Background Unhealthy lifestyle behaviors may be associated with an increased risk of cardiometabolic risk factor aggregation (CMRF≥ 2), and few studies have focused on the correlation between the two in occupational populations. Objective To investigate the current status of CMRF≥2 and the compliance of healthy lifestyle in male occupational personnel, explore the effect of lifestyle on cardiometabolic risk, and provide reference for formulating healthy behavior promotion strategies and reducing cardiometabolic risk in occupational populations. Methods The study subjects were selected from male workers who completed occupational health examinations at an occupational disease prevention and control hospital in Shanxi Province from May to December 2023, and 15125 study subjects aged 18−60 years were finally included according to pre-determined inclusion and exclusion criteria. All subjects received a series of assessments including questionnaires (basic information, lifestyle habits, and occupational factors), physical examination, laboratory tests, and six cardiometabolic risk factors (central obesity, hypertension, type 2 diabetes, hypertriglyceridemia, low-density lipoprotein cholesterolemia, and hyperuricemia). A healthy lifestyle score was calculated based on six behavioral factors (smoking, drinking, physical activity, diet, sleep, sedentary behavior) and 1 point for one positive healthy behavior. The enrolled workers were then divided into three groups according to their total scores: 0−1 points (poor group), 2−3 points (moderate group), and 4−6 points (good group). Logistic regression models were used to analyze the statistical associations between lifestyle and CMRF≥2. Results The median age of the 15125 male study participants was 40 years, and the positive rate of CMRF≥2 was 53.5%. Adherence to moderate alcohol consumption was the most compliant healthy behavior (79.4%), followed by current non-smoking (41.7%), while adherence to adequate sedentary behavior (23.6%) and healthy eating (21.1%) were relatively low, and only 17.4% were able to adhere to four or more healthy lifestyle behaviors. After adjusting for confounders, when compared with the poor group high lifestyle score was found to be a protective factor for CMRF≥2 (moderate group: OR=0.70, 95%CI: 0.63, 0.77; good group: OR=0.66, 95%CI: 0.58, 0.75). In stratified analyses across different job types (coal miners, auxiliary workers, ground workers, and others), occupational stress (with or without), shift patterns (day shift, night shift, or rotating shift), dust exposure (with or without), and noise exposure (with or without), individuals with higher lifestyle scores exhibited a consistently lower risk of CMRF≥2. There was an interaction between shift pattern and lifestyle on CMRF≥2 (P_interaction<0.05). Conclusion This occupational group has a high positive rate of CMRF≥2 and a high prevalence of poor lifestyles with low adherence to some healthy behaviors. High healthy lifestyle scores are associated with a lower risk of aggregation of cardiometabolic risk factors, which may be reduced through lifestyle interventions.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

Objective To investigate the application value of indobufen combined with clopidogrel in elderly patients with coronary heart disease after percutaneous coronary intervention(PCI)with aspirin contraindications.Methods Elderly patients with coronary heart disease with aspirin contraindications were selected as study subjects and divided into 2 groups by random number table method.The control group was given oral clopidogrel bisulfate tablet 75 mg,qd;the treatment group was additionally given oral indobufen tablet 200 mg,qd,and both groups were treated for 3 months.Cardiac function indexes,coagulation-fibrinolytic system indexes,platelet function indexes,vascular endothelial function indexes and microcirculation function indexes were compared between the two groups before and after treatment,and the incidence of MACE and adverse drug reactions were analyzed.Results In this trial,39 cases in both the treatment group and the control group were included in the statistical analysis.The total effective rate of treatment group and control group were 94.87％and 79.49％,respectively,and the total effective rate of treatment group were higher than that of control group(P＜0.05).After treatment,the left ventricular ejection fraction(LVEF)of treatment group and control group were(57.13±3.16)％and(55.65±3.01)％,and the left ventricular end-diastolic volume index(LVEDVI)were(61.29±3.46)and(63.78±3.12)mL·m-2,respectively;the cardiac index were(3.68±0.31)and(3.41±0.28)L·min-1·m-2,and the stroke output index(SVI)were(57.37±2.57)and(55.29±2.74)mL·m-2,respectively;plasminogen activator inhibitor-1(PAI-1)levels were(46.29±4.18)and(49.37±5.24)ng·mL-1;antithrombin Ⅲ(AT-Ⅲ)levels were(131.04±10.65)％and(120.95±9.73)％,respectively;tissue plasminogen activator(t-PA)levels were(0.54±0.09)and(0.46±0.10)U·mL-1;fibrinogen(FIB)levels were(3.52±0.61)and(4.03±0.59)g·L-1,respectively;PT were(15.43±0.65)and(14.92±0.57)s,respectively.Compared with control group,the above indexes in treatment group were statistically significant(all P＜0.05).In the treatment group,there were 1 case of malignant arrhythmia in the cardiovascular adverse event(MACE),and in the control group,there were 2 cases of acute myocardial infarction,3 cases of malignant arrhythmia,2 cases of target vessel revascularization,and 1 case of acute thrombus in the stent.The incidence of MACE in the treatment group and the control group were 2.56％and 20.51％,respectively;the difference were statistically significant(P＜0.05).Conclusion In elderly patients with coronary heart disease contraindicated with aspirin after PCI,indobufen combined with clopidogrel can improve the cardiac function and microcirculation function,improve coagulation and fibrinolysis function,reduce vascular endothelial function injury,and reduce the incidence of MACE.

Pharmaceutical cocrystals is an advanced technology to improve the physicochemical and biological properties of drugs. However, there are few studies on the in vivo metabolism of pharmaceutical cocrystals. In this study, the pharmacokinetics of wogonin cocrystal in normal rats was further studied on the basis of the previous preparation of wogonin-aloperine cocrystal. Firstly, an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was established for simultaneous determination of wogonin and its metabolite wogonoside in rat plasma, and to investigate the methodology. The method was applied to the pharmacokinetic study of wogonin-aloperine cocrystal (Wog-Alop) in rats. The results showed that wogonin and its metabolite wogonoside had a good linear relationship in the range of 1-800 ng·mL^-1, and the precision, accuracy, matrix effect and stability in this range met the requirements of biological analysis. Compared with direct administration of wogonin, the C_max of wogonin and its metabolite in rats increased to 7.44-fold and 9.15-fold, AUC_0-t increased to 1.67-fold and 3.72-fold, and oral bioavailability of wogonin increased to 187.66% after cocrystal administration. Wog-Alop cocrystal can significantly improve the C_max, AUC, and oral bioavailability of wogonin and its metabolite, which provides a new perspective for the clinical application of wogonin. This study was approved by the Experimental Animal Ethics Review Committee of Beijing University of Chinese Medicine (approval number: BUCM-2023032307-1148).

Objective To analyze the prevalence of chronic diseases and its influencing factors of dust-exposed male workers in a coal mine. Methods A total of 9 782 dust-exposed male workers from a coal mine in Shanxi Province were selected as the study subjects using the purposive sampling method. Their occupational health examination results were collected to analyze the prevalence of chronic diseases and its influencing factors. Results The prevalence of dyslipidemia, hyperuricemia, hypertension and diabetes were 40.3%, 30.7%, 23.5% and 5.6%, respectively. The prevalence of chronic diseases was 64.8%. Among them, the prevalence of having one, two, three or more chronic diseases were 36.5%, 21.6% and 6.7%, respectively. The prevalence of comorbid chronic diseases was 28.3%, with the highest prevalence of concurrent dyslipidemia and hyperuricemia of 11.0%. The results of binary logistic regression analysis showed that the risk of chronic disease was higher in workers <40 years old, smoking, overweight, obesity and total working years >20 years (all P<0.05). The results of multinomial logistic regression analysis showed that workers <40 years old, overweight, obesity and total working years >20 years were risk factors for having one chronic disease (all P<0.05). The workers <40 years old, smoking, overweight, obesity and total working years >20 years were risk factors for having two chronic diseases (all P<0.05). The workers <40 years old, smoking, alcohol consumption, overweight, obesity, other types of work, and working years >20 years were risk factors for having three or more chronic diseases (all P<0.05). Conclusion The prevalence of chronic diseases is high and the comorbidity of chronic diseases is common among dust-exposed male workers. The main influencing factors were age, smoking, alcohol consumption, overweight, obesity, type of work, and working year. Workers with more contributing factors have higher risk of chronic comorbidities.

BACKGROUND:MRI is important for the diagnosis of early knee osteoarthritis.MRI image recognition and intelligent segmentation of knee osteoarthritis using deep learning method is a hot topic in image diagnosis of artificial intelligence. OBJECTIVE:Through deep learning of MRI images of knee osteoarthritis,the segmentation of femur,tibia,patella,cartilage,meniscus,ligaments,muscles and effusion of knee can be automatically divided,and then volume of knee fluid and muscle content were measured. METHODS:100 normal knee joints and 100 knee osteoarthritis patients were selected and randomly divided into training dataset(n=160),validation dataset(n=20),and test dataset(n=20)according to the ratio of 8:1:1.The Coarse-to-Fine sequential training method was used to train the 3D-UNET network deep learning model.A Coarse MRI segmentation model of the knee sagittal plane was trained first,and the rough segmentation results were used as a mask,and then the fine segmentation model was trained.The T1WI and T2WI images of the sagittal surface of the knee joint and the marking files of each structure were input,and DeepLab v3 was used to segment bone,cartilage,ligament,meniscus,muscle,and effusion of knee,and 3D reconstruction was finally displayed and automatic measurement results(muscle content and volume of knee fluid)were displayed to complete the deep learning application program.The MRI data of 26 normal subjects and 38 patients with knee osteoarthritis were screened for validation. RESULTS AND CONCLUSION:(1)The 26 normal subjects were selected,including 13 females and 13 males,with a mean age of(34.88±11.75)years old.The mean muscle content of the knee joint was(1 051 322.94±2 007 249.00)mL,the mean median was 631 165.21 mL,and the mean volume of effusion was(291.85±559.59)mL.The mean median was 0 mL.(2)There were 38 patients with knee osteoarthritis,including 30 females and 8 males.The mean age was(68.53±9.87)years old.The mean muscle content was(782 409.18±331 392.56)mL,the mean median was 689 105.66 mL,and the mean volume of effusion was(1 625.23±5 014.03)mL.The mean median was 178.72 mL.(3)There was no significant difference in muscle content between normal people and knee osteoarthritis patients.The volume of effusion in patients with knee osteoarthritis was higher than that in normal subjects,and the difference was significant(P<0.05).(4)It is indicated that the intelligent segmentation of MRI images by deep learning can discard the defects of manual segmentation in the past.The more accuracy evaluation of knee osteoarthritis was necessary,and the image segmentation was processed more precisely in the future to improve the accuracy of the results.