A few randomized trials have compared impedance-compensated biphasic defibrillators in clinical use. We aim to compare pulsed biphasic (PB) and biphasic truncated exponential (BTE) waveforms in a non-inferiority cardioversion (CVS) study. This was a prospective monocentric randomized clinical trial. Eligible patients admitted for elective CVS of atrial fibrillation (AF) between February 2019 and March 2020 were alternately randomized to treatment with either a PB defibrillator (DEFIGARD TOUCH7, Schiller Médical, Wissembourg, France) or a BTE high-energy (BTE-HE) defibrillator (LIFEPAK15, Physio-Control Inc., Redmond, WA, USA). Fixed-energy protocol (200-200-200 J) was administered. CVS success was accepted if sinus rhythm was restored at 1 min post-shock. The study design considered non-inferiority testing of the primary outcome: cumulative delivered energy (CDE). Seventy-three out of 78 randomized patients received allocated intervention: 38 BTE-HE (52%), 35 PB (48%). Baseline characteristics were well-balanced between groups ( > 0.05). Both waveforms had similar CDE (mean ± standard deviation, 95% confidence interval): BTE-HE (253.9 ± 120.2 J, 214-293 J) vs. PB (226.0 ± 109.8 J, 188-264 J), = 0.31. Indeed, effective PB shocks delivered significantly lower energies by mean of 25.6 J (95% CI 24-27.1 J, < 0.001). Success rates were similar (BTE-HE vs. PB): 1 min first-shock (84.2% vs. 82.9%), 1 min CVS (97.4% vs. 94.3%), 2 h CVS (94.7% vs. 94.3%), 24 h CVS (92.1% vs. 94.3%), > 0.05. Safety analysis did not find CVS hazards, reporting insignificant changes of myocardial-specific biomarkers, transient and rare ST-segment deviations, and no case of harmful tachyarrhythmias and apnea. Cardioversion of AF with fixed-energy protocol 200-200-200 J was highly efficient and safe for both PB and BTE-HE waveforms. These similar performances were achieved despite differences in the waveforms' technical design, associated with significantly lower delivered energy for the effective PB shocks. Clinical Trial Registration: Registration number: NCT04032678, trial register: ClinicalTrials.gov.
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http://dx.doi.org/10.3390/diagnostics11061107 | DOI Listing |
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School of Health and Medical Sciences, University of Southern Queensland, Ipswich 4305, Australia.
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January 2025
School of Chemical Engineering, Yeungnam University, 280 Daehak-ro, Gyeongsan 38541, Republic of Korea.
Solid polymer electrolytes (SPEs) for symmetrical supercapacitors are proposed herein with activated carbon as electrodes and optimized solid polymer electrolyte membranes, which serve as the separators and electrolytes. We propose the design of a low-cost solid polymer electrolyte consisting of guanidinium nitrate (GuN) and poly(ethylene oxide) (PEO) with poly(vinylpyrrolidone) (PVP). Using the solution casting approach, blended polymer electrolytes with varying GuN weight percentage ratios of PVP and PEO are prepared.
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January 2025
Área de Bioquímica y Biología Molecular, Departamento de Biología Molecular, Universidad de León, 24007 León, Spain.
Bioplastics are emerging as a promising solution to reduce pollution caused by petroleum-based plastics. Among them, polyhydroxyalkanoates (PHAs) stand out as viable biotechnological alternatives, though their commercialization is limited by expensive downstream processes. Traditional PHA extraction methods often involve toxic solvents and high energy consumption, underscoring the need for more sustainable approaches.
View Article and Find Full Text PDFMicromachines (Basel)
December 2024
Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China.
Reconfigurable processor-based acceleration of deep convolutional neural network (DCNN) algorithms has emerged as a widely adopted technique, with particular attention on sparse neural network acceleration as an active research area. However, many computing devices that claim high computational power still struggle to execute neural network algorithms with optimal efficiency, low latency, and minimal power consumption. Consequently, there remains significant potential for further exploration into improving the efficiency, latency, and power consumption of neural network accelerators across diverse computational scenarios.
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