Objectives: DIAMOND-AF (DiamondTemp™ Ablation System for the Treatment of Paroxysmal Atrial Fibrillation) was a prospective, multicenter, noninferiority, randomized trial that compared the safety and effectiveness of the DTA system versus those of a force-sensing RF ablation system (control) for the treatment of patients with drug-refractory, recurrent, symptomatic paroxysmal atrial fibrillation (AF).
Background: Irrigated radiofrequency (RF) ablation catheters lose tissue temperature acuity, which is vital in assessing lesion formation. DiamondTemp Ablation (DTA) was designed to re-establish accurate tissue temperature measurements during ablation.
Methods: A total of 482 patients with paroxysmal AF were randomized (239 DTA, 243 control) to undergo pulmonary vein isolation and were followed up at 23 sites. Patients were screened for disease progression, cardiac characteristics, and prior interventions. Primary endpoints were effectiveness (freedom from atrial arrhythmia recurrence) and safety (composite of procedure- and device-related serious adverse events).
Results: The primary safety event rate was 3.3% in the DTA group versus 6.6% in the control group (p < 0.001 vs. 6.5% noninferiority margin). Primary effectiveness was met in 79.1% of DTA subjects and 75.7% of control subjects (p < 0.001 vs. -12.5% noninferiority margin). Secondary endpoint analysis found that off-drug effectiveness favored DTA compared with the control (142 [59.4%] vs. 120 [49.4%], respectively; p = 0.03). Total RF time and individual RF ablation duration were significantly shorter with less saline infused through the DTA catheter (p < 0.001). Both arms saw clinically meaningful improvements in quality of life at 12 months.
Conclusions: Safety and efficacy of the DTA system proved noninferior to force-sensing RF ablation in a paroxysmal AF population. Efficiencies were observed using DTA with shorter total RF times, individual RF ablation durations, and less saline infusion. (DiamondTemp™ Ablation System for the Treatment of Paroxysmal Atrial Fibrillation; NCT03334630).
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http://dx.doi.org/10.1016/j.jacep.2020.11.009 | DOI Listing |
Int J Nanomedicine
December 2024
Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, People's Republic of China.
Liver cancer has become a major global health challenge due to its high incidence, high rate of late diagnosis and limited treatment options. Although there are many clinical treatments available for liver cancer, the cure rate is still very low, and now researchers have begun to explore new aspects of liver cancer treatment, and nanotechnology has shown great potential for improving diagnostic accuracy and therapeutic efficacy and is therefore a promising treatment option. In diagnosis, nanomaterials such as gold nanoparticles, magnetic nanoparticles, and silver nanoparticles can realize highly sensitive and specific detection of liver cancer biomarkers, supporting diagnosis and real-time monitoring of the disease process.
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December 2024
Centre for Quantitative Medicine, Duke-NUS Medical School Singapore.
Background: Pneumothorax is a medical emergency caused by the abnormal accumulation of air in the pleural space-the potential space between the lungs and chest wall. On 2D chest radiographs, pneumothorax occurs within the thoracic cavity and outside of the mediastinum, and we refer to this area as "lung + space." While deep learning (DL) has increasingly been utilized to segment pneumothorax lesions in chest radiographs, many existing DL models employ an end-to-end approach.
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December 2024
Xi'an Shiyou University School of Electronic Engineering, Xi'an, 710065, China.
The expressway green channel is an essential transportation policy for moving fresh agricultural products in China. In order to extract knowledge from various records, this study presents a cutting-edge approach to extract information from textual records of failure cases in the vertical field of expressway green channel. We proposed a hybrid approach based on BIO labeling, pre-trained model, deep learning and CRF to build a named entity recognition (NER) model with the optimal prediction performance.
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Department for Orthopaedic and Trauma Surgery, Lucerne Cantonal Hospital LUKS, Spitalstrasse, Lucerne, Switzerland.
Objective: To maximize local tumor control, stabilize affected bones, and preserve or replace joints with minimal interventional burden, thereby enhancing quality of life for empowered living.
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Sci Rep
December 2024
The Key Laboratory for Computer Systems of State Ethnic Affairs Commission, School of Computer and Artificial Intelligence, Southwest Minzu University, Chengdu, 610041, China.
Coronary artery disease represents a formidable health threat to middle-aged and elderly populations worldwide. This research introduces an advanced BP neural network algorithm, EPSOSA-BP, which integrates particle swarm optimization, simulated annealing, and a particle elimination mechanism to elevate the precision of heart disease prediction models. To address prior limitations in feature selection, the study employs single-hot encoding and Principal Component Analysis, thereby enhancing the model's feature learning capability.
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