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Interleukin-37 (IL-37) is unique in the IL-1 family since it broadly suppresses innate immunity and elevates in humans with inflammatory and autoimmune diseases. IL-37 shows definite groups and transcripts for human IL37 gene, but it is still not completely understood the effect and mechanisms of inflammatory response in endothelial cells. It is well accepted that endothelial dysfunction caused by inflammation is a key initiating event in atherosclerotic plaque formation, which leads to the occurrence and development of the cardiovascular adverse events in clinical since the inflammatory responses of endothelial cells could induce and enhance the deposition of extensive lipid and the formation of atherosclerotic plaque in the intima. Thus, it is essential to investigate the role and potential mechanisms in endothelial inflammatory response to prevent the formation and development of many cardiovascular diseases including atherosclerosis. So far, the recent studies have revealed that IL-37 is able to inhibit inflammatory response by suppressing the TLR2-NF-κB-ICAM-1 pathway intracellularly in human coronary artery endothelial cells (HCAECs). Further, the role of IL-37 may be related to the IL-18 pathway extracellularly and involved in the adhesion and transmigration of neutrophils in HCAECs.

Background. Diabetes mellitus (DM) can induce cardiomyocyte injury and lead to diabetic cardiomyopathy (DCM) which presently has no specific treatments and consequently increase risk of mortality. Objective. To characterize the therapeutic effect of 6-gingerol (6-G) on DCM and identify its potential mechanism. Methods. In vivo streptozotocin- (STZ-) induced DM model was established by using a high-fat diet and STZ, followed by low-dose (25 mg/kg) and high-dose (75 mg/kg) 6-G intervention. For an in vitro DCM model, H9c2 rat cardiomyoblast cells were stimulated with high glucose ( glucose = 33  mM) and palmitic acid (100 μM) and then treated with 6-G (100 μM). Histological and echocardiographic analyses were used to assess the effect of 6-G on cardiac structure and function in DCM. Western blotting, ELISA, and real-time qPCR were used to assess the expression of ferroptosis, inflammation, and the Nrf2/HO-1 pathway-related proteins and RNAs. Protein expression of collagen I and collagen III was assessed by immunohistochemistry, and kits were used to assay SOD, MDA, and iron levels. Results. The results showed that 6-G decreased cardiac injury in both mouse and cell models of DCM. The cardiomyocyte hypertrophy and interstitial fibrosis were attenuated by 6-G treatment in vivo and resulted in an improved heart function. 6-G inhibited the expression of ferroptosis-related protein FACL4 and the content of iron and enhanced the expression of anti-ferroptosis-related protein GPX4. In addition, 6-G also diminished the secretion of inflammatory cytokines, including IL-1β, IL-6, and TNF-α. 6-G treatment activated the Nrf2/HO-1 pathway, enhanced antioxidative stress capacity proved by increased activity of SOD, and decreased MDA production. Compared with in vivo, 6-G treatment of H9c2 cells treated with high glucose and palmitic acid could produce a similar effect. Conclusion. These findings suggest that 6-G could protect against DCM by the mechanism of ferroptosis inhibition and inflammation reduction via enhancing the Nrf2/HO-1 pathway.

Background Suboptimal health status (SHS) is a reversible state between ideal health and illness and it can be effectively reversed by risk prediction, disease prevention, and personalized medicine under the global background of predictive, preventive, and personalized medicine (PPPM) concepts. More and more Chinese nurses have been troubled by psychological symptoms (PS). The correlation between PS and SHS is unclear in nurses. The purpose of current study is to investigate the prevalence of SHS and PS in Chinese nurses and the relationship between SHS and PS along with predisposing factors as well as to discuss the feasibility of improving health status and preventing diseases according to PPPM concepts in Chinese nurses. Methods A cross-sectional study was conducted with the cluster sampling method among 9793 registered nurses in Foshan city, China. SHS was evaluated with the Suboptimal Health Status Questionnaire-25 (SHSQ-25). Meanwhile, the PS of depression and anxiety were evaluated with Self-Rating Depression Scale (SDS) and Self-Rating Anxiety Scale (SAS) self-assessment questionnaires. The relationship between PS and SHS in Chinese nurses was subsequently analyzed. Results Among the 9793 participants, 6107 nurses were included in the final analysis. The prevalence of SHS in the participants was 74.21% (4532/6107) while the symptoms of depression and anxiety were 47.62% (2908/6107) and 24.59% (1502/6107) respectively. The prevalence of SHS in the participants with depression and anxiety was significantly higher than those without the symptoms of depression (83.3% vs 16.7%, P < 0.001) and anxiety (94.2% vs 5.8%, P < 0.0001). The ratio of exercise habit was significantly lower than that of non-exercise habit (68.8% vs 78.4%, P < 0.001) in SHS group. Conclusions There is a high prevalence of SHS and PS in Chinese nurses. PS in Chinese nurses are associated with SHS. Physical exercise is a protective factor for SHS and PS so that the exercise should be strongly recommended as a valuable preventive measure well in the agreement with PPPM philosophy. Along with SDS and SAS, SHSQ-25 should also be highly recommended and applied as a novel predictive/preventive tool for the health measures from the perspectives of PPPM in view of susceptible population and individual screening, the predisposition to chronic disease preventing, personalization of intervention, and the ideal health state restoring.

Background: The existing prediction models lack the generalized applicability for chronic heart failure (CHF) readmission. We aimed to develop and validate a widely applicable nomogram for the prediction of 180-day readmission to the patients.Methods: We prospectively enrolled 2,980 consecutive patients with CHF from two hospitals. A nomogram was created to predict 180-day readmission based on the selected variables. The patients were divided into three datasets for development, internal validation, and external validation (mean age: 74.2 ± 14.1, 73.8 ± 14.2, and 71.0 ± 11.7 years, respectively; sex: 50.2, 48.8, and 55.2% male, respectively). At baseline, 102 variables were submitted to the least absolute shrinkage and selection operator (Lasso) regression algorithm for variable selection. The selected variables were processed by the multivariable Cox proportional hazards regression modeling combined with univariate analysis and stepwise regression. The model was evaluated by the concordance index (C-index) and calibration plot. Finally, the nomogram was provided to visualize the results. The improvement in the regression model was calculated by the net reclassification index (NRI) (with tenfold cross-validation and 200 bootstraps).Results: Among the selected 2,980 patients, 1,696 (56.9%) were readmitted within 180 days, and 1,502 (50.4%) were men. A nomogram was established by the results of Lasso regression, univariate analysis, stepwise regression and multivariate Cox regression, as well as variables with clinical significance. The values of the C-index were 0.75 [95% confidence interval (CI): 0.72–0.79], 0.75 [95% CI: 0.69–0.81], and 0.73 [95% CI: 0.64–0.83] for the development, internal validation, and external validation datasets, respectively. Calibration plots were provided for both the internal and external validation sets. Five variables including history of acute heart failure, emergency department visit, age, blood urea nitrogen level, and beta blocker usage were considered in the final prediction model. When adding variables involving hospital discharge way, alcohol taken and left bundle branch block, the calculated values of NRI demonstrated no significant improvements.Conclusions: A nomogram for the prediction of 180-day readmission of patients with CHF was developed and validated based on five variables. The proposed methodology can improve the accurate prediction of patient readmission and have the wide applications for CHF.