Numerous c-mesenchymal-epithelial transition(c-MET)inhibitors have been reported as potential anticancer agents.However,most fail to enter clinical trials owing to poor efficacy or drug resistance.To date,the scaffold-based chemical space of small-molecule c-MET inhibitors has not been analyzed.In this study,we constructed the largest c-MET dataset,which included 2,278 molecules with different struc-tures,by inhibiting the half maximal inhibitory concentration(IC50)of kinase activity.No significant differences in drug-like properties were observed between active molecules(1,228)and inactive mol-ecules(1,050),including chemical space coverage,physicochemical properties,and absorption,distri-bution,metabolism,excretion,and toxicity(ADMET)profiles.The higher chemical diversity of the active molecules was downscaled using t-distributed stochastic neighbor embedding(t-SNE)high-dimensional data.Further clustering and chemical space networks(CSNs)analyses revealed commonly used scaffolds for c-MET inhibitors,such as M5,M7,and M8.Activity cliffs and structural alerts were used to reveal"dead ends"and"safe bets"for c-MET,as well as dominant structural fragments consisting of pyr-idazinones,triazoles,and pyrazines.Finally,the decision tree model precisely indicated the key structural features required to constitute active c-MET inhibitor molecules,including at least three aromatic het-erocycles,five aromatic nitrogen atoms,and eight nitrogen-oxygen atoms.Overall,our analyses revealed potential structure-activity relationship(SAR)patterns for c-MET inhibitors,which can inform the screening of new compounds and guide future optimization efforts.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

As metabolic regulators of cellular functions,amino acids play a crucial role in maintaining glucose homeostasis,protein synthesis,reproduction,immunity,and hormone secretion.In recent years,the role of amino acid metabolism in the occurrence and development of glycolipid metabolism disorders has become a research hotspot.Glycolipid metabolism disorders represent the common core pathological mechanism of metabolic diseases such as obesity and diabetes,and also serve as risk factors for cardiovascular diseases and malignancies.This review focuses on the role of various amino acids in glycolipid metabolism disorders,elaborates the specific mechanism of amino acids in regulating the body's metabolism,and analyzes their potential roles in disease prevention,diagnosis and treatment,in order to provide reference for further research on the application of amino acids in related fields.

Objective:To investigate the incidence of shoulder work-related musculoskeletal disorders (WMSDs) among occupational population in China, and to explore their intrinsic association with personal and work-related factors.Methods:In April 2024, 73497 valid questionnaires of the Chinese version of the Musculoskeletal Disorders Electronic Questionnaire were retrospectively analyzed from June 2018 to December 2023 in 22 provinces and 29 key industries in China, and the general information, occurrence of WMSDs and related risk factors of key occupational populations in different regions in China were collected. By using Chi-square test and confirmatory factor analysis, the relationship between shoulder fatigue and pain in key occupational groups and individual factors, work type, work posture and work organization was discussed, and the internal relationship was analyzed based on structural equation model.Results:Higher incidence of shoulder fatigue and pain were associated with female, lack of physical exercise, uncomfortable working posture and neck leaning forward ( P<0.05). Structural equation model analysis showed that work type, work posture and work organization were strongly correlated ( r=0.58, 0.55). Work organization and work type were strongly correlated with shoulder fatigue ( r=0.65) and moderately correlated with shoulder fatigue ( r=0.21). Shoulder fatigue was moderately associated with shoulder pain ( r=0.40). Individual factors, work type, work posture and shoulder fatigue could directly affect shoulder pain ( OR=0.07, -0.09, 0.17 and 0.40), and work type and work posture could also indirectly affect shoulder pain through shoulder fatigue ( OR=0.08, 0.03). Work organization only indirectly affected shoulder pain through shoulder fatigue ( OR=0.26) . Conclusion:The main influencing factor of shoulder pain is shoulder fatigue, followed by work posture and individual factors. Structural equation model can better reflect the complex relationship between work type, work posture and work organization and shoulder WMSDs. Improving work posture and work organization may be an effective way to control the influence of shoulder fatigue on shoulder pain.

Objective:To explore the structural equation model to explore the levels of work-related musculoskeletal disorders (WMSDs) and various risk factors in the feet and ankle of China's occupational population, providing scientific basis for for preventing WMSDs in feet and ankles.Methods:Data of 73497 national occupational epidemiological cases were selected from June 2018 to December 2023 used the Chinese version of the Electronic Questionnaire on Musculoskeletal Disorders. The adverse ergonomic factors and their source classification standard and confirmatory factor analysis were used to investigate foot and ankle WMSDs and their related risk factors (including individual factors, work organization, work posture, work type, fatigue, etc.) in key occupational groups in China, and structural equation model hypothesis, fitting, verification, and path and intermediary effect analysis were carried out. The model fit evaluation indexes included Chi-square specific degrees of freedom ( χ2/ df), gauge fit index (NFI), Tucker Lewis index (TLI), goodness of Fit index (GFI), adjusted Goodness of Fit index (AGFI) and approximate root mean square error (RMSEA) . Results:A total of 73497 occupational workers were surveyed, with local muscle fatigue and WMSDs incidence rates in the feet and ankles being 17.17% and 12.06%, respectively. The fitting index of the adjusted structural equation model basically meets the standard (GFI=1, AGFI=1, RMESA=0.042, NFI=0.716, TLI=0.663). The top three factors affecting feet and ankle WMSDs are feet and ankle muscle fatigue, work type, and work organization, with standardized path coefficients of 0.221, 0.105, and 0.095, respectively. The top two factors affecting feet and ankle muscle fatigue are work organization and work type, with standardized path coefficients of 0.548 and 0.383, respectively. Feet and ankle muscle fatigue, work type, work organization, and work posture have a direct effect on feet and ankle WMSDs, with effect values of 0.221, 0.105, 0.095, and 0.077, respectively. The organization and type of work can also have indirect effects through feet and ankle muscle fatigue, with effect values of 0.121 and 0.084, respectively.Conclusion:Feet and ankle muscle fatigue has a direct impact on WMSDs, and plays a mediating role between ankle and ankle WMSDs caused by work organization and work type. Feet and ankle muscle fatigue is an important pathway leading to feet and ankle WMSDs. It is recommended that employers and managers detect job fatigue early and take corresponding prevention and intervention measures, which can play a key role in preventing feet and ankle WMSDs.

Objective:To explore the structural relationship between WMSDs in the upper limbs and various risk factors in the occupational population in China, based on a large sample epidemiological survey and structural equation analysis, and to establish a structural equation model, so as to lay a foundation for the prevention and control of such diseases.Methods:The Chinese version of the Musculoskeletal Disorders Electronic Questionnaire was used to conduct a nationwide survey on the prevalence of WMSDs in the upper extremity. Six factors related to WMSDs in the upper extremity were extracted by the classification standard of adverse ergonomic factors and their source and confirmatory factor analysis, including work organization, work type, upper extremity work posture, individual factors, upper extremity fatigue and upper extremity WMSDs. The structural equation analysis was carried out and the structural equation model was established.Results:The incidence of WMSDs and fatigue in the upper limbs was 24.44% and 43.76%, respectively. The adjusted structural equation model fitting indicators were generally up to the standard (GFI=1.000, AGFI=1.000, RMSEA=0.043, NFI=0.808, TLI=0.784) . The four exogenous latent variables of work organization, work type, upper limb work posture and individual factors were correlated. There was a strong positive correlation between job type and upper limb work posture ( r=0.865) , a moderate positive correlation between work organization and job type and upper limb work posture ( r=0.570, 0.490) , and a weak negative correlation between individual factors and the other three exogenous latent variables. Upper limb work posture and individual factors had direct effects on upper limb WMSDs, and the effect coefficients were 0.10 and 0.06, respectively. Upper limb fatigue played a mediating role between work organization, work type, upper limb work posture and upper limb WMSDs. The effect coefficient was 0.46, and the composition ratios of indirect effects were 100.0%, 100.0%, and 38.3%, respectively. The direct path effect of upper limb work posture, individual factors and upper limb WMSDs was weaker than the mediating path through upper limb fatigue. Conclusion:When carrying out the prevention and control of upper limbWMSDs, it is necessary to comprehensively consider the pathogenesis path of upper limb muscle fatigue and upper limb WMSDs caused by work organization, work type, and upper limb work posture, so as to provide theoretical reference for improving the prevention and control level of such diseases.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

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: 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: Based on the China-VHD database, this study sought to develop and validate a Valvular Heart Disease- specific Age-adjusted Comorbidity Index (VHD-ACI) for predicting mortality risk in patients with VHD. METHODS & RESULTS: The China-VHD study was a nationwide, multi-centre multi-centre cohort study enrolling 13,917 patients with moderate or severe VHD across 46 medical centres in China between April-June 2018. After excluding cases with missing key variables, 11,459 patients were retained for final analysis. The primary endpoint was 2-year all-cause mortality, with 941 deaths (10.0%) observed during follow-up. The VHD-ACI was derived after identifying 13 independent mortality predictors: cardiomyopathy, myocardial infarction, chronic obstructive pulmonary disease, pulmonary artery hypertension, low body weight, anaemia, hypoalbuminaemia, renal insufficiency, moderate/severe hepatic dysfunction, heart failure, cancer, NYHA functional class and age. The index exhibited good discrimination (AUC, 0.79) and calibration (Brier score, 0.062) in the total cohort, outperforming both EuroSCORE II and ACCI (P < 0.001 for comparison). Internal validation through 100 bootstrap iterations yielded a C statistic of 0.694 (95% CI: 0.665-0.723) for 2-year mortality prediction. VHD-ACI scores, as a continuous variable (VHD-ACI score: adjusted HR (95% CI): 1.263 (1.245-1.282), P < 0.001) or categorized using thresholds determined by the Yoden index (VHD-ACI ≥ 9 vs. < 9, adjusted HR (95% CI): 6.216 (5.378-7.184), P < 0.001), were independently associated with mortality. The prognostic performance remained consistent across all VHD subtypes (aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid valve disease, mixed aortic/mitral valve disease and multiple VHD), and clinical subgroups stratified by therapeutic strategy, LVEF status (preserved vs. reduced), disease severity and etiology. CONCLUSION The VHD-ACI is a simple 13-comorbidity algorithm for the prediction of mortality in VHD patients and providing a simple and rapid tool for risk stratification.