Prediction of all-cause mortality in coronary artery disease patients with atrial fibrillation based on machine learning models.

BACKGROUND: Machine learning (ML) can include more diverse and more complex variables to construct models. This study aimed to develop models based on ML methods to predict the all-cause mortality in coronary artery disease (CAD) patients with atrial fibrillation (AF). METHODS: A total of 2037 CAD patients with AF were included in this study. Three ML methods were used, including the regularization logistic regression, random forest, and support vector machines. The fivefold cross-validation was used to evaluate model performance. The performance was quantified by calculating the area under the curve (AUC) with 95% confidence intervals (CI), sensitivity, specificity, and accuracy. RESULTS: After univariate analysis, 24 variables with statistical differences were included into the models. The AUC of regularization logistic regression model, random forest model, and support vector machines model was 0.732 (95% CI 0.649-0.816), 0.728 (95% CI 0.642-0.813), and 0.712 (95% CI 0.630-0.794), respectively. The regularization logistic regression model presented the highest AUC value (0.732 vs 0.728 vs 0.712), specificity (0.699 vs 0.663 vs 0.668), and accuracy (0.936 vs 0.935 vs 0.935) among the three models. However, no statistical differences were observed in the receiver operating characteristic (ROC) curve of the three models (all P > 0.05). CONCLUSION: Combining the performance of all aspects of the models, the regularization logistic regression model was recommended to be used in clinical practice.

Neuromedin S increases L-type Ca(2+) channel currents through G(i)α-protein and phospholipase C-dependent novel protein kinase C delta pathway in adult rat ventricular myocytes.

Neuromedin S (NMS), a peptide structurally related to NMU, has been identified in the mammalian heart tissues. However to date, any role of NMS in cardiomyocytes and the relevant mechanisms still remain unknown. In this study, we identified a novel functional role of NMS in modulating L-type Ca(2+) channels in adult rat ventricular myocytes, in which NMU type 2 receptors (NMUR2), but not NMUR1, are endogenously expressed. We found that NMS at 0.1 µM reversibly increased I(Ba) by ~29.7%. Intracellular infusion of GDP-ß-S or a selective antibody raised against the G(i)-protein blocked the stimulatory effects of NMS. The classical and novel protein kinase C (nPKC) antagonist calphostin C or chelerythrine chloride, as well as the phospholipase C (PLC) inhibitor U73122, abolished NMS responses, whereas a classical PKC antagonist Gö6976 or a PKA antagonist PKI 5-24 had no such effects. Pretreatment of cells with PKC-δ specific inhibitor rottlerin or intracellular application of a PKC-δ-derived inhibitory peptide, δV1-1, abolished NMS responses, while an inactive control peptide had no effects. In summary, NMS acting through NMUR2 increases I(Ba) via a G(i)α-protein-dependent PKC-δ pathway in rat ventricular myocytes.

Farnesyl thiosalicylic acid prevents iNOS induction triggered by lipopolysaccharide via suppression of iNOS mRNA transcription in murine macrophages.

Inducible nitric oxide synthase (iNOS) is a molecule critical for the development of inflammation-associated disorders. Its induction should be tightly controlled in order to maintain cellular homeostasis. Upon lipopolysaccharide (LPS) stimulation, iNOS, in most settings, is induced by the activation of inhibitor of κB-α (IκB-α)-nuclear factor κB (NF-κB) signaling. Farnesyl thiosalicylic acid (FTS), a synthetic small molecule that is considered to detach Ras from the inner cell membrane, has been shown to exhibit numerous anti-inflammatory functions. However, it remains unclear whether and how it affects iNOS induction in macrophages. The present study addressed this issue in cultured macrophages and endotoxemic mice. Results showed that FTS pretreatment significantly prevented LPS-induced increases in iNOS protein and mRNA expression levels in murine cultured macrophages, which were confirmed in organs in vivo from endotoxemic mice, such as the liver and lung. Mechanistic studies revealed that FTS pretreatment did not affect IκB-α degradation and NF-κB activation in LPS-treated macrophages. The nuclear transport of the active NF-κB was also not affected by FTS. But FTS pretreatment reduced the binding of NF-κB to its DNA elements, and reduced NF-κB bindings to iNOS promoter inside LPS-treated macrophages. Finally, our results showed that FST pretreatment increased mouse survival rate compared to LPS alone treatment. Taken together, these results indicate that FTS attenuates iNOS induction in macrophages likely through inhibition of iNOS mRNA transcription, providing further insight into the molecular mechanism of action of FTS in inflammatory disorder therapy.