Group percolation in interdependent networks with reinforcement network layer.

In many real-world interdependent network systems, nodes often work together to form groups, which can enhance robustness to resist risks. However, previous group percolation models are always of a first-order phase transition, regardless of the group size distribution. This motivates us to investigate a generalized model for group percolation in interdependent networks with a reinforcement network layer to eliminate collapse. Some backup devices that are equipped for a density ρ of reinforced nodes constitute the reinforcement network layer. For each group, we assume that at least one node of the group can function in one network and a node in another network depends on the group to function. We find that increasing the density ρ of reinforcement nodes and the size S of the dependency group can significantly enhance the robustness of interdependent networks. Importantly, we find the existence of a hybrid phase transition behavior and propose a method for calculating the shift point of percolation types. The most interesting finding is the exact universal solution to the minimal density ρ of reinforced nodes (or the minimum group size S) to prevent abrupt collapse for Erdos-Rényi, scale-free, and regular random interdependent networks. Furthermore, we present the validity of the analytic solutions for a triple point ρ (or S ), the corresponding phase transition point p , and second-order phase transition points p in interdependent networks. These findings might yield a broad perspective for designing more resilient interdependent infrastructure networks.

Korotkoff sounds dynamically reflect changes in cardiac function based on deep learning methods.

Korotkoff sounds (K-sounds) have been around for over 100 years and are considered the gold standard for blood pressure (BP) measurement. K-sounds are also unique for the diagnosis and treatment of cardiovascular diseases; however, their efficacy is limited. The incidences of heart failure (HF) are increasing, which necessitate the development of a rapid and convenient pre-hospital screening method. In this review, we propose a deep learning (DL) method and the possibility of using K-methods to predict cardiac function changes for the detection of cardiac dysfunctions.

A Study on the Association between Korotkoff Sound Signaling and Chronic Heart Failure (CHF) Based on Computer-Assisted Diagnoses.

Background: Korotkoff sound (KS) is an important indicator of hypertension when monitoring blood pressure. However, its utility in noninvasive diagnosis of Chronic heart failure (CHF) has rarely been studied. Purpose: In this study, we proposed a method for signal denoising, segmentation, and feature extraction for KS, and a Bayesian optimization-based support vector machine algorithm for KS classification. Methods: The acquired KS signal was resampled and denoised to extract 19 energy features, 12 statistical features, 2 entropy features, and 13 Mel Frequency Cepstrum Coefficient (MFCCs) features. A controlled trial based on the VALSAVA maneuver was carried out to investigate the relationship between cardiac function and KS. To classify these feature sets, the K-Nearest Neighbors (KNN), decision tree (DT), Naive Bayes (NB), ensemble (EM) classifiers, and the proposed BO-SVM were employed and evaluated using the accuracy (Acc), sensitivity (Se), specificity (Sp), Precision (Ps), and F1 score (F1). Results: The ALSAVA maneuver indicated that the KS signal could play an important role in the diagnosis of CHF. Through comparative experiments, it was shown that the best performance of the classifier was obtained by BO-SVM, with Acc (85.0%), Se (85.3%), and Sp (84.6%). Conclusions: In this study, a method for noise reduction, segmentation, and classification of KS was established. In the measured data set, our method performed well in terms of classification accuracy, sensitivity, and specificity. In light of this, we believed that the methods described in this paper can be applied to the early, noninvasive detection of heart disease as well as a supplementary monitoring technique for the prognosis of patients with CHF.

A Simple Logistic Regression Model for Predicting the Likelihood of Recurrence of Atrial Fibrillation Within 1 Year After Initial Radio-Frequency Catheter Ablation Therapy.

BACKGROUND: The clinical factors associated with the recurrence of atrial fibrillation (Af) in patients undergoing catheter ablation (CA) are still ambiguous to date. PURPOSE: 1. To recognize preoperative serologic factors and clinical features associated with Af recurrence after the first ablation treatment. 2. To Develop a Logical Regression Model for Predicting the Likelihood of Recurrence Within 1 Year After the Initial Radio-Frequency Catheter Ablation (RFCA) Therapy. METHODS: Atrial fibrillation patients undergoing RFCA at our institution from January 2016 to June 2021 were included in the analysis (n = 246). A combined dataset of relevant parameters was collected from the participants (clinical characteristics, laboratory results, and time to recurrence) (n = 200). We performed the least absolute shrinkage and selection operator (Lasso) regression with 100 cycles, selecting variables present in all 100 cycles to identify factors associated with the first recurrence of atrial fibrillation. A logistic regression model for predicting whether Af would recur within a year was created using 70% of the data as a training set and the remaining data to validate the accuracy. The predictions were assessed using calibration plots, concordance index (C-index), and decision curve analysis. RESULTS: The left atrial diameter, albumin, type of Af, whether other arrhythmias were combined, and the duration of Af attack time were associated with Af recurrence in this sample. Some clinically meaningful variables were selected and combined with recognized factors associated with recurrence to construct a logistic regression prediction model for 1-year Af recurrence. The receiver operating characteristic (ROC) curve for this model was 0.8695, and the established prediction model had a C-index of 0.83. The performance was superior to the extreme curve in the decision curve analysis. CONCLUSION: Our study demonstrates that several clinical features and serological markers can predict the recurrence of Af in patients undergoing RFCA. This simple model can play a crucial role in guiding physicians in preoperative evaluation and clinical decision-making.

An Exploratory Study on the Relationship between Brachial Arterial Blood Flow and Cardiac Output.

Background: We have obtained prospective clinical outcomes using the brachial artery largely, such as Korotkoff sound and vasomotor function measurement by ultrasound guidance to predict the prognosis of cardiovascular diseases. Very few reports on the quantitative measurement of the relationship between the brachial artery blood flow and cardiac output have been reported. Purpose: (1) To investigate whether the quantitative relationship between the brachial artery blood flow and cardiac output existed. (2) To provide a theoretical basis for taking advantage of artificial intelligence (AI) using Korotkoff sound analogously as far as possible to predict the cardiac output. Methods: A total of 586 patients who underwent cardiac color ultrasound in our center from 2021.3 to 2021.7 were included for analyses. The vascular parameters of the right upper limb brachial artery (such as the Diameter, Area, Blood Velocity, and Flow) were measured immediately after the cardiac color ultrasound, and some basic clinical parameters (Age, Sex, BMI, and Disease) were recorded subsequently. Ultimately, the Mann-Whitney and independent sample T-test were used to analyze the data. Results: (1) The mean Rate of the brachial arterial blood flow to cardiac output was 1.23%, and the mean 95% CI was (1.18%, 1.29%), indicating that the value was mainly concentrated in the current value interval. The indicator demonstrates that there is no significant difference currently among the patients with hypertension, coronary heart disease, and cardiac dysfunction. (2) The brachial artery wall diameter (Dist) is significantly thicker in patients with coronary heart disease and hypertension compared to patients with other cardiovascular diseases. (3) Cardiac output augments remarkably in patients with hypertension. Conclusion: Our study suggests that the Rate (brachial artery blood flow/cardiac output) is a constant of 1.23% approximately. It provides a theoretical basis for the subsequent application of the artificial intelligence (AI) method to predict heart function using Korotkoff sound, cope with large computational amounts, and improve computational speed. It is also indirectly proved that hypertension can lead to a change in peripheral vascular hyperplasia and increase cardiac output.