Chronic inflammation plays a pivotal role in the development of atherosclerosis, where the pro-inflammatory cytokine-induced expression of endothelial adhesion molecules and the recruitment of monocytes are the crucial events leading to its pathogenesis. Glossogyne tenuifolia ethanol extract (GTE) is shown to have potent anti-inflammatory and antioxidant activities. We evaluated the effects of GTE and its major components, luteolin (lut), luteolin-7-glucoside (lut-7-g), and oleanolic acid (OA) on TNF-α-induced expression of adhesion molecules in human umbilical vein endothelial cells (HUVECs). The results demonstrated that GTE, lut, and lut-7-g attenuated the expression of intercellular adhesion molecule-1 (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1) in TNF-α-activated HUVECs, and inhibited the adhesion of monocytes to TNF-α-activated HUVECs. The TNF-α-induced mRNA expression of ICAM-1 and VCAM-1 was also suppressed, revealing their inhibitory effects at the transcriptional level. Furthermore, GTE, lut, and lut-7-g blocked the TNF-α-induced degradation of nuclear factor-kB inhibitor (IkB), an indicator of the activation of nuclear factor-kB (NF-kB). In summary, GTE and its bioactive components were effective in preventing the adhesion of monocytes to cytokine-activated endothelium by the inhibition of expression of adhesion molecules, which in turn is mediated through blocking the activation and nuclear translocation of NF-kB. The current results reveal the therapeutic potential of GTE in atherosclerosis.

Objectives: Increased triglyceride glucose (TyG) index appears to be linked to carotid and coronary atherosclerosis and calcifications and possesses an elevated future risk of developing cardiovascular disease. Corrected QT (QTc) interval prolongation is associated with ventricular arrhythmias and sudden cardiac death, and a high prevalence of prolonged QTc interval was previously reported in blue-collar workers. The purpose of this study was to find the possible causal inter-relationship between TyG index and QTc interval in a large population of Chinese male steelworkers. Methods: A total of 3189 male workers from two steel plants were enrolled. They responded to a cross-sectional questionnaire on basic attributes and lifestyle, including sleep patterns. All workers in the two plants underwent periodic health checkups, including twelve-lead electrocardiography. Structural equation modeling (SEM) was used to assess the direct and indirect effects of TyG index on QTc interval. Results: With increasing TyG index tertile, the male steelworkers had an increased QTc interval. Applying multivariate analysis, TyG index was associated independently with the odds of QTc prolongation (adjusted odds ratio = 2.73, 95% confidence interval = 1.39–5.24, p = 0.004). SEM revealed that TyG index, hypertension, obesity, lifestyle, white blood cell (WBC) count, and liver function had statistically significant direct effects on QTc interval. Furthermore, TyG index also had an indirect effect on QTc interval through hypertension, obesity, WBC count, and liver function. Moreover, lifestyle had an indirect effect on QTc interval through TyG index. The final model explained 14% of the variability in QTc interval. Conclusions: An increased TyG index was associated with QTc interval prolongation in this study, and SEM delineated possible causal pathways and inter-relationships of the risk factors contributing to the occurrence of QTc prolongation among Chinese male steelworkers.

Objectives: Nonalcoholic fatty liver disease (NAFLD) has been associated with an increased risks of corrected QT (QTc) prolongation and left ventricular hypertrophy (LVH), both of which are associated with the development of cardiovascular disease. Rotating night shift work and a higher risk of incident NAFLD have been reported in male steelworkers. This study aimed to investigate the association of the severity of NAFLD with a prolonged QTc interval and LVH in a large cohort of Chinese male steelworkers. Methods: We examined baseline data of 2998 male steel workers aged 26 to 71 years at two plants. All workers at both plants received regular health assessments, including 12-lead ECG and echocardiography. Abdominal ultrasonography was performed to evaluate the severity of NAFLD. QTc prolongation was defined as follows: normal ≤ 430 ms, borderline 431–450 ms, and abnormal ≥ 451 ms. LVH was defined as a left ventricular mass index (LVMI) >131 g/m². Associations of NAFLD with an abnormal QTc interval and LVH were examined using univariate and multivariate analyses. Results: The QTc interval and the LVMI were significantly correlated with the NAFLD fibrosis score, and the severity of NAFLD was correlated with an abnormal QTc interval and LVH (p for trend < 0.05). Multivariate analysis showed that in comparison to the workers without NAFLD, the odds ratios of having an abnormal QTc interval and LVH were 2.54 (95% CI: 1.22–5.39, p = 0.013) times and 2.23 (95% CI: 1.02–5.01, p = 0.044) times higher in the workers with moderate/severe NAFLD. Conclusions: NAFLD may be closely associated with the risks of an abnormal QTc interval and LVH, suggesting that regular electrocardiogram and echocardiogram monitoring could be used to evaluate the risk of arrhythmia and LVH in male steelworkers with NAFLD.

This study aimd to assess recent trends in the control of low-density lipoprotein cholesterol (LDL-C) and the utilization of lipid-lowering drugs (LLD) among patients with atherosclerotic cardiovascular disease (ASCVD) in Taiwan. Patients with ASCVD and without a history of hemorrhagic stroke were identified from the Taiwanese Secondary Prevention for patients with AtheRosCLErotic disease (T-SPARCLE) Registry. ASCVD patients were stratified into four categories: those who ever had acute coronary syndrome (ACS), those who underwent percutaneous coronary intervention or coronary artery bypass grafting (PCI/CABG) without ACS, those who ever had an ischemic stroke (IS) without ACS or PCI/CABG, and other ASCVD cases. We assessed their latest recorded LDL-C levels for the periods 2015-16, 2017-18, and 2019-20. LLD therapy patterns were presented as monotherapy, dual therapy, or combination therapy of three or more drugs, with statin use classified by intensity. We identified 3831 ASCVD patients in 2015-16, 3531 in 2017-18, and 1231 in 2019-20. LLD utilization rose from 58.4% in 2015-16 to 73.2% in 2019-20. The proportions of patients achieving LDL-C goals in 2015-16, 2017-18, and 2019-20 were 21.5%, 25.8%, and 33.3% in the ACS cohort (goal <70 mg/dL); 20.4%, 26.1%, and 39.0% in the PCI/CABG cohort (goal <70 mg/dL); 54.4%, 58.5%, and 58.9% in the IS cohort (goal <100 mg/dL); and 60.0%, 65.5%, and 67.0% in the other ASCVD cohort (goal <100 mg/dL), respectively. Over half of the patients were prescribed moderate-intensity statins. Statin use, age, history of diabetes mellitus, and hypertension were important factors for attaining LDL-C goal in ACS patients. Despite improvements in LDL-C management observed over recent years, significant gaps remain in guideline adherence, especially for patients with ACS or PCI/CABG in Taiwan, with over 60% not meeting LDL-C targets. Intensifying efforts to align clinical practice with guidelines are imperative.

This research examines the application of non-invasive acoustic analysis for detecting obstructions in vascular access (fistulas) used by kidney dialysis patients. Obstructions in these fistulas can interrupt essential dialysis treatment. In this study, we utilized a condenser microphone to capture the blood flow sounds before and after angioplasty surgery, analyzing 3819 sound samples from 119 dialysis patients. These sound signals were transformed into spectrogram images to classify obstructed and unobstructed vascular accesses, that is fistula conditions before and after the angioplasty procedure. A novel lightweight two-dimension convolutional neural network (CNN) was developed and benchmarked against pretrained CNN models such as ResNet50 and VGG16. The proposed model achieved a prediction accuracy of 100%, surpassing the ResNet50 and VGG16 models, which recorded 99% and 95% accuracy, respectively. Additionally, the study highlighted the significantly smaller memory size of the proposed model (2.37 MB) compared to ResNet50 (91.3 MB) and VGG16 (57.9 MB), suggesting its suitability for edge computing environments. This study underscores the efficacy of diverse deep-learning approaches in the obstructed detection of dialysis fistulas, presenting a scalable solution that combines high accuracy with reduced computational demands.