2020 ABME Paper Awards
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Annals of Biomedical Engineering, Vol. 48, No. 12, December 2020 (Ó 2020) pp. 2703–2704 https://doi.org/10.1007/s10439-020-02690-y
Editorial
2020 ABME Paper Awards (Published online 20 November 2020)
Each year, the top papers in Annals of Biomedical Engineering (ABME) are selected for editorial awards that are presented at the annual Biomedical Engineering Society (BMES) meeting. During the 2020 BMES Virtual Annual Meeting, a total of five awards were presented: two awards for the most citations,3,8 one award for the most downloads,9 and two editor’s choice awards.2,7 These papers were selected from a total of 185 papers published in 2019. While the citation and download awards were selected purely quantitatively, the editor’s choice awards were selected based on overall impact and quality of the papers. The awards covered a wide range of topics in biomechanics and cardiovascular engineering. Rotman et al. developed a new polymeric transcatheter aortic valve replacement (TAVR) as a possible alternative to the currently used chemically-fixed xenograft valves.8 The hydrodynamic performance of the new valve was compared with a gold standard surgical tissue valve and a clinically used xenograft TAVR valve. The polymeric valve had the best effective orifice area and was the least thrombogenic. The novel valve had a higher regurgitation fraction, but it was still within minimum standard requirements. The newly developed polymeric TAVR valve shows potential for clinical use by outperforming tissue valves. Hatoum et al.. assessed the effect of transcatheter aortic valve (TAV) misalignment with respect to the aortic root axis on sinus hemodynamics.3 Valve misalignment is thought to be one factor associated with increased risk of leaflet thrombosis following TAVR. Sinus hemodynamics were evaluated in vitro with a 3D printed aortic root using particle-image velocimetry. The results of the study showed that valve misalignment does affect sinus hemodynamics by decreasing blood velocity and vorticity, and can potentially increase the risk of leaflet thrombosis. Stemper et al.. evaluated the role of head impact exposure in concussion tolerance.9 Repetitive head impact exposure was quantified in 50 college football players that sustained a concussion during the season. Head impact exposure on the day of injury and the season to date of injury was quantified and compared
to a group of matched controls. The results showed an association between repetitive head impact exposure and concussion diagnosis, supporting the need to limit head impacts in athletes to reduce concussion risk. Rane et al.. used a deep neural network to predict musculoskeletal forces during dynamic movements.7 The forces predicted by the network were comparable to values from musculoskeletal modelling when trained on a dataset of kinematic, kinetic, and electromyographic gait measurements. Additionally, when trained on datasets from the international Grand Challenge competitions, the network outperformed most of the winning submissions. A neural network has advantag
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