(±)-Penicithrones A-D (1a/1b-4a/4b), four novel pairs of anthrone-cyclopentenone heterodimers characterized by a distinctive bridged 6/6/6-5 tetracyclic core skeleton, together with three previously identified compounds (5-7), were isolated from the crude extract of the mangrove-derived fungus Penicillium sp., guided by heteronuclear single quantum correlation (HSQC)-based small molecule accurate recognition technology (SMART 2.0) and liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based molecular networking. The structural elucidation of new compounds was accomplished through comprehensive spectroscopic analysis, and their absolute configurations were determined using DP4+ ¹³C nuclear magnetic resonance (NMR) calculations and electronic circular dichroism (ECD) calculations. Compounds 1a/1b-4a/4b demonstrated moderate cytotoxicity against three human cancer cell lines HeLa, HCT116 and MCF-7 with half maximal inhibitory concentration (IC₅₀) values ranging from 15.95 ± 1.64 to 28.56 ± 2.59 μmol·L^-1.
Humans ; Penicillium/chemistry* ; Molecular Structure ; Cyclopentanes/isolation & purification* ; Cell Line, Tumor ; Antineoplastic Agents/pharmacology* ; Tandem Mass Spectrometry ; Dimerization ; HeLa Cells ; Magnetic Resonance Spectroscopy

Objective:To discuss the influencing factors of angina pectoris in the patients with diabetes mellitus(DM),to construct a Bayesian network model to explore the network relationships among the influencing factors,and to predict the risk of angina pectoris in the patients with DM.Methods:Based on the UK Biobank(UKB)database,the Logistic regression aralysis model was used to screen the influencing factors of angina pectoris in the patients with DM.The taboo search algorithm was used for structure learning,and the Bayesian parameter estimation method was used for parameter learning to construct the Bayesian network model.Results:A total of 22 712 DM patients were included.The influencing factors of angina pectoris in the patients with DM included 14 variables:gender,age,body mass index(BMI),triglycerides(TG),total cholesterol(TC),glycated hemoglobin(HbA1c),hypertension,maternal smoking around delivery,smoking status,alcohol consumption,regular exercise,insomnia,sleep duration,and childhood relative body size(P<0.05).A Bayesian network model was constructed with 15 nodes and 22 directed edges.Among them,age,HbA1c,hypertension,regular exercise,BMI,and sleep duration were directly associated with the occurrence of angina pectoris in the patients with DM,while gender,smoking status,alcohol consumption,TC,TG,insomnia,childhood relative body size,and maternal smoking around delivery were indirectly associated with the occurrence of angina pectoris in the patients with DM.Conclusion:Age,HbA1c,hypertension,regular exercise,BMI,and sleep duration are direct influencing factors of angina pectoris in the patients with DM.Controlling HbA1c,blood pressure,and BMI levels,engaging in regular exercise,and maintaining appropriate sleep duration are beneficial for reducing the risk of angina pectoris in the patients with DM.

Objective:To screen the Alzheimer's disease(AD)-related genes and construct its diagnostic model using bioinformatics technology and machine learning(ML)algorithms,to discuss the immunological characteristics of AD patients,and to provide novel biomarkers for AD diagnosis.Methods:The AD-related gene expression dataset GSE125583 was downloaded from the Gene Expression Omnibus(GEO)database.Differentially expressed genes(DEGs)were identified through differential analysis.Gene Ontology(GO)functional enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway enrichment analyses were performed to explore the biological functions and signaling pathways of DEGs.A protein-protein interaction(PPI)network was constructed,and hub genes were screened using Cytoscape software combined with three ML algorithms:Least Absolute Shrinkage and Selection Operator(LASSO),eXtreme Gradient Boosting(XGBoost),and Random Forest(RF).The screened hub genes were utilized to build an AD diagnostic model via RF,followed by feature importance ranking.The model's efficacy and key genes were evaluated using a test set.Single-sample gene set enrichment analysis(ssGSEA)was used for immune cell infiltration analysis between AD group and control group.Results:Differential analysis identified 1 287 DEGs.The GO functional enrichment analysis results revealed that DEGs were primarily involved in biological functions related to neural signaling,synapses,and vesicles.KEGG signaling pathway enrichment analysis indicated significant enrichment of DEGs in ion transport,neurotransmitter,and ligand-gated channel pathways.Nine overlapping hub genes were screened by the three ML algorithms.In the AD diagnostic model,the top four key genes with highest diagnostic performance were adenylate cyclase-activating polypeptide 1(ADCYAP1),brain-derived neurotrophic factor(BDNF),platelet-derived growth factor receptor β(PDGFRB),and C-X-C motif chemokine receptor 4(CXCR4),with corresponding area under the curve(AUC)values of 0.852,0.795,0.820,and 0.756,respectively.The model achieved an AUC of 0.828,accuracy of 81.25%,sensitivity of 84.40%,and specificity of 71.43%.The immune cell infiltration analysis results demonstrated higher infiltration of macrophages,monocytes,natural killer(NK)cells,and lymphocytes in AD tissue.Among these,NK/natural killer T(NKT)cells and plasmacytoid dendritic cells showed significant correlations with the four key genes(P<0.05).Conclusion:The feature genes screened based on bioinformatics and ML exhibit diagnostic potential for AD.Genes such as ADCYAP1 may serve as potential biomarkers for AD diagnosis,offering significant implications for early prevention and treatment.

Objective:To carry out a study on the cost analysis of the clinical use of Artificial Intelligence-assisted Diagnosis Technology for Coronary CT Angiography(CCTA-AI)to explore the cost differences and cost effects of Coronary CT Angiography(CCTA)examinations before and after the introduction of Artificial Intelligence(AI),and analyze the impact of the application of AI technology on the high-quality development of public hospitals.Methods:The operation cost method was used to measure the changes in the cost and efficiency of CCTA examinations before and after the application of AI technology in five sample hospitals in Beijing,and diagnostic accuracy was used as the effect value to calculate the cost effect of CCTA-AI diagnosis versus CCTA-manual diagnosis,and to analyze the main factors affecting the unit cost.Results:The average cost of examination in 5 sample hospitals after the application of AI-assisted diagnosis system was 1 074.90 yuan,and 1 266.61 yuan before the application,with a large difference between institutions.The cost-effectiveness analysis based on diagnostic accuracy showed that the AI group had an absolute advantage over the manual group,with a cost of 1 074 900 yuan for the AI group and an effectiveness of 855.05 persons,and a cost of 1 266 610 yuan for the physician group,with an effectiveness of 815.07 persons for the high-year-end physician group,and an effectiveness of 793.40 persons for the low-year-end physician group;0.46 man-hours could be saved for each patient examined;the unit cost of CCTA examination was affected by a number of factors,among which"the number of annual examinations"and"the number of CT units involved in CCTA examination"had the greatest influence on the unit cost of CCTA examination.Conclusion:The application of AI-assisted diagnostic technology can promote the improvement of quality and efficiency in public hospitals in a certain extent,and help optimize the overall distribution of medical resources at the system level.In the future,the cost analysis of AI technology should be further strengthened to comprehensively assess its actual contribution to the healthcare system.

Objective:To carry out a study on the cost analysis of the clinical use of Artificial Intelligence-assisted Diagnosis Technology for Coronary CT Angiography(CCTA-AI)to explore the cost differences and cost effects of Coronary CT Angiography(CCTA)examinations before and after the introduction of Artificial Intelligence(AI),and analyze the impact of the application of AI technology on the high-quality development of public hospitals.Methods:The operation cost method was used to measure the changes in the cost and efficiency of CCTA examinations before and after the application of AI technology in five sample hospitals in Beijing,and diagnostic accuracy was used as the effect value to calculate the cost effect of CCTA-AI diagnosis versus CCTA-manual diagnosis,and to analyze the main factors affecting the unit cost.Results:The average cost of examination in 5 sample hospitals after the application of AI-assisted diagnosis system was 1 074.90 yuan,and 1 266.61 yuan before the application,with a large difference between institutions.The cost-effectiveness analysis based on diagnostic accuracy showed that the AI group had an absolute advantage over the manual group,with a cost of 1 074 900 yuan for the AI group and an effectiveness of 855.05 persons,and a cost of 1 266 610 yuan for the physician group,with an effectiveness of 815.07 persons for the high-year-end physician group,and an effectiveness of 793.40 persons for the low-year-end physician group;0.46 man-hours could be saved for each patient examined;the unit cost of CCTA examination was affected by a number of factors,among which"the number of annual examinations"and"the number of CT units involved in CCTA examination"had the greatest influence on the unit cost of CCTA examination.Conclusion:The application of AI-assisted diagnostic technology can promote the improvement of quality and efficiency in public hospitals in a certain extent,and help optimize the overall distribution of medical resources at the system level.In the future,the cost analysis of AI technology should be further strengthened to comprehensively assess its actual contribution to the healthcare system.

Objective:To carry out a study on the cost analysis of the clinical use of Artificial Intelligence-assisted Diagnosis Technology for Coronary CT Angiography(CCTA-AI)to explore the cost differences and cost effects of Coronary CT Angiography(CCTA)examinations before and after the introduction of Artificial Intelligence(AI),and analyze the impact of the application of AI technology on the high-quality development of public hospitals.Methods:The operation cost method was used to measure the changes in the cost and efficiency of CCTA examinations before and after the application of AI technology in five sample hospitals in Beijing,and diagnostic accuracy was used as the effect value to calculate the cost effect of CCTA-AI diagnosis versus CCTA-manual diagnosis,and to analyze the main factors affecting the unit cost.Results:The average cost of examination in 5 sample hospitals after the application of AI-assisted diagnosis system was 1 074.90 yuan,and 1 266.61 yuan before the application,with a large difference between institutions.The cost-effectiveness analysis based on diagnostic accuracy showed that the AI group had an absolute advantage over the manual group,with a cost of 1 074 900 yuan for the AI group and an effectiveness of 855.05 persons,and a cost of 1 266 610 yuan for the physician group,with an effectiveness of 815.07 persons for the high-year-end physician group,and an effectiveness of 793.40 persons for the low-year-end physician group;0.46 man-hours could be saved for each patient examined;the unit cost of CCTA examination was affected by a number of factors,among which"the number of annual examinations"and"the number of CT units involved in CCTA examination"had the greatest influence on the unit cost of CCTA examination.Conclusion:The application of AI-assisted diagnostic technology can promote the improvement of quality and efficiency in public hospitals in a certain extent,and help optimize the overall distribution of medical resources at the system level.In the future,the cost analysis of AI technology should be further strengthened to comprehensively assess its actual contribution to the healthcare system.

Objective:To carry out a study on the cost analysis of the clinical use of Artificial Intelligence-assisted Diagnosis Technology for Coronary CT Angiography(CCTA-AI)to explore the cost differences and cost effects of Coronary CT Angiography(CCTA)examinations before and after the introduction of Artificial Intelligence(AI),and analyze the impact of the application of AI technology on the high-quality development of public hospitals.Methods:The operation cost method was used to measure the changes in the cost and efficiency of CCTA examinations before and after the application of AI technology in five sample hospitals in Beijing,and diagnostic accuracy was used as the effect value to calculate the cost effect of CCTA-AI diagnosis versus CCTA-manual diagnosis,and to analyze the main factors affecting the unit cost.Results:The average cost of examination in 5 sample hospitals after the application of AI-assisted diagnosis system was 1 074.90 yuan,and 1 266.61 yuan before the application,with a large difference between institutions.The cost-effectiveness analysis based on diagnostic accuracy showed that the AI group had an absolute advantage over the manual group,with a cost of 1 074 900 yuan for the AI group and an effectiveness of 855.05 persons,and a cost of 1 266 610 yuan for the physician group,with an effectiveness of 815.07 persons for the high-year-end physician group,and an effectiveness of 793.40 persons for the low-year-end physician group;0.46 man-hours could be saved for each patient examined;the unit cost of CCTA examination was affected by a number of factors,among which"the number of annual examinations"and"the number of CT units involved in CCTA examination"had the greatest influence on the unit cost of CCTA examination.Conclusion:The application of AI-assisted diagnostic technology can promote the improvement of quality and efficiency in public hospitals in a certain extent,and help optimize the overall distribution of medical resources at the system level.In the future,the cost analysis of AI technology should be further strengthened to comprehensively assess its actual contribution to the healthcare system.

Objective:To carry out a study on the cost analysis of the clinical use of Artificial Intelligence-assisted Diagnosis Technology for Coronary CT Angiography(CCTA-AI)to explore the cost differences and cost effects of Coronary CT Angiography(CCTA)examinations before and after the introduction of Artificial Intelligence(AI),and analyze the impact of the application of AI technology on the high-quality development of public hospitals.Methods:The operation cost method was used to measure the changes in the cost and efficiency of CCTA examinations before and after the application of AI technology in five sample hospitals in Beijing,and diagnostic accuracy was used as the effect value to calculate the cost effect of CCTA-AI diagnosis versus CCTA-manual diagnosis,and to analyze the main factors affecting the unit cost.Results:The average cost of examination in 5 sample hospitals after the application of AI-assisted diagnosis system was 1 074.90 yuan,and 1 266.61 yuan before the application,with a large difference between institutions.The cost-effectiveness analysis based on diagnostic accuracy showed that the AI group had an absolute advantage over the manual group,with a cost of 1 074 900 yuan for the AI group and an effectiveness of 855.05 persons,and a cost of 1 266 610 yuan for the physician group,with an effectiveness of 815.07 persons for the high-year-end physician group,and an effectiveness of 793.40 persons for the low-year-end physician group;0.46 man-hours could be saved for each patient examined;the unit cost of CCTA examination was affected by a number of factors,among which"the number of annual examinations"and"the number of CT units involved in CCTA examination"had the greatest influence on the unit cost of CCTA examination.Conclusion:The application of AI-assisted diagnostic technology can promote the improvement of quality and efficiency in public hospitals in a certain extent,and help optimize the overall distribution of medical resources at the system level.In the future,the cost analysis of AI technology should be further strengthened to comprehensively assess its actual contribution to the healthcare system.

Objective:To carry out a study on the cost analysis of the clinical use of Artificial Intelligence-assisted Diagnosis Technology for Coronary CT Angiography(CCTA-AI)to explore the cost differences and cost effects of Coronary CT Angiography(CCTA)examinations before and after the introduction of Artificial Intelligence(AI),and analyze the impact of the application of AI technology on the high-quality development of public hospitals.Methods:The operation cost method was used to measure the changes in the cost and efficiency of CCTA examinations before and after the application of AI technology in five sample hospitals in Beijing,and diagnostic accuracy was used as the effect value to calculate the cost effect of CCTA-AI diagnosis versus CCTA-manual diagnosis,and to analyze the main factors affecting the unit cost.Results:The average cost of examination in 5 sample hospitals after the application of AI-assisted diagnosis system was 1 074.90 yuan,and 1 266.61 yuan before the application,with a large difference between institutions.The cost-effectiveness analysis based on diagnostic accuracy showed that the AI group had an absolute advantage over the manual group,with a cost of 1 074 900 yuan for the AI group and an effectiveness of 855.05 persons,and a cost of 1 266 610 yuan for the physician group,with an effectiveness of 815.07 persons for the high-year-end physician group,and an effectiveness of 793.40 persons for the low-year-end physician group;0.46 man-hours could be saved for each patient examined;the unit cost of CCTA examination was affected by a number of factors,among which"the number of annual examinations"and"the number of CT units involved in CCTA examination"had the greatest influence on the unit cost of CCTA examination.Conclusion:The application of AI-assisted diagnostic technology can promote the improvement of quality and efficiency in public hospitals in a certain extent,and help optimize the overall distribution of medical resources at the system level.In the future,the cost analysis of AI technology should be further strengthened to comprehensively assess its actual contribution to the healthcare system.

To explore the effectiveness of "near peer learning" (NPL) in the electromyography(EMG)teaching module for neurology residents.