Objective To investigate the causal relationship between hyperthyroidism and systemic lu-pus erythematosus(SLE).Methods According to the current summary data of genome-wide association studies(GWAS),the screened single nucleotide polymorphism(SNP)was selected as the instrumental varia-bles,hyperthyroidism served as the exposure factor and SLE as the outcome variable,and the Mendelian ran-domization analysis(MR)analysis method was used for conducting the study.Among them,the inverse vari-ance weighting(IVW)was the main MR analysis method,the MR-Egger regression method was used to the test for horizontal pleiotropy,and the sensitivity adopted the leave-one-method test,and the MR results con-ducted the visualized analysis by scatter plot,forest plot and funnel plot.Results Ten valid SNP were screened.In the MR analysis,IVW supported a causal relationship between hyperthyroidism and SLE(OR=1.838,95%CI:1.302-2.593,P<0.001);the MR-egger regression method supported the relationship be-tween hyperthyroidism and SLE(OR=4.070,95%CI:1.961-8.449,P=0.003);in addition,the weighted median method also supported the relationship between hyperthyroidism and SLE(OR=1.685,95%CI:1.238-2.294,P<0.001).Conclusion There appears to be a causal relationship between hyperthyroidism and SLE.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss, with few effective treatments currently available. The multifactorial nature of AD, shaped by genetic, environmental, and biological factors, complicates both research and clinical management. Recent advances in artificial intelligence (AI) and multi-omics technologies provide new opportunities to elucidate the molecular mechanisms of AD and identify early biomarkers for diagnosis and prognosis. AI-driven approaches such as machine learning, deep learning, and network-based models have enabled the integration of large-scale genomic, transcriptomic, proteomic, metabolomic, and microbiomic datasets. These efforts have facilitated the discovery of novel molecular signatures and therapeutic targets. Methods including deep belief networks and joint deep semi-non-negative matrix factorization have contributed to improvements in disease classification and patient stratification. However, ongoing challenges remain. These include data heterogeneity, limited interpretability of complex models, a lack of large and diverse datasets, and insufficient clinical validation. The absence of standardized multi-omics data processing methods further restricts progress. This review systematically summarizes recent advances in AI-driven multi-omics research in AD, highlighting achievements in early diagnosis and biomarker discovery while discussing limitations and future directions needed to advance these approaches toward clinical application.

Objective To investigate the relationship between biological aging and the rate of cognitive decline in middle-aged and elderly populations.Methods Longitudinal tracking data of cognitive function were obtained from the China Health and Retirement Longitudinal Study(CHARLS).We employed the Klemera and Doubal method(KDM)to estimate biological age(BA),and calculate the biological aging index(BAI)and biological aging type(BAT).A multivariate linear regression model was employed to analyze the relationships between baseline BAI,BAT,and cognitive function scores.Based on the baseline analysis,a mixed-effects model was used to examine the longitudinal associations between baseline BAI,BAT,and cognitive function during follow-up.Results A total of 5 897 participants were included in the study.BAI was found to be negatively associated with baseline cognitive function scores,with the partial regression coefficient(β)(95％CI)being-0.185(—0.231,—0.139)(P＜0.001).Compared with the lagged aging group,the premature aging group had lower cognitive function scores(β[95％CI]:—0.741[—0.966,—0.516]).For age and sex,for each additional year of baseline BAI,cognitive function scores declined by an average of 0.012(95％CI:—0.019,—0.005)points per year after adjusting for age and sex,and declined by 0.011(95％CI:—0.018,—0.004)points per year after adjusting for other covariates.Compared with participants with lagged aging,those with premature aging experienced,on average,an additional decline of 0.042(95％CI:—0.075,0.009)points per year in cognitive function scores after adjusting for age and sex alone,and by 0.039(95％CI:—0.072,—0.007)points per year after adjusting for other covariates.Conclusion Biological aging affects the rate of cognitive decline in middle-aged and elderly populations.A higher BAI is associated with a faster decline in cognitive function.Compared with those with lagged aging,individuals with premature aging exhibit a more rapid rate of cognitive decline.

Objective:To investigate the mechanism of polygonatum and astragalus compound(PA)in inhibiting the progression of lung adenocarcinoma. Methods:CCK-8 assay was used to assess the inhibitory rate of proliferation in A549 and H1299 cells treated with PA at different concentrations and to calculate the half maximal inhibitory concentration(IC50).C57BL/6 mice(KRASG12D/+;TP53flox/flox)were treated with adenovirus carrying Cre enzyme via nasal inhalation to establish a mouse model of primary lung adenocarcinoma.The model mice were fed with PA-containing diet to directly observe the effect of PA on the lung adenocarcinoma tissue.Immunohistochemical staining was used to examine the pathological status of the lung tissue.Bioinformatics analysis indicated that PA affects the progression of lung adenocarcinoma through the apelin-peroxisome proliferator-activated receptor gamma coactivator 1-alpha(PGC1α)-mitochondrial brown fat uncoupling protein 1(UCP1).Real-time quantitative PCR and Western blotting analysis were used to study the effect of PA on the mRNA and protein expression levels of apelin-PGC1α-UCP1 signaling pathway related genes.An ATP detection kit and flow cytometry were used to evaluate the effect of PA on the ATP and mitochondrial ROS production,respectively,in A549 and H1299 cells.siUCP1 was used to silent the expression of UCP1 while Z160 was used to induce UCP1 overexpression in A549 and H1299 cells,and the changes in ATP and mitochondrial ROS production were examined to further investigate whether PA acts on apelin-PGC1α-UCP1 signaling pathway to affect the progression of lung adenocarcinoma. Results:PA could obviously inhibit the proliferation of A549 and H1299 cells with the IC50 values of 10.66 mg/mL for A549 cells and 9.66 mg/mL for H1299 cells.In the mouse primary lung adenocarcinoma model,PA could effectively inhibit the growth of tumor,downregulate apelin-PGC1α-UCP1 signaling pathway and inhibit the expression of lung adenocarcinoma-promoting gene UCP1.In A549 and H1299 cells,PA could significantly inhibit the expression of apelin,PGC1α and UCP1(P＜0.05),promote the production of ATP(P＜0.000 1)and ROS,restore mitochondrial oxidative phosphorylation,and inhibit aerobic glycolysis(P＜0.01).UCP1 silencing could increase the production of ATP(P＜0.01)and mitochondrial ROS and decrease the expression of key glycolysis enzymes hexokinase 2(HK2)and pyruvate kinase isozyme type M2(PKM2)(P＜0.05).Increasing the expression of UCP1 could reduce the ATP production(P＜0.01)and mitochondrial ROS generation in cells while increase the expression of HK2 and PKM2(P＜0.05).Treating cells with PA and Z160 simultaneously(PA+Z160)could reverse the inhibitory effect of PA on the ATP production and glycolysis of tumor cells(P＜0.05). Conclusion:PA can downregulate the apelin-PGC1α-UCP1 signaling pathway,inhibit mitochondrial uncoupling,restore mitochondrial oxidative phosphorylation,inhibit aerobic glycolysis,reverse the Warburg effect,and thus inhibit lung adenocarcinoma progression.

Objective To develop a model based on a graph convolutional network(GCN)to achieve ef-ficient classification of the cold and hot medicinal properties of Chinese herbal medicines(CHMs).Methods After screening the dataset provided in the published literature,this study includ-ed 495 CHMs and their 8 075 compounds.Three molecular descriptors were used to repre-sent the compounds:the molecular access system(MACCS),extended connectivity finger-print(ECFP),and two-dimensional(2D)molecular descriptors computed by the RDKit open-source toolkit(RDKit_2D).A homogeneous graph with CHMs as nodes was constructed and a classification model for the cold and hot medicinal properties of CHMs was developed based on a GCN using the molecular descriptor information of the compounds as node features.Fi-nally,using accuracy and F1 score to evaluate model performance,the GCN model was ex-perimentally compared with the traditional machine learning approaches,including decision tree(DT),random forest(RF),k-nearest neighbor(KNN),Na?ve Bayes classifier(NBC),and support vector machine(SVM).MACCS,ECFP,and RDKit_2D molecular descriptors were al-so adopted as features for comparison.Results The experimental results show that the GCN achieved better performance than the traditional machine learning approach when using MACCS as features,with the accuracy and F1 score reaching 0.836 4 and 0.845 3,respectively.The accuracy and F1 score have increased by 0.869 0 and 0.812 0,respectively,compared with the lowest performing feature combina-tion OMER(only the combination of MACCS,ECFP,and RDKit_2D).The accuracy and F1 score of DT,RF,KNN,NBC,and SVM are 0.505 1 and 0.501 8,0.616 2 and 0.601 5,0.676 8 and 0.624 3,0.616 2 and 0.607 1,0.636 4 and 0.622 5,respectively.Conclusion In this study,by introducing molecular descriptors as features,it is verified that molecular descriptors and fingerprints play a key role in classifying the cold and hot medici-nal properties of CHMs.Meanwhile,excellent classification performance was achieved using the GCN model,providing an important algorithmic basis for the in-depth study of the"struc-ture-property"relationship of CHMs.

Glycogen storage disease (GSD) - type Ib is an autosomal recessive disease, often accompanied by inflammatory bowel disease. A case of GSD -Ib combined with Crohn′s disease-like enterocolitis is reported. The diagnosis and treatment of this patient is helpful to improve the understanding of this rare disease.

Glycogen storage disease (GSD) - type Ib is an autosomal recessive disease, often accompanied by inflammatory bowel disease. A case of GSD -Ib combined with Crohn′s disease-like enterocolitis is reported. The diagnosis and treatment of this patient is helpful to improve the understanding of this rare disease.

Objective:To explore the association between sleep duration, sleep quality and the prevalence of hypertension in the elderly aged 65 years and above.Methods:This study was conducted among the elderly in communities in Yiwu, China from April to July, 2019, and participants were recruited through physical examination in the hospital. Face-to-face interview was performed to obtain basic information. Sleep duration and sleep quality were evaluated by Pittsburgh Sleep Quality Index (PSQI). Associations between sleep duration, sleep quality and hypertension were evaluated by multivariate logistic regression analysis.Results:A total of 3 169 elderly persons, aged ≥65 years old, were included in the study. The overall prevalence of hypertension was 50.8%. The elderly with very poor sleep quality and short sleep duration accounted for 22.4% and 28.5%, respectively. After adjusting for demographic characteristics, socioeconomic status, lifestyle and health status, the OR of hypertension for the elderly with very poor sleep quality was 1.42 (95% CI: 1.12-1.80) compared with those with very good sleep quality. Compared with the elderly with sleep duration of 6-7 h a night, the OR of hypertension for those with sleep duration <6 h was 1.37 (95% CI: 1.15-1.65). As the sleep quality decreased, the risk for hypertension increased. An U-shaped association was found between sleep duration and risk of hypertension. Subgroup analyses showed that this association existed in both men and women, but only significant in the elderly aged <75 years. Conclusion:Poor sleep quality and short sleep duration were associated with risk for hypertension in the elderly.

Objective:To explore the influencing factors of job burnout of medical staff and provide reference for the formulation of intervention measures.Methods:From November to December, 2018, a questionnaire survey was conducted among medical staff in a general hospital by using the research design of the current situation survey. A total of 1193 questionnaires were distributed and 939 questionnaires were returned, with a recovery rate of 78.7%, including 891 valid questionnaires and an effective recovery rate of 94.9%. Social support rating scale (SSRs) was used to evaluate social support, and Maslach Burnout Scale (MBI-GS) was used to evaluate job burnout. Single factor analysis was performed by chi square test and Fisher exact probability method. To explore the influencing factors of job burnout by using disordered multi classification logistic.Results:The average age was (27.47 ± 4.22) years old, female accounted for 71.5% (637/891) . The total physical examination rate of job burnout was 46.6%. The scores of emotional exhaustion, cynicism and decreased sense of achievement were (10.10±3.75) , (6.14±3.43) , (17.91±4.13) respectively. Multiple logistic regression analysis showed that, compared with the non detected job burnout, the young, working for 1-3 years, average sleep ≤6 hours, and poor social support were more likely to have mild job burnout ( OR=0.91, 0.40, 2.25, 2.38, P<0.05) ; female, high night shift frequency in the past year, average sleep ≤6 h. Those with poor social support were more likely to have moderate to severe job burnout ( OR=1.59, 2.94, 4.01, 2.40, 3.66, P<0.05) . Conclusion:Corresponding measures should be taken to reduce job burnout and improve work efficiency.

Objective:To explore the influencing factors of job burnout of medical staff and provide reference for the formulation of intervention measures.Methods:From November to December, 2018, a questionnaire survey was conducted among medical staff in a general hospital by using the research design of the current situation survey. A total of 1193 questionnaires were distributed and 939 questionnaires were returned, with a recovery rate of 78.7%, including 891 valid questionnaires and an effective recovery rate of 94.9%. Social support rating scale (SSRs) was used to evaluate social support, and Maslach Burnout Scale (MBI-GS) was used to evaluate job burnout. Single factor analysis was performed by chi square test and Fisher exact probability method. To explore the influencing factors of job burnout by using disordered multi classification logistic.Results:The average age was (27.47 ± 4.22) years old, female accounted for 71.5% (637/891) . The total physical examination rate of job burnout was 46.6%. The scores of emotional exhaustion, cynicism and decreased sense of achievement were (10.10±3.75) , (6.14±3.43) , (17.91±4.13) respectively. Multiple logistic regression analysis showed that, compared with the non detected job burnout, the young, working for 1-3 years, average sleep ≤6 hours, and poor social support were more likely to have mild job burnout ( OR=0.91, 0.40, 2.25, 2.38, P<0.05) ; female, high night shift frequency in the past year, average sleep ≤6 h. Those with poor social support were more likely to have moderate to severe job burnout ( OR=1.59, 2.94, 4.01, 2.40, 3.66, P<0.05) . Conclusion:Corresponding measures should be taken to reduce job burnout and improve work efficiency.