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Abstract Interleukin-6 (IL-6) plays a crucial role in the pathogenesis of cardiovascular disease (CVD), and IL-6 receptor (IL-6R) blockade has emerged as a promising therapeutic option. However, their specific therapeutic effects in different types of CVDs remain unclear. This study aimed to assess the efficacy of IL-6R blockade in the management of various CVDs, including hypertension (HTN), coronary heart disease (CHD), myocardial infarction (MI), atrial fibrillation (AF), and heart failure (HF). The Mendelian randomization (MR) approach was utilized to investigate the therapeutic impact of IL-6R blockade on HTN, CHD, MI, AF, and HF based on the genome-wide association study (GWAS) summary statistics. MR-Egger intercept test, Cochran's Q test, and leave-one-out analysis were used for sensitivity analysis to verify the reliability of the MR results. The Bonferroni method was used to correct for bias caused by multiple comparisons. Inverse variance weighted (IVW) results demonstrated that IL-6R blockade significantly influenced CHD (odds ratio (OR) = 0.757, 95% confidence interval (CI): 0.690 - 0.832, P = 5.804 × 10–9) and MI (OR = 0.840, 95% CI: 0.744 - 0.949, P = 0.005). However, IL-6R blockade had no significant effect on HTN (OR = 1.015, 95% CI: 0.950 - 1.084, P = 0.663), AF (OR = 0.905, 95% CI: 0.800 - 1.025, P = 0.116) and HF (OR = 1.012, 95% CI: 0.921 - 1.113, P = 0.805). Genetically predicted IL-6R blockade was associated with a protective effect on CHD and MI, but not HTN, AF and HF. This study's findings offer valuable insights for tailoring IL-6R blockade treatment for different types of CVD, and serve as a reference for future research.

BackgroundPrevious studies have shown an association between lipid-lowering drugs, circulating inflammatory factors, and atrial fibrillation (AF), but the specific effects of lipid-lowering drugs on AF and whether they can be mediated by circulating inflammatory factors remain unclear.MethodsWe collected 10 genetic variants encoding lipid-lowering drug targets (LDLR, HMGCR, PCSK9, NPC1L1, APOB, APOB, ABCG5, ABCG8, LPL, APOC3, and PPARA) and AF based on genome-wide association study (GWAS) summary statistics. Drug target Mendelian randomization (MR) was used to explore the causal relationship between lipid-lowering drugs and AF. In addition, we performed a mediation analysis of 91 circulating inflammatory factors to explore potential mediators. Sensitivity analyses were performed to verify the reliability of the MR Results by MR-Egger intercept test, Cochran's Q test and leave-one-out test.ResultsThe results of IVW method showed that LPL agonist had a protective effect on AF(OR = 0. 854, 95%CI: 0.816–0.894, P = 1.844E-11). However, the other nine lipid-lowering drug targets had no significant effect on AF. Notably, we found a mediator role of Fibroblast Growth Factor 5 (FGF5) in the protective effect of LPL agonist on AF with a mediator ratio of 9.22%. Sensitivity analyses supported the robustness of our findings, indicating a possible mediating pathway by which LPL agonists affect the risk of AF.ConclusionOur study provides new insights into the complex interactions among lipid-lowering agents, circulating inflammatory factors and AF, and also identified a potential mediating role of FGF5 in the pathogenesis of AF. Our findings highlight the potential of LPL agonists and targeting specific inflammatory factors for therapeutic intervention in AF, providing promising avenues for future research and clinical strategies for the management and prevention of AF.

The pilot directional protection based on amplitude comparison of the traveling wave (TW) has good performance in flexible DC transmission systems. However, this type of protection may fail to detect internal line faults correctly, as the amplitude of the forward voltage TW may be greater than that of the backward voltage TW in some fault cases. To address this issue, a novel pilot protection method based on the difference of fault TWs has been proposed. The proposed method fully considers the polarity of backward voltage TW and current TW. By utilizing the electrical quantity relationship at the line boundary, the difference between the two TWs mentioned above is transformed into the difference between the forward and backward voltage TWs. In this way, a conclusion is drawn that the absolute value of the integral ratio of forward and backward voltage TWs is less than 1 in the case of forward faults, and much greater than 1 when reverse faults occur. Therefore, a reliable directional identification criterion based on the forward and backward voltage TWs has been developed. Hardware-in-the-loop experiments and test results showcase that the proposed protection method has higher sensitivity and reliability for line faults.

Rapid epicentral distance estimation is of great significance for earthquake early warning (EEW). To rapidly and reliably predict epicentral distance, we developed machine learning models with multiple feature inputs for epicentral distance estimation using a single station and explored the feasibility of three machine learning methods, namely, Random Forest, eXtreme Gradient Boosting, and Support Vector Machine, for epicentral distance estimation. We used strong-motion data recorded by the Japanese Kyoshin network within a range of 1° (~112 km) from the epicenter to train machine learning models. We used 30 features extracted from the P-wave signal as inputs to the machine learning models and the epicentral distance as the prediction target of the models. For the same test data set, within 0.1-5 s after the P-wave arrival, the epicentral distance estimation results of these three machine learning models were similar. Furthermore, these three machine learning methods can obtain smaller mean absolute errors and root mean square errors, as well as larger coefficients of determination (R²), for epicentral distance estimation than traditional EEW epicentral distance estimation methods, indicating that these three machine learning models can effectively improve the accuracy of epicentral distance estimation to a certain extent. In addition, we analyzed the importance of different features as inputs to machine learning models using SHapley additive exPlanations. We found that using the top 15 important features as inputs, these three machine learning models can also achieve good results for epicentral distance estimation. Based on our results, we inferred that the machine learning models for estimating epicentral distance proposed in this study are meaningful in EEW. [ABSTRACT FROM AUTHOR]