STAT1 regulates neutrophil gelatinase B-associated lipocalin induction in influenza-induced myocarditis.

Influenza is a significant public health and economic threat around the world. Epidemiological studies have demonstrated a close association between influenza pandemics and cardiovascular mortality. Moreover, it has been shown that there is a decrease in cardiovascular mortality in high-risk patients following vaccination with the influenza vaccine. Here, we have investigated the role of anti-viral STAT1 signaling in influenza-induced myocarditis. Wild-type mice (C57BL/6) were infected with either influenza A/PR/8/34 or control, and cellular response and gene expression analysis from the heart samples were assessed 7 days later. The expression of interferon response genes STAT1, STAT2, Mx1, OASL2, ISG15, chemokines CCL2, CCL3, CXCL9 and CXCL10, and the frequency of neutrophils (CD45+CD11b+Ly6G+) and CD4+ T cells (CD45+CD4+) were all significantly increased in influenza-infected mice when compared to vehicle controls. These data suggest that influenza infection induces interferons, inflammatory chemokines, and cellular recruitment during influenza infection. We further investigated the role of STAT1 in influenza-induced myocarditis. The frequency of neutrophils and the levels of lipocalin 2 were significantly increased in STAT1-/- mice when compared to WT controls. Finally, we investigated the role of Lcn2 in viral-induced myocarditis. We found that in the absence of Lcn2, there was preserved cardiac function in Lcn2-/- mice when compared to WT controls. These data suggest that the absence of Lcn2 is cardioprotective during viral-induced myocarditis.

Sodium-Glucose Cotransporter-2 Inhibitor, Empagliflozin, Suppresses the Inflammatory Immune Response to Influenza Infection.

Influenza is a highly contagious, acute respiratory disease that causes significant public health and economic threats. Influenza infection induces various inflammatory mediators, IFNs, and recruitment of inflammatory cells in the host. This inflammatory "cytokine storm" is thought to play a role in influenza-induced lung pathogenesis. Empagliflozin is a drug primarily used to lower blood glucose in type II diabetes patients by inhibiting the sodium-glucose cotransporter-2 (SGLT-2) found in the proximal tubules in the kidneys. In this study, we have investigated the effects of empagliflozin on the pulmonary immune response to influenza infection. C57BL/6 mice (wild type) were infected with influenza A/PR/8/34 and treated with empagliflozin, and the disease outcomes were analyzed. Empagliflozin treatment decreased the expression of the inflammatory cytokines IL-1ß, IL-6, and CCL2; the percentage of inflammatory monocytes and inducible NO synthase-positive macrophages; and IFN response genes Stat1 and CXCL9 during influenza infection. Further, empagliflozin treatment decreases the expression of IL-6, CCL2, and CCL5 in RAW264.7 macrophages and bone marrow-derived macrophages. However, empagliflozin treatment increased influenza viral titer during infection. Despite fostering an increased viral burden, treatment with empagliflozin decreases the mortality in wild type and high fat diet-induced atherosclerotic LDLR-/- mice. Based on our findings, empagliflozin may have therapeutic implications for use in patients to prevent lung damage and acute respiratory illness.

Predicting Risk of Cardiac Arrest in Young Asian Americans: Insights from an Artificial Neural Network Analysis of the Nationwide Cohort.

We used the Artificial Neural Network (ANN) model to identify predictors of Sudden Cardiac Arrest (SCA) in a national cohort of young Asian patients in the United States. The National Inpatient Sample (2019) was used to identify young Asians (18-44-year-old) who were hospitalized with SCA. The neural network's predicted criteria for SCA were selected. After eliminating missing data, young Asians (n = 65,413) were randomly divided into training (n = 45,094) and testing (n = 19347) groups. Training data (70%) was used to calibrate ANN while testing data (30%) was utilized to assess the algorithm's accuracy. To determine ANN's performance in predicting SCA, we compared the frequency of incorrect prediction between training and testing data and measured the area under the Receiver Operating Curve (AUC). The 2019 young Asian cohort had 327,065 admissions (median age 32 years; 84.2% female), with SCA accounting for 0.21%. The exact rate of error in predictions vs. tests was shown by training data (0.2% vs 0.2%). In descending order, the normalized importance of predictors to accurately predict SCA in young adults included prior history of cardiac arrest, sex, age, diabetes, anxiety disorders, prior coronary artery bypass grafting, hypertension, congenital heart disease, income, peripheral vascular disease, and cancer. The AUC was 0.821, indicating an excellent ANN model for SCA prediction. Our ANN models performed excellently in revealing the order of important predictors of SCA in young Asian American patients. These findings could have a considerable impact on clinical practice to develop risk prediction models to improve the survival outcome in high-risk patients.