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*

Ferroptosis has received great attention as an iron-dependent programmed cell death for efficient cancer therapy. However, with the accumulation of iron in tumor cells, the antioxidant system is activated by reducing glutathione (GSH) with glutathione peroxidase 4 (GPX4), which critically limits the ferroptosis therapeutic effect. Herein, an iron and GPX4 silencing siRNA (siGPX4) co-encapsulated ferritin nanocage (HFn@Fe/siGPX4) was developed to enhance ferroptosis by disruption of redox homeostasis and inhibition of antioxidant enzyme synergistically. The siGPX4 were loaded into the nanocages by pre-incubated with iron, which could significantly improve the loading efficiency of the gene drugs when compared with the reported gene drug loading strategy by ferritin nanocages. And more iron was overloaded into the ferritin through the diffusion method. When HFn@Fe/siGPX4 was taken up by human breast cancer cell MCF-7 in a TfR1-mediated pathway, the excess iron ions in the drug delivery system could for one thing induce ferroptosis by the production of reactive oxygen species (ROS), for another promote siGPX4 escaping from the lysosome to exert gene silencing effect more effectively. Both the in vitro and in vivo results demonstrated that HFn@Fe/siGPX4 could significantly inhibit tumor growth by synergistical ferroptosis. Thus, the developed HFn@Fe/siGPX4 afforded a combined ferroptosis strategy for ferroptosis-based antitumor as well as a novel and efficient gene drug delivery system.

Traditional Chinese medicine (TCM) serves as a treasure trove of ancient knowledge, holding a crucial position in the medical field. However, the exploration of TCM's extensive information has been hindered by challenges related to data standardization, completeness, and accuracy, primarily due to the decentralized distribution of TCM resources. To address these issues, we developed a platform for TCM knowledge discovery (TCMKD, https://cbcb.cdutcm.edu.cn/TCMKD/). Seven types of data, including syndromes, formulas, Chinese patent drugs (CPDs), Chinese medicinal materials (CMMs), ingredients, targets, and diseases, were manually proofread and consolidated within TCMKD. To strengthen the integration of TCM with modern medicine, TCMKD employs analytical methods such as TCM data mining, enrichment analysis, and network localization and separation. These tools help elucidate the molecular-level commonalities between TCM and contemporary scientific insights. In addition to its analytical capabilities, a quick question and answer (Q&A) system is also embedded within TCMKD to query the database efficiently, thereby improving the interactivity of the platform. The platform also provides a TCM text annotation tool, offering a simple and efficient method for TCM text mining. Overall, TCMKD not only has the potential to become a pivotal repository for TCM, delving into the pharmacological foundations of TCM treatments, but its flexible embedded tools and algorithms can also be applied to the study of other traditional medical systems, extending beyond just TCM.

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*

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.

Traditional Chinese medicine(TCM)serves as a treasure trove of ancient knowledge,holding a crucial position in the medical field.However,the exploration of TCM's extensive information has been hindered by challenges related to data standardization,completeness,and accuracy,primarily due to the decen-tralized distribution of TCM resources.To address these issues,we developed a platform for TCM knowledge discovery(TCMKD,https://cbcb.cdutcm.edu.cn/TCMKD/).Seven types of data,including syndromes,formulas,Chinese patent drugs(CPDs),Chinese medicinal materials(CMMs),ingredients,targets,and diseases,were manually proofread and consolidated within TCMKD.To strengthen the integration of TCM with modern medicine,TCMKD employs analytical methods such as TCM data mining,enrichment analysis,and network localization and separation.These tools help elucidate the molecular-level commonalities between TCM and contemporary scientific insights.In addition to its analytical capabilities,a quick question and answer(Q&A)system is also embedded within TCMKD to query the database efficiently,thereby improving the interactivity of the platform.The platform also provides a TCM text annotation tool,offering a simple and efficient method for TCM text mining.Overall,TCMKD not only has the potential to become a pivotal repository for TCM,delving into the pharmaco-logical foundations of TCM treatments,but its flexible embedded tools and algorithms can also be applied to the study of other traditional medical systems,extending beyond just TCM.

Objective To observe the feasibility of transesophageal echocardiography(TEE)combined with agitated saline contrast echocardiography(ASCE)for identifying morphological characteristics of high-risk patent foramen ovale(PFO)and evaluating the risk of PFO related stroke.Methods Totally 212 PFO patients diagnosed by TEE combined with ASCE were enrolled,including 100 cases with cryptogenic stroke(CS)(CS group)and 112 without CS(non-CS group).Anatomical morphological characteristics of PFO were comparatively analyzed between groups to screen the independent factors of CS.Results In CS group,the left and right atrial opening diameters of PFO were all larger than those in non-CS group in both resting-state and stimulated state.The aortic root diameter in CS group was larger than that in non-CS group,and the incidence of atrial septal aneurysm(ASA),high activity of the atrial septum,inferior vena cava valve or Chiari network,large amount of right-to-left-shunt(RLS)in stimulated state,and multiple outlets of the oval valve in CS group were all higher than those in non-CS group(all P＜0.05).Logistic regression analysis showed that ASA,high atrial septal activity,large amount of RLS and multiple oval valve outlets were all independent factors associated with CS(OR=0.211,0.384,0.999,0.199,all P＜0.05).Conclusion TEE combined with ASCE could identify anatomical characteristics of high-risk PFO and assess the risk of PFO related stroke.

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.