Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Liver ischemia-reperfusion injury (LIRI) is a pathological process involving multiple injury factors and cell types, with different stages. Currently, protective drugs targeting a single condition are limited in efficacy, and interventions on immune cells will also be accompanied by a series of side effects. In the current bottleneck research stage, the multi-target and obvious clinical efficacy of Chinese medicine (CM) is expected to become a breakthrough point in the research and development of new drugs. In this review, we summarize the roles of reactive oxygen species (ROS) and reactive nitrogen species (RNS) in various stages of hepatic ischemia-reperfusion and on various types of cells. Combined with the current research progress in reducing ROS/RNS with CM, new therapies and mechanisms for the treatment of hepatic ischemia-reperfusion are discussed.
Reperfusion Injury/drug therapy* ; Reactive Oxygen Species/metabolism* ; Reactive Nitrogen Species/metabolism* ; Humans ; Liver/drug effects* ; Animals ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/pharmacology*

Liposomes serve as critical carriers for drugs and vaccines, with their biological effects influenced by their size. The microfluidic method, renowned for its precise control, reproducibility, and scalability, has been widely employed for liposome preparation. Although some studies have explored factors affecting liposomal size in microfluidic processes, most focus on small-sized liposomes, predominantly through experimental data analysis. However, the production of larger liposomes, which are equally significant, remains underexplored. In this work, we thoroughly investigate multiple variables influencing liposome size during microfluidic preparation and develop a machine learning (ML) model capable of accurately predicting liposomal size. Experimental validation was conducted using a staggered herringbone micromixer (SHM) chip. Our findings reveal that most investigated variables significantly influence liposomal size, often interrelating in complex ways. We evaluated the predictive performance of several widely-used ML algorithms, including ensemble methods, through cross-validation (CV) for both liposome size and polydispersity index (PDI). A standalone dataset was experimentally validated to assess the accuracy of the ML predictions, with results indicating that ensemble algorithms provided the most reliable predictions. Specifically, gradient boosting was selected for size prediction, while random forest was employed for PDI prediction. We successfully produced uniform large (600 nm) and small (100 nm) liposomes using the optimised experimental conditions derived from the ML models. In conclusion, this study presents a robust methodology that enables precise control over liposome size distribution, offering valuable insights for medicinal research applications.

Functional dyspepsia (FD), characterized by persistent or recurrent dyspeptic symptoms without identifiable organic, systemic or metabolic causes, is an increasingly recognized global health issue. The objective of this guideline is to equip clinicians and nursing professionals with evidence-based strategies for the management and treatment of adult patients with FD using traditional Chinese medicine (TCM). The Guideline Development Group consulted existing TCM consensus documents on FD and convened a panel of 35 clinicians to generate initial clinical queries. To address these queries, a systematic literature search was conducted across PubMed, EMBASE, the Cochrane Library, China National Knowledge Infrastructure (CNKI), VIP Database, China Biology Medicine (SinoMed) Database, Wanfang Database, Traditional Medicine Research Data Expanded (TMRDE), and the Traditional Chinese Medical Literature Analysis and Retrieval System (TCMLARS). The evidence from the literature was critically appraised using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. The strength of the recommendations was ascertained through a consensus-building process involving TCM and allopathic medicine experts, methodologists, pharmacologists, nursing specialists, and health economists, leveraging their collective expertise and empirical knowledge. The guideline comprises a total of 43 evidence-informed recommendations that span a range of clinical aspects, including the pathogenesis according to TCM, diagnostic approaches, therapeutic interventions, efficacy assessments, and prognostic considerations. Please cite this article as: Zhang SS, Zhao LQ, Hou XH, Bian ZX, Zheng JH, Tian HH, Yang GH, Hong WS, He YY, Liu L, Shen H, Li YP, Xie S, Shu J, Zeng BF, Li JX, Liu Z, Xiao ZH, Xiao JD, Zheng PY, Huang SG, Chen SL, Fei GJ. International clinical practice guideline on the use of traditional Chinese medicine for functional dyspepsia (2025). J Integr Med. 2025; 23(5):502-518.
Dyspepsia/drug therapy* ; Humans ; Medicine, Chinese Traditional/methods* ; Practice Guidelines as Topic ; Drugs, Chinese Herbal/therapeutic use*

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

OBJECTIVES: To investigate the role and mechanism of fibroblast growth factor (FGF) 19 in inflammation-induced injury of vascular endothelial cells caused by high glucose (HG). METHODS: Human umbilical vein endothelial cells (HUVECs) were randomly divided into four groups: control, HG, FGF19, and HG+FGF19 (n=3 each). The effect of different concentrations of glucose and/or FGF19 on HUVEC viability was assessed using the CCK8 assay. Flow cytometry was utilized to examine the impact of FGF19 on HUVEC apoptosis. Levels of interleukin-6 (IL-6), inducible nitric oxide synthase (iNOS), total superoxide dismutase (T-SOD), and malondialdehyde (MDA) were measured by ELISA. Real-time quantitative PCR and Western blotting were used to determine the mRNA and protein expression levels of vascular endothelial growth factor (VEGF), nuclear factor erythroid 2 related factor 2 (Nrf2), and heme oxygenase-1 (HO-1). Cells were further divided into control, siRNA-Nrf2 (siNrf2), HG, HG+FGF19, HG+FGF19+negative control, and HG+FGF19+siNrf2 groups (n=3 each) to observe the effect of FGF19 on oxidative stress injury in HUVECs induced by high glucose after silencing the Nrf2 gene. RESULTS: Compared to the control group, the HG group exhibited increased apoptosis rate, increased IL-6, iNOS and MDA levels, and increased VEGF mRNA and protein expression, along with decreased T-SOD activity and decreased mRNA and protein expression of Nrf2 and HO-1 (P<0.05). Compared to the HG group, the HG+FGF19 group showed reduced apoptosis rate, decreased IL-6, iNOS and MDA levels, and decreased VEGF mRNA and protein expression, with increased T-SOD activity and increased Nrf2 and HO-1 mRNA and protein expression (P<0.05). Compared to the HG+FGF19+negative control group, the HG+FGF19+siNrf2 group had decreased T-SOD activity and increased MDA levels (P<0.05). CONCLUSIONS FGF19 can alleviate inflammation-induced injury in vascular endothelial cells caused by HG, potentially through the Nrf2/HO-1 signaling pathway.
Humans ; NF-E2-Related Factor 2/genetics* ; Signal Transduction ; Human Umbilical Vein Endothelial Cells/drug effects* ; Fibroblast Growth Factors/pharmacology* ; Heme Oxygenase-1/physiology* ; Apoptosis/drug effects* ; Glucose ; Inflammation ; Interleukin-6/analysis* ; Vascular Endothelial Growth Factor A/genetics* ; Nitric Oxide Synthase Type II/analysis* ; Cells, Cultured

Objective:To evaluate the effects and underlying mechanisms of preoperative intestinal microbiota modulation on the recovery of prolonged postoperative ileus(PPOI)in gastric cancer patients with sarcopenia Methods:Skeletal muscle mass(SMM)was assessed using bioelectrical impedance analysis(BIA),and the skeletal muscle index(SMI)was calculated.A total of 156 gastric cancer patients with sarcopenia who underwent surgery at the Department of Gastrointestinal Surgery,Liyang People's Hospital,from January 2022 to December 2024 were randomly assigned to either the control group or the experimental group.The control group received conventional prehabilitation interventions,including nutritional support,respiratory function training,and physical exercise.The experimental group received probiotic-based intestinal microbiota modulation in addition to conventional prehabilitation interventions.Postoperative recovery indicators,including time to first flatus,incidence of postoperative complications,length of hospital stay,and hospitalization costs,were compared between the two groups.Results:Among the 156 sarcopenic gastric cancer patients,121 were male,and 35 were female.Statistically significant differences were observed between the two groups in terms of time to first flatus,incidence of postoperative complications,and length of hospital stay(P<0.05).Postoperative complications occurred in 38 patients(24.3%),with 10 cases(6.4%)in the experimental group and 28 cases(17.9%)in the control group.The time to first flatus in the experimental group was significantly shorter than in the control group(48.0 hours vs 72.0 hours,P<0.001).Conclusion:Preoperative intestinal microbiota modulation significantly reduces the incidence of PPOI and postoperative complications in gastric cancer patients with low SMI,thereby promoting postoperative gastrointestinal function recovery.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Liposomes serve as critical carriers for drugs and vaccines,with their biological effects influenced by their size.The microfluidic method,renowned for its precise control,reproducibility,and scalability,has been widely employed for liposome preparation.Although some studies have explored factors affecting liposomal size in microfluidic processes,most focus on small-sized liposomes,predominantly through experimental data analysis.However,the production of larger liposomes,which are equally significant,remains underexplored.In this work,we thoroughly investigate multiple variables influencing liposome size during microfluidic preparation and develop a machine learning(ML)model capable of accurately predicting liposomal size.Experimental validation was conducted using a staggered herringbone micromixer(SHM)chip.Our findings reveal that most investigated variables significantly influence liposomal size,often interrelating in complex ways.We evaluated the predictive performance of several widely-used ML algorithms,including ensemble methods,through cross-validation(CV)for both lipo-some size and polydispersity index(PDI).A standalone dataset was experimentally validated to assess the accuracy of the ML predictions,with results indicating that ensemble algorithms provided the most reliable predictions.Specifically,gradient boosting was selected for size prediction,while random forest was employed for PDI prediction.We successfully produced uniform large(600 nm)and small(100 nm)liposomes using the optimised experimental conditions derived from the ML models.In conclusion,this study presents a robust methodology that enables precise control over liposome size distribution,of-fering valuable insights for medicinal research applications.