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Objective: Sirtuin (SIRT)1, as a molecular link between immunity and metabolic pathways, is a key immune response regulator. The significance of SIRT1 in peripheral blood mononuclear cells (PBMCs) of neuromyelitis optica spectrum disorder (NMOSD) has not been investigated. Here, we aimed to evaluate the SIRT1 mRNA level in PBMCs of patients with NMOSD and its clinical relevance and explore the potential mechanism of SIRT1 action. Methods: A total of 65 patients with NMOSD and 60 normal controls from North China were enrolled. Using real-time fluorescence quantitative-polymerase chain reaction, mRNA levels were detected in PBMCs, and protein levels were detected using western blotting. Results: Compared to the healthy controls and chronic-phase patients with NMOSD, SIRT1 mRNA and protein levels in PBMCs of NMOSD patients with acute attack were significantly downregulated (p< 0.0001). ∆EDSS scores (EDSS scores in the acute phase—EDSS scores before the recentattack) were higher in NMOSD patients with low SIRT1 mRNA level than in patients with high SIRT1 expression (p= 0.042). SIRT1 mRNA level in patients with acute-phase NMSOD was positively correlated with lymphocyte and monocyte counts and negatively correlated with neutrophil counts and the neutrophil-to-lymphocyte ratio. Furthermore, the transcription factor FOXP3 mRNA level was significantly positively correlated with the SIRT1mRNA level in PBMCs of patients with acute-phase NMOSD. Conclusions: Our study indicated that SIRT1 mRNA expression was downregulated in the PBMCs of patients with acute-phase NMOSD, and its level was correlated with the clinical parameters of the patients, suggesting a potential role of SIRT1 in NMOSD.

The electrophysiological characteristics of idiopathic ventricular arrhythmias (VAs) from the noncoronary sinus (NCS) have not been fully described.

Electrocardiogram (ECG) interpretation is an integral part of the clinical ECG workflow; however, this process is often time‐consuming and labor‐intensive. We aim to develop a rapid, inexpensive means to detect abnormal ECGs using artificial intelligence (AI) from scanned ECG printouts. The study included 1172 12‐lead ECG scans performed in 1172 individuals from a community in Guangzhou, China; 878 (74.9%) were diagnosed with sinus rhythm, and the remaining 294 (25.1%) with abnormal rhythms. A deep learning model consisting of a convolutional neural network based on InceptionV3 and a fully connected layer followed by a GEV activation was trained to classify scanned tracings as either normal or abnormal. In a hold‐out testing set, the model achieved a area under curve (AUC), sensitivity, specificity, PPV, and NPV of 0.932 (95% confidence interval [CI]: 0.890, 0.976), 0.816 (95% CI: 0.657, 0.923), 0.993 (95% CI: 0.959, 1.0), 0.969 (95% CI: 0.838, 0.999), and 0.950 (95% CI: 0.90, 0.980) respectively, when using a probability threshold of 0.5. When compared with a physiological expert, these results show comparable performance with a statistically significant increase in specificity and a non‐significant decrease in sensitivity at the 95% level. We have developed a rapid, inexpensive, accurate means to detect abnormal ECGs using AI. Easy and accurate identification of such “abnormal” ECGs could allow the mass automated review of ECGs in community settings where abnormal ones could be flagged using AI for detailed clinical review by healthcare professionals. We developed a deep learning algorithm that uses scanned ECG printouts to predict normal or abnormal rhythm. In addition, the method can produce saliency maps, showing which areas are relevant in the diagnosis to aid interpretability. The method has applications in low resource settings such as screening, where electrophysiological experts may not be available.

The Kirchhoff index is a novel distance-based topological index corresponding to networks, which is the sum of resistance distances between all pairs of nodes. It assumes a significant role in describing the flow of a network and can also characterize the stability of the network. The computation of the Kirchhoff index of a network is frequently performed through spectral analysis methods. However, for networks with irregular structures, this method may not be applicable. In this paper, we propose a polygon network model and calculate its Kirchhoff index by reconstructing the network construction process. Furthermore, by establishing the relationship between the known Kirchhoff index and the Laplacian spectrum of the network, we derive the Kirchhoff index of the network and its relationship with other network indices, such as the Global mean-first passage time and the average path length. We then perform calculations on these related indices to gain a more comprehensive understanding of the network. • The precise formula for the Kirchhoff index of Vicsek polygon networks is derived. • The relation between the Kirchhoff index and the first passage problem is established. • The ratio of resistance distance to distance is 2/3 for Vicsek 3-polygon. [ABSTRACT FROM AUTHOR]

In this paper, we study a hierarchical scale-free network with special fractal structure. We study the average path length and associate the average path length with the symbol length. Then using a recursive method, we obtain the asymptotic formula for average path length of our networks. [ABSTRACT FROM AUTHOR]

This study sought to determine the long-term outcomes of catheter ablation (CA) of ventricular tachycardia (VT) in a series of patients with arrhythmogenic right ventricular cardiomyopathy (ARVC) without background implantable cardioverter-defibrillator (ICD) therapy.

Biomarkers have been related to the arrhythmia recurrence following catheter ablation (CA) of atrial fibrillation (AF). We hypothesized that concurrent measurement of several biomarkers would additively improve their predictive value. One thousand four hundred and ten consecutive AF patients (68% male; 57.2 ± 11.6 years) undergoing CA were enrolled. Baseline characteristics, serum B type brain natriuretic peptide (BNP) and high sensitivity C reactive protein (hsCRP), estimated glomerular filtration rate (eGFR), ablation parameters, arrhythmia data at discharge, 1, 3, 6, and then every 6 months post CA were collected. Follow‐up ended when arrhythmia recurred or until 31st December 2016. Three hundred and sixty‐five (25.9%) patients had arrhythmia recurrence post‐CA during a mean follow‐up of 20.7 ± 8.8 months. BNP, hsCRP, and eGFR levels and their cut‐off values of 237.45 pg/mL, 1.6 mg/dL, and 82.5 mL/min/1.73 m2were good predictors for AF recurrence (all P< 0.01). On multivariate analysis, increasing BNP and hsCRP, decreasing eGFR, gender, and early recurrence (ER) were independent predictors of AF recurrence (all P< 0.01). Compared to BNP alone, BNP plus eGFR or both eGFR and CRP showed incrementally better predictive values (ROC comparisons, all P< 0.01). Similar findings were evident in the subgroups of patients with paroxysmal or nonparoxysmal AF. Measurement of BNP, CRP, and eGFR were incrementally additive to clinical risk factors in a cumulative manner to improve prediction of arrhythmia recurrence post‐CA of AF. The implications of poor arrhythmia outcome in AF patients with multiple abnormal biomarkers pre‐CA procedure may help with patient selection and inform the likelihood of success or the need of more complicated CA procedure(s).

This study aimed to evaluate the electrophysiological mechanisms of post-surgical atrial tachycardias (ATs) during mapping with an automated high-resolution mapping system (Rhythmia, Boston Scientific, Marlborough, Massachusetts).

In this paper, we construct the evolving networks from hollow cube in fractal geometry by encoding. We set the unit cubes as nodes of network, where two nodes are neighbors if and only if their corresponding cubes have common surface. We also study some characteristics of the network, such as degree distribution, clustering coefficient and average path length. We obtain this network with small world and scale-free properties by the self-similar structure. [ABSTRACT FROM AUTHOR]

Ventricular arrhythmias (VAs) originating from the anterosuperior left ventricular outflow tract (LVOT) represent a challenging location for catheter ablation. This study investigates mapping and ablation of VA from anterosuperior LVOT via a transseptal approach.

For a family of homogeneous Moran sets, where at each level, subintervals are arranged with increasing spaces between neighboring subintervals from left to right, we obtained a formula of the Hausdorff dimensions.For a family of homogeneous Moran sets, where at each level, subintervals are arranged with increasing spaces between neighboring subintervals from left to right, we obtained a formula of the Hausdorff dimensions.

Supplemental Digital Content is available in the text.

Supplemental Digital Content is available in the text.

Supplemental Digital Content is available in the text.

Complex networks created by fractals have been associated with various applications such as social networks, modern communication, and fractal antennas. In this paper, we consider a series of scale-free networks based on the construction of high dimensional Sierpiński pyramids. Using self-similarity and elementary renewal theorem, we derive the asymptotic formulas of the average path length (APL) and the average Fermat distance (AFD) on our network sequence in turn. Our approaches are suitable for various networks modeled on self-similar fractals. It is known that the ratio of AFD to APL is between 3 / 2 and 2, which implies that AFD is also a key property to characterize small-world effect. Through an elaborate investigation of our networks, the limit of this ratio for our evolving networks is 3 / 2 and we analyze the result in the light of our interpretation. In fact, in some scale-free networks, the nodes with high degrees have more influence on the choice of Fermat points and thus Fermat distance. Our conclusions may lead to some interesting relations concerning the hyperbolicity, Laplacian spectrum and some multiparameter indices of networks. [ABSTRACT FROM AUTHOR]

The Kirchhoff index is a novel distance-based topological index corresponding to networks, which is the sum of resistance distances between all pairs of nodes. It plays an important role in describing the flow of a network. In this paper, we propose a polygon network model and derive the eigenvalue evolving rule between two generations of the network, and thus obtain the exact Kirchhoff index using the spectral graph theory. • A class of pseudo-fractal polygon networks is constructed. • The Laplacian eigenvalue evolving rule is deduced by the recursive method. • The sum of resistance distances between all pairs of nodes is determined. [ABSTRACT FROM AUTHOR]