Atherosclerosis (AS) is the killer of human health and longevity, which is majorly caused by oxidized lipoproteins that attack macrophages in the endarterium. The Shen-Hong-Tong-Luo (SHTL) formula has shown great clinical efficacy and vascular protective effect for over 30 years in China, to attenuate AS progression. However, its pharmacological mechanism needs more investigation. In this study, we first investigated the chemical composition of SHTL by fingerprint analysis using high-performance liquid chromatography. In primary mouse peritoneal macrophages induced by lipopolysaccharide (LPS), we found that SHTL pretreatment suppressed reactive oxygen species accumulation and reversed the increases of the inflammatory factors, TNF-α and IL-6. Moreover, lipid accumulation induced by oxidized low-density lipoprotein (Ox-LDL) in macrophages was inhibited by SHTL. Additionally, network pharmacology was used to predict the potential targets of SHTL as the PPAR-γ/LXR-α/ABCA1 signaling pathway, which was validated in macrophages and ApoE^-/- mice by histopathological staining, qPCR, and Western blot analysis. Importantly, the protective effect of SHTL in the LPS- and Ox-LDL-induced macrophages against inflammation and lipid accumulation was attenuated by GW9662, a PPAR-γ antagonist, which confirmed the prediction results of network pharmacology. In summary, these results indicated that SHTL pretreatment reduced inflammation and lipid accumulation of macrophages by activating the PPAR-γ/LXR-α/ABCA1 pathway, which may provide a new insight into the mechanism of SHTL in the suppression of AS progression.

Background: Coronary heart disease (CHD) is one of the common chronic diseases in clinical practice, often accompanied by inflammatory reactions. In recent years, the system inflammation response index (SIRI) has aroused researchers’ interest as a novel inflammatory biomarker. This study aims to explore the relationship between the SIRI and CHD through the National Health and Nutrition Examination Survey (NHANES) database.Methods: We conducted a cross-sectional study and analyzed participants aged 40 and above with complete data from the NHANES survey years 2007–2016. Logistic regression analysis was used in this study to explore the relationship between the risk of CHD and SIRI. Stratified subgroup analysis was conducted based on age, gender, race, education level, body mass index (BMI), smoking status, drinking, hypertension, diabetes and angina pectoris to evaluate the relationship between SIRI and CHD in different populations. Additionally, restricted cubic spline (RCS) analysis was employed to investigate whether there is a nonlinear association between SIRI and CHD.Results: A total of 6374 eligible participants were included, among whom 387 were diagnosed with CHD. The SIRI levels in the CHD group were significantly higher than those in the non-CHD group. After adjusting for potential confounders, an elevated SIRI level was associated with an increased risk of CHD, with an odds ratio of 1.12, 95% CI: (1.03, 1.22), P = 0.008. Subgroup analysis results indicated a significant interaction between SIRI and CHD among genders (P for interaction 0.05). The RCS analysis showed a significant linear relationship between SIRI and CHD (P for non-linearity >0.05), with an inflection point at 2.86.Conclusion: Our study indicates that an elevated system inflammation response index is associated with a higher risk of CHD. Particularly among women.

Machine learning (ML) models have been constructed to predict the risk of in-hospital mortality in patients with myocardial infarction (MI). Due to diverse ML models and modeling variables, along with the significant imbalance in data, the predictive accuracy of these models remains controversial. This study aimed to review the accuracy of ML in predicting in-hospital mortality risk in MI patients and to provide evidence-based advices for the development or updating of clinical tools. PubMed, Embase, Cochrane, and Web of Science databases were searched, up to June 4, 2024. PROBAST and ChAMAI checklist are utilized to assess the risk of bias in the included studies. Since the included studies constructed models based on severely unbalanced datasets, subgroup analyses were conducted by the type of dataset (balanced data, unbalanced data, model type). This meta-analysis included 32 studies. In the validation set, the pooled C-index, sensitivity, and specificity of prediction models based on balanced data were 0.83 (95 % CI: 0.795-0.866), 0.81 (95 % CI: 0.79-0.84), and 0.82 (95 % CI: 0.78-0.86), respectively. In the validation set, the pooled C-index, sensitivity, and specificity of ML models based on imbalanced data were 0.815 (95 % CI: 0.789-0.842), 0.66 (95 % CI: 0.60-0.72), and 0.84 (95 % CI: 0.83-0.85), respectively. ML models such as LR, SVM, and RF exhibit high sensitivity and specificity in predicting in-hospital mortality in MI patients. However, their sensitivity is not superior to well-established scoring tools. Mitigating the impact of imbalanced data on ML models remains challenging.

Cardiovascular disease (CVD) is the primary cause of death in humans. Atherosclerosis (AS) is the most common CVD and a major cause of many CVD-related fatalities. AS has numerous risk factors and complex pathogenesis, and while it has long been a research focus, most mechanisms underlying its progression remain unknown. Noncoding RNAs (ncRNAs) represent an important focus in epigenetics studies and are critical biological regulators that form a complex network of gene regulation. Abnormal ncRNA expression disrupts the normal function of tissues or cells, leading to disease development. A large body of evidence suggests that ncRNAs are involved in all stages of atherosclerosis, from initiation to progression, and that some are significantly differentially expressed during AS development, suggesting that they may be powerful markers for screening AS or potential treatment targets. Here, we review the role of ncRNAs in AS development and recent developments in the use of ncRNAs for AS-targeted therapy, providing evidence for ncRNAs as diagnostic markers and therapeutic targets.

Soluble suppression of tumorigenicity-2 (sST-2), a marker of myocardial fibrosis and remodeling, has been related to the development of atrial fibrillation (AF). The aim of this meta-analysis was to evaluate the relationship between baseline serum sST-2 levels and the risk of AF recurrence after ablation. Relevant observational studies were retrieved from PubMed, Web of Science, Embase, Wanfang and China National Knowledge Infrastructure (CNKI). A random-effects model was used to combine the data, accounting for between-study heterogeneity. Fourteen prospective cohorts were included. Pooled results showed higher sST-2 levels before ablation in patients with AF recurrence compared to those without AF recurrence (standardized mean difference = 1.15, 95% confidence interval [CI] = 0.67 to 1.63, P < 0.001; I2 = 92%). Meta-regression analysis suggested that the proportion of patients with paroxysmal AF (PaAF) was positively related to the difference in serum sST-2 levels between patients with and without AF recurrence (coefficient = 0.033, P < 0.001). Subgroup analysis showed a more remarkable difference in serum sST-2 levels between patients with and without AF recurrence in studies where PaAF was ≥ 60% compared to those where it was < 60% (P = 0.007). Further analyses showed that high sST-2 levels before ablation were associated with an increased risk of AF recurrence (odds ratio [OR] per 1 ng/mL increment of sST-2 =1.05, OR for high versus low sST-2 = 1.73, both P values < 0.05). In conclusion, high sST-2 baseline levels may be associated with an increased risk of AF recurrence after catheter ablation.