Background: Smoking is harmful, which has become a major public health burden. Physical activity may be related to smoking. Physical activity is one of the current methods for smoking control and smoking cessation. Different types of physical activity may have different effect on smoking behavior. Objective: The purpose of this study was to identify the direction and extent of the impact of different types of physical activity above moderate intensity (including work physical activity, recreational physical activity, commuter physical activity and sedentary behavior) on smoking behavior. Materials and methods: In this study, a total of 2,015 individuals (1,233 males and 782 females, mean age 54.02±17.31 years) was selected from the representative population aged 20 and above in the National Health and Nutrition Survey of the United States from 2017 to 2018. Physical activity was assessed using the Global Physical Activity Questionnaire (GPAQ) ; the tobacco use questionnaire (SMQ) was used to determine whether the sample had smoking behavior at this stage. Binary Logistic regression analysis was performed with various physical activities as independent variables and smoking behavior as dependent variables. All data were analyzed through Statistical Product and Service Solutions (SPSS) 26.0. Results: After adjusted for all confounding variables, physical activity at work was close to significantly associated with smoking behavior (P=0.053), odds ratio (OR) =1.135 (95%Cl: 0.999-1.289). Recreational physical activity was significantly associated with smoking behavior (P < 0.001), OR=0.729 (95%Cl: 0.639-0.832). Commuting physical activity was significantly associated with smoking behavior (P < 0.001), OR=1.214 (95%Cl:1.048-1.405). Sedentary behavior was significantly associated with smoking behavior (P < 0.001), OR=1.363 (95%Cl: 1.154-1.611). Conclusions: Given that different types of physical activity have different associations with smoking behavior. Therefore, when physical activity is used as a tobacco control measurement, it is necessary to pay attention to the type and environment of physical activity. Recreational physical activities should be appropriately increased, sedentary behavior should be reduced, and smoking prohibit environment should be expanded as far as possible to achieve better clinical intervention effects.

Myocardial infarction (MI), a type of coronary heart disease, has had a significantly increased incidence in recent years. The balance of cardiomyocyte apoptosis and autophagy after MI is one of the main determinants of patient prognosis. Both affect myocardial fibrosis and ventricular remodeling and regulate cell survival. However, there are few studies on the regulation mechanism of cardiomyocyte autophagy and apoptosis in the early stage after MI. In this study, based on analyzing the scRNA-seq and mRNA-seq data of mice in the early stage of MI, we found that the expression of S100a8 and S100a9 increased first and then decreased in the early stage of MI, and their expression level changed with the number of neutrophils. Further, through the functional enrichment analysis of the differentially expressed genes, we found that S100a8 and S100a9 were simultaneously associated with autophagy and apoptosis and could regulate autophagy and apoptosis of cardiomyocytes through MAPK or PI3K-AKT signaling pathways. This study provides valuable insights for clarifying the pathogenesis of early stage MI and improving its early treatment.

Background: Dilated cardiomyopathy (DCM) is a serious cardiac heterogeneous pathological disease, which may be caused by mutations in the LMNA gene. Lamins interact with not only lamina-associated domains (LADs) but also euchromatin by alone or associates with the lamina-associated polypeptide 2 alpha (LAP2α). Numerous studies have documented that LMNA regulates gene expression by interacting with LADs in heterochromatin. However, the role of LMNA in regulating euchromatin in DCM is poorly understood. Here, we determine the differential binding genes on euchromatin in DCM induced by LMNA mutation by performing an integrated analysis of bioinformatics and explore the possible molecular pathogenesis mechanism. Results: Six hundred twenty-three and 4484 differential binding genes were identified by ChIP-seq technology. The ChIP-seq analysis results and matched RNA-Seq transcriptome data were integrated to further validate the differential binding genes of ChIP-seq. Five and 60 candidate genes involved in a series of downstream analysis were identified. Finally, 4 key genes (CREBBP, PPP2R2B, BMP4, and BMP7) were harvested, and these genes may regulate LMNA mutation-induced DCM through WNT/β-catenin or TGFβ-BMP pathways. Conclusions: We identified four key genes that may serve as potential biomarkers and novel therapeutic targets. Our study also illuminates the possible molecular pathogenesis mechanism that the abnormal binding between LMNA or LAP2α-lamin A/C complexes and euchromatin DNA in LMNA mutations, which may cause DCM through the changes of CREBBP, PPP2R2B, BMP4, BMP7 expressions, and the dysregulation of WNT/β-catenin or TGFβ-BMP pathways, providing valuable insights to improve the occurrence and development of DCM. Graphic

This study showcases the successful development of a hybrid reduced graphene oxide-metal organic framework modified glassy carbon electrode (rGO-MOFs/GCE) electrochemical biosensor for the detection of synthetic testosterone (TST) as a performance-enhancing drug in athletes. The results demonstrate a linear relationship between the voltametric responses and the testosterone concentration, indicating the sensor's effectiveness in measuring TST. Furthermore, the sensor's performance in detecting TST in real human serum samples from athletes was also validated. The sensor's linear range and detection limit are comparable, if not superior, to those of other recently developed electrochemical sensors, positioning it as a promising tool for TST detection. Additionally, the method's selectivity was assessed and found to be unaffected by substances typically present in biological fluids and food samples, further affirming the sensor's reliability. The application of the rGO/MOF/GCE sensor to detecting and quantifying TST in actual human serum samples yielded favorable results. The synergistic effects of rGO and MOF, which improve the electrical conductivity and biocompatibility of MOF and the porosity and catalytic activity of rGO, are responsible for the sensor's enhanced performance in terms of sensitivity, selectivity, stability, and response time. These findings underscore the potential of the rGO/MOF/GCE sensor platform for various biomedical applications.

Numerous studies have shown that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can infect host cells through binding to angiotensin I converting enzyme 2 (ACE2) expressing in various tissues and organs. In this study, we deeply analyzed the single-cell expression profiles of ACE2 in fetal and adult human hearts to explore the potential mechanism of SARS-CoV-2 harming the heart. The molecular docking software was used to simulate the binding of SARS-CoV-2 and its variant spike protein with ACE2. The genes closely related to ACE2 in renin-angiotensin system (RAS) were identified by constructing a protein-protein interaction network. Through the analysis of single-cell transcription profiles at different stages of human embryos, we found that the expression level of ACE2 in ventricular myocytes was increased with embryonic development. The results of single-cell sequencing analysis showed that the expression of ACE2 in ventricular myocytes was upregulated in heart failure induced by dilated cardiomyopathy compared with normal hearts. The upregulation of ACE2 increases the risk of infection with SARS-CoV-2 in fetal and adult human hearts. We also further confirmed the expression of ACE2 and ACE2-related genes in normal and SARS-CoV-2-infected human pluripotent stem cell-derived cardiomyocytes. In addition, the pathway analysis revealed that ACE2 may regulate the differently expressed genes in heart failure through calcium signaling pathway and Wnt signaling pathway.