Background With the prevalence of cardiovascular diseases increasing worldwide, early prediction and accurate assessment of heart failure (HF) risk are crucial to meet the clinical demand. Objective Our study objective was to develop machine learning (ML) models based on real-world electronic health records to predict 1-year in-hospital mortality, use of positive inotropic agents, and 1-year all-cause readmission rate. Methods For this single-center study, we recruited patients with newly diagnosed HF hospitalized between December 2010 and August 2018 at the First Affiliated Hospital of Dalian Medical University (Liaoning Province, China). The models were constructed for a population set (90:10 split of data set into training and test sets) using 79 variables during the first hospitalization. Logistic regression, support vector machine, artificial neural network, random forest, and extreme gradient boosting models were investigated for outcome predictions. Results Of the 13,602 patients with HF enrolled in the study, 537 (3.95%) died within 1 year and 2779 patients (20.43%) had a history of use of positive inotropic agents. ML algorithms improved the performance of predictive models for 1-year in-hospital mortality (areas under the curve [AUCs] 0.92-1.00), use of positive inotropic medication (AUCs 0.85-0.96), and 1-year readmission rates (AUCs 0.63-0.96). A decision tree of mortality risk was created and stratified by single variables at levels of high-sensitivity cardiac troponin I (<0.068 μg/L), followed by percentage of lymphocytes (<14.688%) and neutrophil count (4.870×109/L). Conclusions ML techniques based on a large scale of clinical variables can improve outcome predictions for patients with HF. The mortality decision tree may contribute to guiding better clinical risk assessment and decision making.

Doxorubicin therapy in mice (antitumor dosage) markedly enhanced platelet functions measured as agonist-induced platelet aggregation, degranulation, and adhesion to endothelial cells, actions leading to thrombus formation and thrombosis-independent vascular injury. Clopidogrel treatment ameliorated thrombus formation and vascular toxicity induced by doxorubicin via inhibiting platelet activity.

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ObjectivesInsulin resistance (IR) has been shown to play important role in the pathogenesis of type 2 diabetes mellitus (T2DM). There is an intricate interplay between IR, dyslipidemia, and serum uric acid (SUA) in people with and without diabetes. Physical activity has a positive impact on insulin sensitivity in insulin-resistant populations. However, the effect of different intensities of physical activity on insulin levels under different lipid indices and SUA levels is unclear.MethodsTo explore the association between physical activity and insulin, we enrolled 12,982 participants aged above 18 years from the National Health and Nutrition Examination Survey (NHANES) conducted between 2009 and 2018. Next, we conducted multivariate logistic regression analyses, generated fitted smoothing curves, and visualized the data using generalized additive models.ResultsIncreased intensities of physical activity can significantly reduce insulin levels. The association between physical activity and insulin persisted even after adjusting for confounding factors, with β value (95% CI) = −17.10 (−21.64, −12.56) in moderate group, β value (95% CI) = −28.60 (−33.08, −24.11) in high group, respectively. High-intensity physical activity significantly lowered insulin levels in the lower and higher SUA tertiles, and three tertiles of LDL-c, HDL-c, and TG. Moreover, the link between physical activity and insulin was stronger in male individuals.ConclusionThis study shows that physical activity can significantly lower insulin levels, and high-intensity physical activity still has additional potential benefits for insulin levels, even in the condition of dyslipidemia and hyperuricemia.

Uric acid is an effective antioxidant. Oxidized low-density lipoprotein (ox-LDL) is derived from circulating LDL and promotes atherosclerosis. The Keap1-Nrf2-ARE pathway is a key body pathway involved in protection against internal and external oxidative damages. The role of uric acid on vascular endothelial function damaged by ox-LDL, and its effect on the Keap1-Nrf2-ARE pathway has not been fully explored. HUVECs were treated with different concentrations of uric acid and ox-LDL to explore the effect of uric acid in vitro. Cell phenotype was determined by cytometry and Western blot. Nuclear translocation of Nrf2 was determined by immunofluorescence. Coimmunoprecipitation was used to determine the level of Nrf2 ubiquitination. A microfluidic device was used to mimic the vascular environment in the body, and the level of mRNA levels of inflammatory factors was determined by RT-PCR. The findings of this study show that suitable uric acid can significantly reduce endothelial damage caused by ox-LDL, such as oxidative stress, inflammation, and increased adhesion. In addition, uric acid reduced Nrf2 ubiquitination and increased nuclear translocation of Nrf2 protein, thus activating the Keap1-Nrf2-ARE pathway and playing a protective role. Interestingly, the effects of UA were significantly inhibited by administration of Brusatol, an inhibitor of Nrf2. In summary, suitable concentrations of uric acid can alleviate the oxidative stress level of endothelial cells through Nrf2 nuclear translocation and further protect cells from damage.