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Machine Learning for H-FIRE Protocols: Tuning Parameters for High-Frequency Irreversible Electroporation by Machine Learning
IEEE Microwave Magazine ( IF 3.6 ) Pub Date : 2021-08-02 , DOI: 10.1109/mmm.2021.3086316 Marco Zappatore , Giulia Cerfeda , Caternina Merla , Luciano Tarricone
IEEE Microwave Magazine ( IF 3.6 ) Pub Date : 2021-08-02 , DOI: 10.1109/mmm.2021.3086316 Marco Zappatore , Giulia Cerfeda , Caternina Merla , Luciano Tarricone
Microwave (MW) technologies are increasingly being employed in biomedicine and health care. Applications such as MW ablation (MWA), MW sensors for the realtime monitoring of physiological parameters, and lab-on-a-chip devices are just some of the examples in a wide variety of possibilities. Recently, highfrequency irreversible electroporation (H-FIRE) has been attracting more attention due to its important applications in oncology. It uses pulsed electrical fields (PEFs) to induce a controlled but irreversible process of permeabilization of cell membranes, thus triggering a substantial alteration in the physiological equilibria of cells, ultimately leading to their death.
中文翻译:
H-FIRE 协议的机器学习:通过机器学习调整高频不可逆电穿孔参数
微波 (MW) 技术越来越多地用于生物医学和医疗保健。诸如 MW 消融 (MWA)、用于实时监测生理参数的 MW 传感器和芯片实验室设备等应用只是各种可能性中的一些示例。最近,高频不可逆电穿孔(H-FIRE)因其在肿瘤学中的重要应用而受到越来越多的关注。它使用脉冲电场 (PEF) 来诱导受控但不可逆的细胞膜透化过程,从而引发细胞生理平衡的重大改变,最终导致细胞死亡。
更新日期:2021-08-02
中文翻译:
H-FIRE 协议的机器学习:通过机器学习调整高频不可逆电穿孔参数
微波 (MW) 技术越来越多地用于生物医学和医疗保健。诸如 MW 消融 (MWA)、用于实时监测生理参数的 MW 传感器和芯片实验室设备等应用只是各种可能性中的一些示例。最近,高频不可逆电穿孔(H-FIRE)因其在肿瘤学中的重要应用而受到越来越多的关注。它使用脉冲电场 (PEF) 来诱导受控但不可逆的细胞膜透化过程,从而引发细胞生理平衡的重大改变,最终导致细胞死亡。