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.

Aim To explore the efficacy of levosimendan on hypoxia pulmonary hypertension through animal experiments, and to further explore the potential mechanism of action using network pharmacological methods and molecular docking technique. Methods The rat model of hypoxia pulmonary hypertension was constructed to detect right heart systolic pressure and right heart remodeling index. HE , Masson, and VG staining were core targets were screened out. GO and KEGG pathway enrichment analysis were performed using the DAVID database. Molecular docking of the core targets was performed with the AutoDock software. Results The results of animal experiments showed that levosimendan had obvious therapeutic effect on hypoxia pulmonary hypertension. The network pharmacology results showed that SRC, HSP90AA1, MAPK1, PIK3R1, AKT1, HRAS, MAPK14, LCK, EGFR and ESR1 used to analyze the changes of rat lung histopathology. Search the Swiss Target Prediction, DrugBank Online, BatMan, Targetnet, SEA, and PharmMapper databases were used to screen for drug targets. Disease targets were retrieved from the GeneCards, OMIM databases. The "drug-target-disease" network was constructed after identification of the two intersection targets. The protein interaction network was constructed and the were the key targets to play a therapeutic role. Molecular docking showed good docking of levosimendan with all the top five core targets with degree values. Conclusions Levosimendan may exert a therapeutic effect on hypoxia-induced pulmonary hypertension through multiple targets.

Objective:To analyze the positive detection rate, main genotypes of β-thalassemia in western region of Guangxi Zhuang Autonomous Region (referred to as Guangxi).Methods:Retrospective analysis of 26 189 individuals who underwent gene testing for thalassemia at the Affiliated Hospital of Youjiang Medical University for Nationalities from January 2013 to December 2019. Using the crossing breakpoint PCR (Gap-PCR) and reverse dot blot (RDB) techniques to detect Chinese common type of 7 kinds of α-thalassemia and 17 kinds of β-thalassemia genotypes, high-throughput sequencing(Sanger) was performed for suspected rare β-thalassemia. Gap-PCR was used for suspected deletion β-thalassemia types.Results:β-thalassemia was diagnosed in 4 495 (17.16%) of 26 189 samples. A total of 6 177 alleles of 20 types of β-thalassemia were detected, mainly CD17 (2 712 cases, 43.90%) and CD41-42 (2 240 cases, 36.26%), including 7 rare alleles: Gγ +( Aγδβ) 0, SEA-HPFH, Hb New York, Hb G-Taipei, Hb Hezhou, Hb G-Coushatta and IVS-Ⅱ-81. There were 3 903 case (86.83%) heterozygous, 273 case (6.07%) double heterozygous, and 319 case (7.10%) homozygous among 4 495 β-thalassaemia subjects. A total of 48 genotypes were detected. The two most common genotypes were CD17/β N (1 890 cases, 42.05%) and CD41-42/β N (1 212 cases, 26.96%), accounted for 69.01% (3 102/4 495). Seven rare genotypes were detected: Gγ +( Aγδβ) 0/β N in 3 cases, Hb New York/β N in 3 cases, Hb G-Taipei/β N in 2 cases, SEA-HPFH/β N, Hb Hezhou/β N, Hb G-Coushatta/β N and IVS-Ⅱ-81/β N in 1 case each. A total of 1 041 cases (3.97%, 1 041/26 189) of 116 types of αβ-thalassemia were detected, mainly -- SEA/αα composite CD17/β N (144 cases, 13.83%), followed by -α 3.7/αα composite CD17/β N (112 cases, 10.76%). Conclusions:Western region of Guangxi is a high prevalence area of β-thalassemia, CD17/β N and CD41-42/β N are the main genotypes. The variation spectrum of β-thalassemia is complex and diverse, with rich genotype.

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.

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.