); group II (donor age 18–59 years, n=11,423); and group III (donor age ≥60 years, n=1,787). Overall survival (OS), graft survival, and mortality were com-pared among the three age groups and the four ACLF grades. Cox regression was also analyzed. Results: The 1-, 3-, and 5-year OS rates were 89.6%, 85.5%, and 82.0% in group I; 89.4%, 83.4%, and 78.2% in group II; and 86.8%, 78.4%, and 71.4% in group III, respectively (p<0.001).When we analyzed the different effects of donor age on OS with different ACLF grades, in groupsII and III, we observed statistical differences. Finally, the cubic spline curve told us that the relative death rate changed linearly with increasing donor age. Conclusions Donor age is related to OS and graft survival of ACLF patients after transplanta-tion, and poorer results were associated with elderly donors. In addition, different donor ages have different effects on recipients with different ACLF grades.

Background/Aims: Liver transplantation is the most effective treatment for the sickest patients with acute-on-chronic liver failure (ACLF). However, the influence of donor age on liver transplantation, especially in ACLF patients, is still unclear. Methods: In this study, we used the data of the Scientific Registry of Transplant Recipients. We included patients with ACLF who received liver transplantation from January 1, 2007, to December 31, 2017, and the total number was 13,857. We allocated the ACLF recipients by age intogroup I (donor age ≤17 years, n=647); group II (donor age 18–59 years, n=11,423); and group III (donor age ≥60 years, n=1,787). Overall survival (OS), graft survival, and mortality were com-pared among the three age groups and the four ACLF grades. Cox regression was also analyzed. Results: The 1-, 3-, and 5-year OS rates were 89.6%, 85.5%, and 82.0% in group I; 89.4%, 83.4%, and 78.2% in group II; and 86.8%, 78.4%, and 71.4% in group III, respectively (p<0.001).When we analyzed the different effects of donor age on OS with different ACLF grades, in groupsII and III, we observed statistical differences. Finally, the cubic spline curve told us that the relative death rate changed linearly with increasing donor age. Conclusions Donor age is related to OS and graft survival of ACLF patients after transplanta-tion, and poorer results were associated with elderly donors. In addition, different donor ages have different effects on recipients with different ACLF grades.

This study investigates the pharmacokinetics and metabolic characteristics of three marine-derived piericidins as potential drug leads for kidney disease: piericidin A (PA) and its two glycosides (GPAs), glucopiericidin A (GPA) and 13-hydroxyglucopiericidin A (13-OH-GPA). The research aims to facilitate lead selection and optimization for developing a viable preclinical candidate. Rapid absorption of PA and GPAs in mice was observed, characterized by short half-lives and low bioavailability. Glycosides and hydroxyl groups significantly enhanced the absorption rate (13-OH-GPA > GPA > PA). PA and GPAs exhibited metabolic instability in liver microsomes due to Cytochrome P450 enzymes (CYPs) and uridine diphosphoglucuronosyl transferases (UGTs). Glucuronidation emerged as the primary metabolic pathway, with UGT1A7, UGT1A8, UGT1A9, and UGT1A10 demonstrating high elimination rates (30%-70%) for PA and GPAs. This rapid glucuronidation may contribute to the low bioavailability of GPAs. Despite its low bioavailability (2.69%), 13-OH-GPA showed higher kidney distribution (19.8%) compared to PA (10.0%) and GPA (7.3%), suggesting enhanced biological efficacy in kidney diseases. Modifying the C-13 hydroxyl group appears to be a promising approach to improve bioavailability. In conclusion, this study provides valuable metabolic insights for the development and optimization of marine-derived piericidins as potential drug leads for kidney disease.
Animals ; Male ; Mice ; Aquatic Organisms/chemistry* ; Biological Availability ; Cytochrome P-450 Enzyme System/metabolism* ; Glucuronosyltransferase/metabolism* ; Microsomes, Liver/metabolism* ; Molecular Structure ; Biological Products/pharmacokinetics* ; Pyridines/pharmacokinetics*

The glucagon receptor (GCGR) is a critical target for the treatment of metabolic disorders such as Type 2 Diabetes Mellitus (T2DM) and obesity. Activation of GCGR enhances systemic insulin sensitivity through paracrine stimulation of insulin secretion, presenting a promising avenue for treatment. However, the discovery of effective GCGR agonists remains a challenging and resource-intensive process, often requiring time-consuming wet-lab experiments to synthesize and screen potential compounds. Recent advances in artificial intelligence technologies have demonstrated great potential in accelerating drug discovery by streamlining screening and efficiently predicting bioactivity. In the present work, we propose DeepGCGR, a two-layer deep learning model that leverages graph convolutional networks (GCN) integrated with a multiple attention mechanism to expedite the identification of GCGR agonists. In the first layer, the model predicts the bioactivity of various compounds against GCGR, efficiently filtering large chemical libraries to identify promising candidates. In the second layer, DeepGCGR classifies high bioactive compounds based on their functional effects on GCGR signaling, identifying those with potential agonistic or antagonistic effects. Moreover, DeepGCGR was specifically applied to identify novel GCGR-regulating compounds for the treatment of T2DM from natural products derived from traditional Chinese medicine (TCM). The proposed method will not only offer an effective strategy for discovering GCGR-targeting compounds with functional activation properties but also provide new insights into the development of T2DM therapeutics.
Deep Learning ; Drug Discovery/methods* ; Humans ; Diabetes Mellitus, Type 2/metabolism* ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/pharmacology*

Objectives To construct frailty risk prediction models based on logistic regression anal-ysis,decision tree and random forest algorithm in elderly patients with heart failure(HF),and to compare the predictive performance of three models.Methods A total of 426 elderly HF patients hospitalized in the Affiliated Hospital of Nantong University from September 2022 to October 2023 were selected using convenience sampling.Based on the results of frailty assessment,194 of them were classified into the frail group and the other 232 into the non-frail group.The 426 patients were divided into training(299 casses)and testing sets(127 cases)in a 7∶3 ratio.Three prediction models were then constructed in the training set,while the test set was used to validate the results.Area under curve and confusion matrix were used to measure performance of the mod-els.The optimal model was then selected by evaluating the performance on the testing set.Results The area under curve value of the logistic regression model,decision tree model and random forest model in the testing set was 0.898,0.825 and 0.903,with a classification accuracy of 84.25％,77.95％and 83.46％,a sensitivity of 82.76％,68.97％and 82.76％,a specificity of 85.51％,85.51％and 84.06％,a positive predictive value of 82.76％,80.00％and 81.36％,and a negative predictive value of 85.51％,76.62％and 85.29％,respectively.The factors that ultimately affecting frailty in elderly HF patients were age,left atrial diameter,depression,albumin,physical activity level and social support.Conclusion Among the three prediction models,the logistic regression model demonstrates best predictive performance for frailty risk in elderly HF patients than the decision tree and random forest models.

Objectives To construct frailty risk prediction models based on logistic regression anal-ysis,decision tree and random forest algorithm in elderly patients with heart failure(HF),and to compare the predictive performance of three models.Methods A total of 426 elderly HF patients hospitalized in the Affiliated Hospital of Nantong University from September 2022 to October 2023 were selected using convenience sampling.Based on the results of frailty assessment,194 of them were classified into the frail group and the other 232 into the non-frail group.The 426 patients were divided into training(299 casses)and testing sets(127 cases)in a 7∶3 ratio.Three prediction models were then constructed in the training set,while the test set was used to validate the results.Area under curve and confusion matrix were used to measure performance of the mod-els.The optimal model was then selected by evaluating the performance on the testing set.Results The area under curve value of the logistic regression model,decision tree model and random forest model in the testing set was 0.898,0.825 and 0.903,with a classification accuracy of 84.25％,77.95％and 83.46％,a sensitivity of 82.76％,68.97％and 82.76％,a specificity of 85.51％,85.51％and 84.06％,a positive predictive value of 82.76％,80.00％and 81.36％,and a negative predictive value of 85.51％,76.62％and 85.29％,respectively.The factors that ultimately affecting frailty in elderly HF patients were age,left atrial diameter,depression,albumin,physical activity level and social support.Conclusion Among the three prediction models,the logistic regression model demonstrates best predictive performance for frailty risk in elderly HF patients than the decision tree and random forest models.