Search

Intramural or epicardial locations of the arrhythmogenic substrate are regarded as one of the main reasons for radiofrequency (RF) catheter ablation failure. This study aims to conduct a comprehensive analysis of various factors including baseline impedance, irrigant and electrode configuration at similar ablation index (AI) value.In 12 ex vivo swine hearts, RF ablation was performed at a target AI value of 500 and a multistep impedance load (100-180 Ω) in 4 settings: (1) conventional unipolar configuration with an irrigant of normal saline (NS); (2) conventional unipolar configuration with an irrigant of half normal saline (HNS); (3) bipolar configuration with an irrigant of NS; (4) sequential unipolar configuration with an irrigant of NS. The relationships between lesion dimensions and above factors were examined.Baseline impedance had a strong negative linear correlation with lesion dimensions at a certain AI. The correlation coefficient between baseline impedance and depth, width, and volume were R = -0.890, R = -0.755 and R = -0.813, respectively (p .01). There were 10 (total: 10/100, 10%; bipolar: 10/25, 40%) transmural lesions during the whole procedure. Bipolar ablation resulted in significantly deeper lesion than other electrode configurations. Other comparisons in our experiment did not achieve statistical significance.There is a strong negative linear correlation between baseline impedance and lesion dimensions at a certain AI value. Baseline impedance has an influence on the overall lesion dimensions among irrigated fluid and ablation configurations. Over a threshold impedance of 150 Ω, the predictive accuracy of AI can be compromised.

Electroencephalogram (EEG) signals are the gold standard tool for detecting epileptic seizures. Long-term EEG signal monitoring is a promising method to realize real-time and automatic epilepsy detection with the assistance of computer-aided techniques and the Internet of Medical Things (IoMT) devices. Machine learning (ML) algorithms combined with advanced feature extraction methods have been widely explored to precisely recognize EEG signals, while among which, little attention has been paid to high computing costs and severe information losses. The lack of model interpretability also impedes the wider application and deeper understanding of ML methods in epilepsy detection. In this research, a novel feature extraction method based on an autoencoder (AE) is proposed in the time domain. The architecture and mechanism are elaborated. In this method, specified features are defined and calculated on the basis of signal reconstruction quantification of the AE. The EEG recognition is performed to validate the effectiveness of the proposed detection method, and the prediction accuracy reached 97%. To further investigate the superiority of the proposed AE-based feature extraction method, a widely used feature extraction method, PCA, is allocated for comparison. In order to understand the underlying working mechanism, permutation importance and SHapley Additive exPlanations (SHAP) are conducted for model interpretability, and the results further confirm the reasonability and effectiveness of the extracted features by AE reconstruction. With high computing efficiency in the time domain and an extensively satisfactory accuracy, the proposed epilepsy detection method exhibits great superiority and potential in almost real-time and automatic epilepsy monitoring.

The laser marking system was combined with MES system, the marking setting database was established by sorting out the product specifications, and the products of various specifications were quickly matched by calling the template, so as to realize the control automation of the product marking system and reduce the difficulty of processing non-standard products. Using image recognition technology, the marking area and the marking effect are digitized, the quality monitoring visual window is established, and the backup is stored in the MES system, so that the quality is controllable and can be checked in the later stage. The laser marking system introduced in this paper reduces manual intervention in production data to the greatest extent, improves the accuracy of information, sorts out various types and specifications of products, and sets up marking templates accordingly, which greatly improves the efficiency of cable network production. Finally, image recognition technology is applied to quality control to realize quality management and record, which provides reference for other production and processing system design.

Myocardial fibrosis plays an important role in the development of heart failure and malignant arrhythmia, which potentially increases the incidence of sudden cardiac death. Therefore, early detection of myocardial fibrosis is of great significance for evaluating the prognosis of patients and formulating appropriate treatment strategies. Late gadolinium enhanced magnetic resonance imaging is considered as the currently effective strategy for noninvasive detection of myocardial fibrosis, but it still suffers from some critical issues. In this work, multifunctional CNA35-labeled perfluoropentane nanoparticles (CNA35-PFP NPs) have been elaborately designed and constructed for molecular imaging of fibrotic myocardium based on ultrasound imaging. These as-constructed CNA35-PFP NPs are intravenously infused into rabbit circulation with an animal model of myocardial infarction. Especially, these targeted CNA35-PFP NPs with nanoscale size could efficiently pass through the endothelial cell gap and adhere to the surface of fibroblasts in the fibrotic myocardium. Importantly, followed by low-intensity focused ultrasound irradiation on the myocardium, these intriguing CNA35-PFP NPs could transform from liquid into gaseous microbubbles, which further significantly enhanced the ultrasound contrast in the fibrotic area, facilitating the detection by diagnostic ultrasound imaging. Therefore, this work provides a desirable noninvasive, economical, and real-time imaging technique for the assessment of cardiac fibrosis with diagnostic ultrasound based on the rational design of liquid-to-gas phase-changeable nanoplatforms.

Background: The effect of catheter ablation (CA) versus drug therapy on cardiac function improvement is not clear for patients with persistent atrial fibrillation and heart failure. To compare the short-term therapeutic effects between CA and conventional drug therapy, we conducted a meta-analysis of the current randomized controlled trials (RCTs). Objective and method: The analyses systematically collected PubMed, Embase, and the Cochrane Library for RCTs comparing catheter ablation with medical therapy in patients with persistent atrial fibrillation and heart failure. In order to reduce the impact of other factors on cardiac function, we limited the maximum follow-up time to two years. The primary outcome is changes in left ventricular ejection fraction (LEVF), second outcomes are changes in 6-minute walk test (6MWT), Minnesota Living with Heart Failure Score (MLHFQ), B-type natriuretic peptide (BNP). SMD was used for the results measured by different methods. Random-effects model or fixed-effects model was used to estimate relative risks (RRs) with 95% confidence intervals (CIs). Results: A total of six randomized controlled trials included 775 patients included in the final analysis, compared with drug therapy, catheter ablation has a better improvement in left ventricular ejection fraction (SMD, 0.57; 95% CI [0.40, 0.94], p