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Abstract Background The impact of delaying atrial fibrillation catheter ablation (AFCA) for antiarrhythmic drug (AAD) management on the disease course remains unclear. This study investigated AFCA rhythm outcomes based on the diagnosis‐to‐ablation time (DAT) and AAD responsiveness in participants with persistent AF (PeAF). Methods We included data from 1038 AAD‐resistant PeAF participants, all of whom had a clear time point for AF diagnosis, especially PeAF at diagnosis time, and had undergone an AFCA for the first time. Participants who experienced recurrences of paroxysmal type on AAD therapy were analyzed as a cohort of AAD‐partial responders; those maintaining PeAF on AAD were AAD‐non‐responders. We determined the DAT cutoff for best discriminating long‐term rhythm outcomes using a maximum log‐likelihood estimation method based on the Cox proportional hazard regression model. Results Of the participants (79.8% male; median age 61), 806 (77.6%) were AAD‐non‐responders. AAD‐non‐responders had a higher body mass index and a larger left atrial diameter than AAD‐partial‐responders. They also had a higher incidence of AF recurrence after AFCA (adjusted hazard ratio 1.75, 95% confidence interval 1.33–2.30; log‐rank p

The pandemic has changed daily urban life, including leisure activities and travel patterns. Although several studies have tracked changes in leisure and travel behavior due to the pandemic, few have analyzed the effects of COVID-19 preventive behavior by considering the interaction between activities and walking for leisure. This study aims to investigate the mutual relationship between outdoor activities and walking for leisure during the pandemic among 537 residents in the Seoul metropolitan area using a three-level least squares (3SLS) approach. The results demonstrate a bidirectional association between outdoor activities and walking during the pandemic. Individuals who avoided long-distance leisure travel increased their frequency of short-distance walking, and postponing or canceling social interactions led to more outdoor leisure activities. Notably, those who refrained from going out for leisure exhibited reduced outdoor activities but an increase in walking for leisure. These findings highlight the significance of considering the synergy between leisure activities and walking amid the pandemic.

Background Increased left atrial pressure (LAP) contributes to dyspnea and heart failure with preserved ejection fraction in patients with atrial fibrillation (AF). The purpose of this study was to investigate the differences in baseline LAP and LAP response to rapid pacing between paroxysmal and persistent AF. Methods and Results This observational study prospectively enrolled 1369 participants who underwent AF catheter ablation, excluding those with reduced left ventricular ejection fraction. H2FPEF score was calculated by echocardiography and baseline characteristics. Patients underwent LAP measurements during AF, sinus rhythm, and heart rates of 90, 100, 110, and 120 beats per minute (bpm), induced by right atrial pacing and isoproterenol. The baseline LAP‐peak in the persistent AF group consistently exceeded that in the paroxysmal AF (PAF) group across each H2FPEF score subgroup (all P

Abstract Although pulmonary vein isolation (PVI) gaps and extrapulmonary vein triggers contribute to recurrence after atrial fibrillation (AF) ablation, their precise mechanisms remain unproven. Our study assessed the impact of PVI gaps on rhythm outcomes using a human AF digital twin. We included 50 patients (76.0% with persistent AF) who underwent catheter ablation with a realistic AF digital twin by integrating computed tomography and electroanatomical mapping. We evaluated the final rhythm status, including AF and atrial tachycardia (AT), across 600 AF episodes, considering factors including PVI level, PVI gap number, and pacing locations. Our findings revealed that antral PVI had a significantly lower ratio of AF at the final rhythm (28% vs. 56%, p = 0.002) than ostial PVI. Increasing PVI gap numbers correlated with an increased ratio of AF at the final rhythm (p

Abstract Background Homeostasis model assessment for insulin resistance (HOMA-IR) is a biomarker for type 2 diabetes mellitus (T2DM). However, the role of HOMA-IR in the non-diabetic is unclear. This study aimed to determine whether IR measured HOMA-IR value is associated with new onset diabetes as well as vascular disease and can be used as an early predictor for diabetes and vascular diseases in non-diabetic participants. Methods From a prospective community-based cohort of 10,030 individuals, 4314 individuals younger than 65 years and without diabetes were enrolled and divided into three groups by baseline HOMA-IR tertiles: low (n = 1454), moderate (n = 1414), and high (n = 1446). The primary outcome was new onset T2DM. Secondary outcomes were chronic kidney disease (CKD) and a composite of coronary artery disease, myocardial infarction, and stroke as macrovascular events. Results The mean age was 51 years. The prevalence of hypertension and cholesterol and HbA1c were higher in the high HOMA-IR group. New onset T2DM (5.8%) and CKD (12.2%) incidence in the high HOMA-IR group was higher than that in the others. The prevalence of macrovascular events did not differ among groups. High-HOMA-IR was an independent risk factor for new onset T2DM (odds ratio 1.86 [1.17–2.96]; p = 0.01) and CKD (1.49 [1.12–1.98]; p = 0.01). Conclusions High HOMA-IR was an early predictor of new onset T2DM and CKD, regardless of HbA1c in non-diabetic individuals. Further research on the specific cut off value will be needed.

Background Although successful atrial fibrillation (AF) ablation can maintain sinus rhythm and reduce the left atrial (LA) dimension, blunted LA reverse remodeling can be observed in patients with atrial myopathy. We explored the potential mechanisms and long‐term outcomes in patients with blunted LA reverse remodeling after successful AF catheter ablation. Methods and Results We included 1685 patients who underwent baseline and 1‐year follow‐up echocardiograms, had a baseline LA dimension ≥40 mm, and did not have a recurrence of AF within a year. The patients were divided into tertile groups according to the delta value of the change in LA dimension on the preprocedure and 1‐year postprocedure echocardiography. After propensity score matching for age, sex, AF type, and LA dimension, 1272 patients were finally included in the analyses (424 in each group; the least/blunted, moderate, and the most reverse remodeling group). The patients in the T1 group (blunted LA reverse remodeling) were independently associated with higher left ventricular mass index (odds ratio [OR], 1.014 [95% CI, 1.005–1.022], P=0.001), change in ΔH2FPEF score (heavy, hypertensive, atrial fibrillation, pulmonary hypertension, elder, filling pressure) score (OR, 1.445 [95% CI, 1.121–1.861], P=0.004), ventricular epicardial adipose tissue volume (OR, 1.010 [95% CI, 1.003–1.017], P=0.003), thinner LA wall thickness (OR, 0.461 [95% CI, 0.271–0.785], P=0.004), lower LA voltage (OR, 0.670 [95% CI, 0.499–0.899], P=0.008), and showed higher long‐term AF recurrence (log‐rank P

Parkinson’s disease (PD) is a neurodegenerative disorder that causes gait abnormalities. Early and accurate recognition of PD gait is crucial for effective treatment. Recently, deep learning techniques have shown promising results in PD gait analysis. However, most existing methods focus on severity estimation and frozen gait detection, while the recognition of Parkinsonian gait and normal gait from the forward video has not been reported. In this paper, we propose a novel spatiotemporal modeling method for PD gait recognition, named WM–STGCN, which utilizes a Weighted adjacency matrix with virtual connection and Multi-scale temporal convolution in a Spatiotemporal Graph Convolution Network. The weighted matrix enables different intensities to be assigned to different spatial features, including virtual connections, while the multi-scale temporal convolution helps to effectively capture the temporal features at different scales. Moreover, we employ various approaches to augment skeleton data. Experimental results show that our proposed method achieved the best accuracy of 87.1% and an F1 score of 92.85%, outperforming Long short-term memory (LSTM), K-nearest neighbors (KNN), Decision tree, AdaBoost, and ST–GCN models. Our proposed WM–STGCN provides an effective spatiotemporal modeling method for PD gait recognition that outperforms existing methods. It has the potential for clinical application in PD diagnosis and treatment.

Abstract In cloud computing, there is a trade-off between SLAV (Service Level Agreement Violation) and system operating cost. Violation rates can be decreased when using more hosts, which increases system operating costs. Therefore, to manage the resources of those hosts efficiently, finding an optimal balancing point between SLAV and system operating cost is critical. In addition, a cost-effective load balancing approach based on the proper migration of VMs (Virtual Machines) in the hosts is needed. For this purpose, some indicators are also necessary to identify the abnormal hosts that violate SLA. One of the primary indicators, CPU usage, is closely related to energy consumption and can be used to reduce SLAV and energy consumption effectively. Our approach focuses on the special environment such as the cloud environment for the scientific data. Here, most of the jobs are data-intensive and a large amount of disk operations is required. Owing to disk operations are likely to affect the performance degradation of the host, disk bandwidth usage as well as CPU usage should be also considered. In this study, we propose the Min-Max Exclusive VM Placement (MMEVMP) strategy to minimize both SLAV and energy consumption. The current working system called KIAF (KISTI Analysis Facility), the CERN ALICE experimental cloud environment for scientific data analysis, is used to analyze the characteristics of data-intensive jobs within it. In this experiment, a lightweight version of the CloudSim simulator was developed and the results were compared to the other methods of different policies. Our evaluation showed that our proposed strategy can reduce SLA violation reasonably as well as the system operating cost-effectively.

BackgroundScar tissue formation after catheter ablation for atrial fibrillation (AF) may adversely affect the diastolic properties of the left atrium (LA), which can result in a stiff LA physiology in a small proportion of patients. In this study, we aimed to explore the relationship between diabetes mellitus and a stiff LA physiology after AF catheter ablation (AFCA).MethodsA total of 1,326 patients who underwent de novo AFCA, and baseline and 1-year follow-up echocardiographies were enrolled. After 1:3 propensity score (PS) matching for age, sex, and AF type, we compared 211 patients with DM with 633 patients without DM. A stiff LA physiology was defined as estimated pulmonary arterial pressure increase of >10 mmHg and a right ventricular systolic pressure of >35 mmHg at 1-year follow-up echocardiography. Pulmonary vascular resistance (PVR) was estimated using echocardiographic parameters.ResultsAmong the 844 PS-matched patients, a stiff LA physiology was observed in 32 patients (4.1%). The patients with DM showed a higher peak LA pressure (p < 0.001) and greater LA wall stress (p = 0.001) than did those without. A stiff LA physiology was independently associated with DM [Odds ratio (OR) = 2.39, 95% confidence interval (CI) 1.02-5.59, p = 0.045], empirical extra-pulmonary vein LA ablation (OR = 3.14, 95% CI 1.07–9.3, p = 0.038) and the ΔPVR (OR = 1.78, 95% CI 1.37–2.31, p < 0.001). The ΔPVR was independently associated with DM (β = 0.37, 95% CI 0.06-0.67, p = 0.020) and a stiff LA physiology (β = 1.40, 95% CI 0.70–2.10, p < 0.001). During the 38.8 ± 29.3months follow-up, the incidence of the clinical recurrence of AF was significantly higher in the patients with a stiff LA physiology than in those without (log rank p = 0.032).ConclusionA stiff LA physiology was independently associated with DM because of the relatively small decrease in the PVR after AFCA in this population. The patients with a stiff LA physiology had worse rhythm outcomes after AFCA than those without.

BackgroundPatients with cardiovascular disease (CVD) tend to have higher mortality rates and reduced physical activity (PA). We aimed to evaluate the effect of PA on mortality in older adults with specific CVD.MethodsWe enrolled 68,223 participants (n = 23,871 with CVD, n = 44,352 without CVD) aged ≥65 years with available physical activity data between 2005 and 2012 from the Korean National Health Insurance Service of Korea-Senior database. CVD was defined as a history of ischemic stroke, transient ischemic attack, heart failure, myocardial infarction, and peripheral artery disease.ResultsPatients with CVD were older than those without CVD. Compared with the sedentary group, the physically active groups with and without CVD had a lower incidence and risk of all-cause death during a median follow up period of 42 (interquartile range 30–51) months. A 500 metabolic equivalent task-min/week increase in PA resulted in an 11% and 16% reduction in the risk of mortality in the non-CVD and CVD groups, respectively. With regard to specific CVDs, the risk of mortality progressively reduced with increasing PA in patients with heart failure or myocardial infarction. However, the reduction reached a plateau in patients with stroke or peripheral artery disease, but was significantly greater in patients with stroke (20% vs. without stoke, 11%, Pint = 0.006) or heart failure (13% vs. without heart failure, 11%; Pint = 0.045)ConclusionsPA was associated with a reduced risk of all-cause mortality in older adults with and without CVD. The benefits of PA in patients with CVD, especially patients with stroke or heart failure, were greater than those without.

Background: Regular exercise improves the functional ability and quality of life of patients with heart failure (HF). However, studies on the results of intensity of exercise in the older population are scarce, especially in the Asian population. Method and Results: A total of 8982 older people (age ≥65 years) with HF were selected from the Korean National Health Insurance Service-Senior database (2005–2012). Participants were stratified according to the levels of physical activity per week as follows: (1) inactive group; (2) insufficiently active group: 1–499 metabolic equivalent task minutes (MET-min)/week; (3) active group: 500–999 MET-min/week; and (4) highly active group: ≥1000 MET-min/week. During a median follow-up period of 3.2 years, the incidence and risk of mortality were reduced in the insufficiently active (6.7 vs. 4.2 per 100 person-years, adjusted hazard ratio [HR], 0.82; 95% confidence interval [CI], 0.71–0.94; p < 0.001), active (3.8 per 100 person-years; HR, 0.81; 95% CI, 0.70–0.95; p = 0.010), and highly active (2.4 per 100 person-years; HR, 0.52; 95% CI, 0.41–0.67; p < 0.001) groups compared to inactive patients. Conclusions: In older Asians with HF, increased physical activity reduced the risk of all-cause mortality. The mortality-reducing benefit started at a lower physical activity compared to the World Health Organization guideline (500–999 MET-min/week), and the risk decreased with more physical activity.

Cloud computing manages system resources such as processing, storage, and networking by providing users with multiple virtual machines (VMs) as needed. It is one of the rapidly growing fields that come with huge computational power for scientific workloads. Currently, the scientific community is ready to work over the cloud as it is considered as a resource-rich paradigm. The traditional way of executing scientific workloads on cloud computing is by using virtual machines. However, the latest emerging concept of containerization is growing more rapidly and gained popularity because of its unique features. Containers are treated as lightweight as compared to virtual machines in cloud computing. In this regard, a few VMs/containers-associated problems of performance and throughput are encountered because of middleware technologies such as virtualization or containerization. In this paper, we introduce the configurations of VMs and containers for cloud-based scientific workloads in order to utilize the technologies to solve scientific problems and handle their workloads. This paper also tackles throughput and efficiency problems related to VMs and containers in the cloud environment and explores efficient resource provisioning by combining four unique methods: hyperthreading (HT), vCPU cores selection, vCPU affinity, and isolation of vCPUs. The HEPSCPEC06 benchmark suite is used to evaluate the throughput and efficiency of VMs and containers. The proposed solution is to implement four basic techniques to reduce the effect of virtualization and containerization. Additionally, these techniques are used to make virtual machines and containers more effective and powerful for scientific workloads. The results show that allowing hyperthreading, isolation of CPU cores, proper numbering, and allocation of vCPU cores can improve the throughput and performance of virtual machines and containers.