Liver fibrosis is the intermediate stage in the progression of many chronic liver diseases to liver cirrhosis， and although there is still a lack of widely accepted and effective chemical or biological agents for reversing liver fibrosis， significant progress has been made in the treatment of liver fibrosis with traditional Chinese medicine. This article elaborates on the molecular mechanisms of different herbal extracts， a single Chinese herb， and Chinese patent drugs in reversing liver fibrosis， such as inhibiting liver inflammation， exerting an effect on lipid peroxidation damage， inhibiting the activation and proliferation of hepatic stellate cells， regulating the synthesis and secretion of pro-fibrogenic factors， and regulating the synthesis and degradation of extracellular matrix， in order to provide more precise options for the treatment of liver fibrosis in the future.

Objective To analyze the influencing factors of malaria infection of labor service exported to overseas in Langfang City, in order to establish a visualization tool to assist clinicians in predicting the risk of malaria. Methods A total of 4 774 expatriate employees of the Nibei Pipeline Project of the Pipeline Bureau from October 2021 to August 2023 were taken as the subjects, and the gender, age, overseas residence area and Knowledge of malaria controlscores of the study subjects were investigated by questionnaire survey, and the possible risk factors of malaria were screened by logistic regression model. At the same time, the nomogram prediction model was established, and the subjects were divided into the training group and the validation group at a ratio of 2:1, and the area under the curve (ROC) and the decision curve were plotted to evaluate the prediction ability and practicability of the prediction model in this study. Results Among the 4 774 study subjects, 96 cases of malaria occurred, and the detection rate was 2.01%. Junior school （OR=1.723，95% CI:1.361-2.173）， and residence in rural areas（OR=2.091，95%CI:1.760 -3.100）were risk factors (OR>1), while protective measures（OR=0.826，95% CI : 0.781 - 0.901） and high malaria education scores （OR=0.872，95% CI : 0.621 - 0.899）were protective factors.The nomogram prediction model results showed that the area under the curve of the nomogram prediction model in the training group was 0.94 (95% CI : 0.85 - 1.00), while the validation group was 0.93 (95% CI : 0.80 - 1.00). The results of the decision curve showed that when the threshold probability of the population was 0-0.9, the nomogram model was used to predict the risk of malaria occurrence with the highest net income. Conclusion The nomogram prediction model (including gender, education, region, protection and malaria education score) established and validated in this study is of great value for clinicians to screen high-risk patients with malaria.

ObjectiveTo identify the patients with metabolic dysfunction-associated fatty liver disease （MAFLD） among the health check-up population， and to perform stratified management of patients with the low， medium， and high risk of advanced fibrosis based on noninvasive fibrosis scores. MethodsA cross-sectional study was conducted among 3 125 individuals who underwent physical examination in Beijing Physical Examination Center from December 2017 to December 2019， and they were divided into MAFLD group with 1 068 individuals and non-MAFLD group with 2 057 individuals. According to BMI， the MAFLD group was further divided into lean MAFLD group （125 individuals with BMI<24 kg/m²） and non-lean MAFLD group （943 individuals with BMI≥24 kg/m²）. Indicators including demographic data， past history， laboratory examination， and liver ultrasound were compared between groups. Fibrosis-4 （FIB-4） score， NAFLD fibrosis score （NFS）， aspartate aminotransferase-to-platelet ratio index （APRI）， and BARD score were calculated for the patients in the MAFLD group to assess the risk of advanced fibrosis. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U rank sum test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test or the Fisher’s exact test was used for comparison of categorical data between groups. A logistic regression analysis was used to investigate the influence of each indicator in MAFLD. ResultsCompared with the non-MAFLD group， the MAFLD group had significantly higher age （Z=-9.758， P<0.05）， proportion of male patients （χ²=137.555， P<0.05）， and levels of body weight （Z=-27.987， P<0.05）， BMI （Z=-32.714， P<0.05）， waist circumference （Z=-31.805， P<0.05）， hip circumference （Z=-26.342， P<0.05）， waist-hip ratio （Z=-28.554， P<0.05）， alanine aminotransferase （ALT） （Z=-25.820， P<0.05）， aspartate aminotransferase （AST） （Z=-16.894， P<0.05）， gamma-glutamyl transpeptidase （GGT） （Z=-25.069， P<0.05）， alkaline phosphatase （Z=-12.533， P<0.05）， triglyceride （Z=-27.559）， total cholesterol （Z=-7.833， P<0.05）， low-density lipoprotein cholesterol （LDL-C） （Z=-8.222， P<0.05）， and uric acid （UA） （Z=-20.024， P<0.05）， as well as a significantly higher proportion of patients with metabolic syndrome （MetS） （χ²=578.220， P<0.05）， significantly higher prevalence rates of hypertension （χ²=241.694， P<0.05）， type 2 diabetes （χ²=796.484， P<0.05）， and dyslipidemia （χ²=369.843， P<0.05）， and a significant reduction in high-density lipoprotein cholesterol （HDL-C） （Z=23.153， P<0.001）. The multivariate logistic regression analysis showed that male sex （odds ratio ［OR］=1.45， 95% confidence interval ［CI］： 1.203‍ ‍—‍ ‍1.737）， ALT （OR=1.05， 95%CI： 1.046‍ ‍—‍ ‍1.062）， LDL-C （OR=1.23， 95%CI： 1.102‍ ‍—‍ ‍1.373）， and comorbidity with MetS （OR=5.97， 95%CI： 4.876‍ ‍—‍ ‍7.316） were independently associated with MAFLD. Compared with the non-lean MAFLD group， the lean MAFLD group had significantly higher age （Z=3.736， P<0.05） and HDL-C （Z=2.679， P<0.05） and significant reductions in the proportion of male patients （χ²=28.970， P<0.05）， body weight （Z=-14.230， P<0.05）， BMI （Z=-18.188， P<0.05）， waist circumference （Z=-13.451， P<0.05）， hip circumference （Z=-13.317， P<0.05）， ALT （Z=-4.519， P<0.05）， AST （Z=-2.258， P<0.05）， GGT （Z=-4.592， P<0.05）， UA （Z=-4.415， P<0.05）， the proportion of patients with moderate or severe fatty liver disease or MetS （χ²=42.564， P<0.05）， and the prevalence rates of hypertension （χ²=12.057， P<0.05） and type 2 diabetes （χ²=3.174， P<0.05）. Among the patients with MAFLD， 10 patients （0.9%） had an FIB-4 score of >2.67， 4 patients （0.4%） had an NFS score of >0.676， 8 patients （0.7%） had an APRI of >1， and 551 patients （51.6%） had a BARD score of ≥2. ConclusionThere is a relatively high prevalence rate of MAFLD among the health check-up population in Beijing， but with a relatively low number of patients with a high risk of advanced fibrosis， and such patients need to be referred to specialized hospitals for liver diseases.

To summarize the applications of data-intelligence technology in diagnosing lysosomal storage disease(LSD), analyze their opportunities and challenges in clinical practice as well as their development trends, and provide insights and recommendations for advancing digitally driven auxiliary diagnostic technologies.

To review the randomized controlled trials (RCTs) at home and abroad on digital intelligence (DI)-driven rehabilitation in patients of neuromuscular disease, compare the effects of DI-driven rehabilitation with traditional rehabilitation, summarize the special needs and challenges faced by patients in rehabilitation of rare neuromuscular diseases, and provide evidence for the development and quality improvement of rehabilitation for rare neuromuscular diseases.

To provide references to and give suggestions to the development and optimiza-tion of Digital Intelligence (DI) technology in management of non-hospitalized patients by systematical review the application of digital technology in non-hospital settings.

Membrane proteins are integral components of cellular membranes, accounting for approximately 30% of the mammalian proteome and serving as targets for 60% of FDA-approved drugs. They are critical to both physiological functions and disease mechanisms. Their functional protein-protein interactions form the basis for many physiological processes, such as signal transduction, material transport, and cell communication. Membrane protein interactions are characterized by membrane environment dependence, spatial asymmetry, weak interaction strength, high dynamics, and a variety of interaction sites. Therefore, in situ analysis is essential for revealing the structural basis and kinetics of these proteins. This paper introduces currently available in situ analytical techniques for studying membrane protein interactions and evaluates the characteristics of each. These techniques are divided into two categories: label-based techniques (e.g., co-immunoprecipitation, proximity ligation assay, bimolecular fluorescence complementation, resonance energy transfer, and proximity labeling) and label-free techniques (e.g., cryo-electron tomography, in situ cross-linking mass spectrometry, Raman spectroscopy, electron paramagnetic resonance, nuclear magnetic resonance, and structure prediction tools). Each technique is critically assessed in terms of its historical development, strengths, and limitations. Based on the authors’ relevant research, the paper further discusses the key issues and trends in the application of these techniques, providing valuable references for the field of membrane protein research. Label-based techniques rely on molecular tags or antibodies to detect proximity or interactions, offering high specificity and adaptability for dynamic studies. For instance, proximity ligation assay combines the specificity of antibodies with the sensitivity of PCR amplification, while proximity labeling enables spatial mapping of interactomes. Conversely, label-free techniques, such as cryo-electron tomography, provide near-native structural insights, and Raman spectroscopy directly probes molecular interactions without perturbing the membrane environment. Despite advancements, these methods face several universal challenges: (1) indirect detection, relying on proximity or tagged proxies rather than direct interaction measurement; (2) limited capacity for continuous dynamic monitoring in live cells; and (3) potential artificial influences introduced by labeling or sample preparation, which may alter native conformations. Emerging trends emphasize the multimodal integration of complementary techniques to overcome individual limitations. For example, combining in situ cross-linking mass spectrometry with proximity labeling enhances both spatial resolution and interaction coverage, enabling high-throughput subcellular interactome mapping. Similarly, coupling fluorescence resonance energy transfer with nuclear magnetic resonance and artificial intelligence (AI) simulations integrates dynamic structural data, atomic-level details, and predictive modeling for holistic insights. Advances in AI, exemplified by AlphaFold’s ability to predict interaction interfaces, further augment experimental data, accelerating structure-function analyses. Future developments in cryo-electron microscopy, super-resolution imaging, and machine learning are poised to refine spatiotemporal resolution and scalability. In conclusion, in situ analysis of membrane protein interactions remains indispensable for deciphering their roles in health and disease. While current technologies have significantly advanced our understanding, persistent gaps highlight the need for innovative, integrative approaches. By synergizing experimental and computational tools, researchers can achieve multiscale, real-time, and perturbation-free analyses, ultimately unraveling the dynamic complexity of membrane protein networks and driving therapeutic discovery.

Background/Aims: Metabolic dysfunction-associated steatohepatitis (MASH) is a significant risk factor for gallstone formation, but mechanisms underlying MASH-related gallstone formation remain unclear. Golgi membrane protein 1 (GOLM1) participates in hepatic cholesterol metabolism and is upregulated in MASH. Here, we aimed to explore the role of GOLM1 in MASH-related gallstone formation. Methods: The UK Biobank cohort was used for etiological analysis. GOLM1 knockout (GOLM1-/-) and wild-type (WT) mice were fed with a high-fat diet (HFD). Livers were excised for histology and immunohistochemistry analysis. Gallbladders were collected to calculate incidence of cholesterol gallstones (CGSs). Biles were collected for biliary lipid analysis. HepG2 cells were used to explore underlying mechanisms. Human liver samples were used for clinical validation. Results: MASH patients had a greater risk of cholelithiasis. All HFD-fed mice developed MASH, and the incidence of gallstones was 16.7% and 75.0% in GOLM1-/- and WT mice, respectively. GOLM1-/- decreased biliary cholesterol concentration and output. In vivo and in vitro assays confirmed that GOLM1 facilitated cholesterol efflux through upregulating ATP binding cassette transporter subfamily G member 5 (ABCG5). Mechanistically, GOLM1 translocated into nucleus to promote osteopontin (OPN) transcription, thus stimulating ABCG5-mediated cholesterol efflux. Moreover, GOLM1 was upregulated by interleukin-1β (IL-1β) in a dose-dependent manner. Finally, we confirmed that IL-1β, GOLM1, OPN, and ABCG5 were enhanced in livers of MASH patients with CGSs. Conclusions In MASH livers, upregulation of GOLM1 by IL-1β increases ABCG5-mediated cholesterol efflux in an OPN-dependent manner, promoting CGS formation. GOLM1 has the potential to be a molecular hub interconnecting MASH and CGSs.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background/Aims: Metabolic dysfunction-associated steatohepatitis (MASH) is a significant risk factor for gallstone formation, but mechanisms underlying MASH-related gallstone formation remain unclear. Golgi membrane protein 1 (GOLM1) participates in hepatic cholesterol metabolism and is upregulated in MASH. Here, we aimed to explore the role of GOLM1 in MASH-related gallstone formation. Methods: The UK Biobank cohort was used for etiological analysis. GOLM1 knockout (GOLM1-/-) and wild-type (WT) mice were fed with a high-fat diet (HFD). Livers were excised for histology and immunohistochemistry analysis. Gallbladders were collected to calculate incidence of cholesterol gallstones (CGSs). Biles were collected for biliary lipid analysis. HepG2 cells were used to explore underlying mechanisms. Human liver samples were used for clinical validation. Results: MASH patients had a greater risk of cholelithiasis. All HFD-fed mice developed MASH, and the incidence of gallstones was 16.7% and 75.0% in GOLM1-/- and WT mice, respectively. GOLM1-/- decreased biliary cholesterol concentration and output. In vivo and in vitro assays confirmed that GOLM1 facilitated cholesterol efflux through upregulating ATP binding cassette transporter subfamily G member 5 (ABCG5). Mechanistically, GOLM1 translocated into nucleus to promote osteopontin (OPN) transcription, thus stimulating ABCG5-mediated cholesterol efflux. Moreover, GOLM1 was upregulated by interleukin-1β (IL-1β) in a dose-dependent manner. Finally, we confirmed that IL-1β, GOLM1, OPN, and ABCG5 were enhanced in livers of MASH patients with CGSs. Conclusions In MASH livers, upregulation of GOLM1 by IL-1β increases ABCG5-mediated cholesterol efflux in an OPN-dependent manner, promoting CGS formation. GOLM1 has the potential to be a molecular hub interconnecting MASH and CGSs.