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.

Objective:To construct multimorbidity patterns among elderly individuals with chronic diseases and to explore the relationship between these patterns and frailty.Methods:A cross-sectional study was conducted involving 4, 706 elderly participants aged 60 years and older from selected prefecture-level cities in Shaanxi Province.Data were collected on general information, chronic diseases, and frailty status.The average age of the participants was 69.9±6.7 years, with males comprising 47.3%(2, 255 cases)and females comprising 52.7%(2, 481 cases)of the sample.Latent class analysis(LCA)was employed to identify multimorbidity patterns, while multivariate logistic regression analysis was utilized to examine the associations between these patterns and frailty.Results:The prevalence of multimorbidity within the study population was found to be 43.6%(2, 052 cases out of 4, 706 cases).The highest rates of multimorbidity were observed in anxiety and depression(100%, 23 cases out of 23 cases), dementia(100%, 6 cases out of 6 cases), and Parkinson's disease(100%, 11 cases out of 11 cases).Stroke followed closely with a rate of 96.8%(597 cases out of 617 cases), while rheumatoid arthritis exhibited the lowest rate of multimorbidity with other chronic diseases at 50%(4 cases out of 8 cases).Five distinct multimorbidity patterns were identified through LCA: the complex multimorbidity class(123 cases), the stroke-respiratory class(546 cases), the sleep disorders-osteoarticular class(488 cases), the cardiovascular-metabolic class(987 cases), and the relatively healthy class(2, 562 cases).When compared to the relatively healthy class, the complex multimorbidity class( OR=2.317, 95% CI: 1.573-3.412), stroke-respiratory class( OR=2.279, 95% CI: 1.862-2.788), sleep disorders-osteoarticular class( OR=1.370, 95% CI: 1.111-1.691), and cardiovascular-metabolic class( OR=1.185, 95% CI: 1.003-1.400)were all found to be significantly associated with frailty. Conclusions:The cardiovascular-metabolic class is the most prevalent among elderly individuals.Various patterns exhibit distinct associations with frailty, with the complex multimorbidity class and the stroke-respiratory class being the most significant, as they markedly elevate the risk of frailty.

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 structures, 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 molecules (1,050), including chemical space coverage, physicochemical properties, and absorption, distribution, 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 pyridazinones, 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 heterocycles, 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.

Objective:To construct multimorbidity patterns among elderly individuals with chronic diseases and to explore the relationship between these patterns and frailty.Methods:A cross-sectional study was conducted involving 4, 706 elderly participants aged 60 years and older from selected prefecture-level cities in Shaanxi Province.Data were collected on general information, chronic diseases, and frailty status.The average age of the participants was 69.9±6.7 years, with males comprising 47.3%(2, 255 cases)and females comprising 52.7%(2, 481 cases)of the sample.Latent class analysis(LCA)was employed to identify multimorbidity patterns, while multivariate logistic regression analysis was utilized to examine the associations between these patterns and frailty.Results:The prevalence of multimorbidity within the study population was found to be 43.6%(2, 052 cases out of 4, 706 cases).The highest rates of multimorbidity were observed in anxiety and depression(100%, 23 cases out of 23 cases), dementia(100%, 6 cases out of 6 cases), and Parkinson's disease(100%, 11 cases out of 11 cases).Stroke followed closely with a rate of 96.8%(597 cases out of 617 cases), while rheumatoid arthritis exhibited the lowest rate of multimorbidity with other chronic diseases at 50%(4 cases out of 8 cases).Five distinct multimorbidity patterns were identified through LCA: the complex multimorbidity class(123 cases), the stroke-respiratory class(546 cases), the sleep disorders-osteoarticular class(488 cases), the cardiovascular-metabolic class(987 cases), and the relatively healthy class(2, 562 cases).When compared to the relatively healthy class, the complex multimorbidity class( OR=2.317, 95% CI: 1.573-3.412), stroke-respiratory class( OR=2.279, 95% CI: 1.862-2.788), sleep disorders-osteoarticular class( OR=1.370, 95% CI: 1.111-1.691), and cardiovascular-metabolic class( OR=1.185, 95% CI: 1.003-1.400)were all found to be significantly associated with frailty. Conclusions:The cardiovascular-metabolic class is the most prevalent among elderly individuals.Various patterns exhibit distinct associations with frailty, with the complex multimorbidity class and the stroke-respiratory class being the most significant, as they markedly elevate the risk of frailty.

Objective:To study the complete genome characterization of Human Astrovirus (HAstV) in Shandong Province.Methods:Stool samples from acute flaccid paralysis (AFP) surveillance in Shandong Province from 2020 to 2022 were collected, and HAstV nucleic acid was examined by real-time quantitative PCR (qPCR). Next-generation sequencing (NGS) was conducted for the positive samples to obtain complete genome sequences and identify the genotype. Homology comparison and phylogenetic analysis were performed by using BioEdit and Mega software.Results:A total of 667 samples were examined by qPCR, of which 14 were HAstV-positive (2.1%), including HAstV-1 ( n=6), MLB1 ( n=6), MLB2 ( n=1), and VA2 ( n=1). The complete genome sequences were obtained from 11 samples. The six HAstV-1 sequences of this study had 98.2% to 99.9% nt similarities with each other and 87.6% to 98.6% with those from other regions. The four MLB1 sequences of this study had 99.1% to 99.9% nt similarities with each other and 92.2% to 99.4% with those from other regions. The VA2 sequence of this study had 96.0% to 96.3% nt similarities with those from other regions. Phylogenetic analysis based on ORF2 region showed that the local HAstV-1 sequences were most closely related to Japanese strains, and had distinct topology with phylogenies based on ORF1a and ORF1b regions. Conclusion:The complete genome sequences of 11 HAstV strains are obtained, and the VA2 complete genome is found.

Objective To study the mechanism of Chaihu Guizhi Decoction(CGD)in the treatment of influenza and staphylococcus aureus co-infection.Methods The co-infection model of influenza and staphylococcus aureus was established and CGD was used to intervene.The chemical components of CGD were qualitatively analyzed by UPLC-Q-Exactive/MS technology.The potential action targets of chemical components in CGD and the related targets of influenza Staphylococcus aureus co-infection were mined by network pharmacology method.The"component target disease"network was constructed.Core targets were selected according to degree ranking.Core action pathways were enriched by KEGG analysis and GO annotation analysis.The core target was verified by RT-qPCR,and the interaction between the core component and the key target was verified by molecular docking.Results CGD could significantly improve the decrease of body weight and thymus index(P<0.05)caused by co-infection.The lung index(P<0.05),relative amount of MmRNA expression(P<0.05)and bacterial load(P<0.05)were decreased,and the survival rate was improved.51 chemical constituents were identified from CGD.Through network pharmacological analysis,107 related targets corresponding to CGD treatment of bacterial pneumonia secondary to influenza were excavated.TNF,AKT1,ALB,VEGFA,MAPK3,PTGS2,STAT3,EGFR and other targets with strong correlation,mainly involved Fc epsilon RI signal pathway,GnRH signal pathway,NF-κB signal path,etc.Molecular docking study showed that the main active component of CGD,including oroxyloside,baicalein and wogonin have strong affinity with TNF,PTGS2 and EGFR targets.Compared with co-infection model group,in CGD group TNF-α、EGFR and PTGS2 increased significantly(P<0.05).Conclusion The main active ingredient of CGD is oroxyloside,baicalein and wogonin.TNF-α,PTGS2,EGFR and other targets to played a role in the treatment of influenza staphylococcus aureus co-infection.

OBJECTIVE To distinguish Anoectochilus roxburghii and relative species by near infrared(NIR) spectroscopy combined with chemometrics, and to establish a prediction model for rapid determine polysaccharides contents in Anoectochilus roxburghii. METHODS The NIR spectroscopy of Anoectochilus roxburghii, Anoectochilus formosanus Hayata and Ludisia discolor were collected. The prepossessing of original spectrum was optimization through accuracy of classification in the NIR model, and six supervised pattern recognition algorithms such as decision tree, K-nearest neighbor algorithm, random forest, partial least squares regression discriminant analysis, linear discriminant analysis and support vector machine(SVM) were applied to identify effect of the classification effect, optimum algorithm and then establish qualitative model. The content of polysaccharides in 76 batches of Anoectochilus roxburghii samples were examined by ultraviolet visible spectrophotometry combined with phenol sulfuric acid method. In order to select optimization algorithm, six quantitative stoichiometry algorithms consisted of SVM, extreme learning machines, decision trees, random forests, principal component regression and partial least squares regression(PLS) were used to connect polysaccharide content and the NIR spectroscopy in Anoectochilus roxburghii respectively. The best method for determining the content of Anoectochilus roxburghii polysaccharides was further optimized by spectra pretreatment, band selection and number of band variables based on successive projection algorithm(SPA). RESULTS The NIR discriminant analysis model was established by SVM with SNV+SG+2ndD, and the classification accuracy was best. The prediction performance was evaluated based on the radial basis kernel function algorithm combined with confusion matrix and ROC curve, and the model performance was good. In addition, the quantitative analysis model was constructed by continuous projection-partial least squares by the prepossessing of SNV+SG+2ndD and the optimal band of 7 000-4 000 cm-1 with 97 of variables number. The accuracy was 0.992, which was the highest. The root mean square error calibration set, correlation coefficient of calibration set, and the root mean square error in validation set, correlation coefficient of validation set were 0.625, 0.993, 0.767, 0.992, separately. The prediction deviation was 8.467, and relative deviation of prediction set was less than 10%. CONCLUSION The established NIR-SVM qualitative model and SPA-PLS quantitative model are accurate and reliable, which are enable to identify Anoectochilus roxburghii and determine polysaccharide content nondestructively. It is a new and promising method for rapid evaluation of Anoectochilus roxburghii quality.

Objective:To explore the clinical value of coronary artery calcification (CAC) detected by chest CT in early screening of coronary atherosclerotic heart disease (CAHD) in civil pilots.Methods:The physical examination data of 2 899 civil pilots were retrospectively analyzed. Pilots were divided into CAHD group and control group based on the results of coronary angiography (CAG). The health data were compared between 2 groups and the clinical value of CAC in the diagnosis of CAHD was analyzed by using binary Logistic regression model and receiver operating characteristic (ROC) curve.Results:Thirty-eight CAHD cases were diagnosed, and the remaining 2 861 were in the control group. Comparing to that of control group, the average age of the pilots in CAHD group was greater ( t=12.09, P<0.001), and the average total flying hours were longer ( Z=-7.68, P<0.001). The proportions of smoking, hyperlipidemia, diabetes, hypertension, fatty liver, obesity, carotid plaques, positive or suspiciously positive in submaximal treadmill exercise test, CAC, as well as the proportions of taking further requested coronary CT angiography and CAG were significantly higher in the CAHD group ( χ2=5.42-1 430.25, P<0.01 or <0.05). Logistic regression model showed that smoking ( OR=2.800, 95% CI: 1.074-7.301, P=0.035), obesity ( OR=3.336,95% CI:1.243-8.956, P=0.017), positive or suspiciously positive in submaximal treadmill exercise test ( OR=17.669, 95% CI: 2.923-106.756, P=0.002) and CAC ( OR=96.039, 95% CI: 11.439-806.396, P<0.001) were the independent risk factors for diagnosing CAHD. The ROC curve results suggested that the sensitivity and specificity of CAC for predicting CAHD was 97.4% and 93.1%, respectively, and the area under the ROC curve was 0.952 ( P<0.001). Conclusions:CAC detected by chest CT in physical examination is helpful for early screening of asymptomatic or atypical CAHD in civil pilots.

Objective:To explore the clinical value of coronary artery calcification (CAC) detected by chest CT in early screening of coronary atherosclerotic heart disease (CAHD) in civil pilots.Methods:The physical examination data of 2 899 civil pilots were retrospectively analyzed. Pilots were divided into CAHD group and control group based on the results of coronary angiography (CAG). The health data were compared between 2 groups and the clinical value of CAC in the diagnosis of CAHD was analyzed by using binary Logistic regression model and receiver operating characteristic (ROC) curve.Results:Thirty-eight CAHD cases were diagnosed, and the remaining 2 861 were in the control group. Comparing to that of control group, the average age of the pilots in CAHD group was greater ( t=12.09, P<0.001), and the average total flying hours were longer ( Z=-7.68, P<0.001). The proportions of smoking, hyperlipidemia, diabetes, hypertension, fatty liver, obesity, carotid plaques, positive or suspiciously positive in submaximal treadmill exercise test, CAC, as well as the proportions of taking further requested coronary CT angiography and CAG were significantly higher in the CAHD group ( χ2=5.42-1 430.25, P<0.01 or <0.05). Logistic regression model showed that smoking ( OR=2.800, 95% CI: 1.074-7.301, P=0.035), obesity ( OR=3.336,95% CI:1.243-8.956, P=0.017), positive or suspiciously positive in submaximal treadmill exercise test ( OR=17.669, 95% CI: 2.923-106.756, P=0.002) and CAC ( OR=96.039, 95% CI: 11.439-806.396, P<0.001) were the independent risk factors for diagnosing CAHD. The ROC curve results suggested that the sensitivity and specificity of CAC for predicting CAHD was 97.4% and 93.1%, respectively, and the area under the ROC curve was 0.952 ( P<0.001). Conclusions:CAC detected by chest CT in physical examination is helpful for early screening of asymptomatic or atypical CAHD in civil pilots.

Chronic subdural hematoma (cSDH) occurs in acute subdural hemorrhage after head trauma or converts from effusion. Traditional treatment is based on conservative treatment and surgical drainage. The effective rate of conservative treatment is only 3%-18%; even with drilling drainage treatment, the recurrence rate is as high as 33%. Recently, the middle meningeal artery embolization technique based on pathological analysis can greatly reduce the recurrence rate, and the operation is simple and curative effect is exact. This article reviews the pathogenesis of cSDH and progress of interventional therapy.