Potruch Assaf, Khoury Salim T, Ilan Yaron
Department of Medicine, Hebrew University-Hadassah Medical Center, Jerusalem, Israel.
Department of Neurology, Nazareth Hospital EMMS, Nazareth, Israel.
Seizure. 2020 Aug;80:201-211. doi: 10.1016/j.seizure.2020.06.032. Epub 2020 Jul 2.
Despite progress in the development of anti-seizure drugs, drug-resistant epilepsy (DRE) occurs in a third of patients. DRE is associated with poor quality of life and increased risk of sudden, unexplained death. The autonomic nervous system and chronobiology play a role in DRE. In the present paper, we provide a narrative review the mechanisms that underlie DRE and characterize some of the autonomic- and chronotherapy-associated parameters that contribute to the degree of response to therapy. Variability describes the functions of many biological systems, which are dynamic and continuously change over time. These systems are required for responses to continuing internal and external triggers, in order to maintain homeostasis and normal function. Both intra- and inter-subject variability in biological systems have been described. We present a platform, which comprises a personalized-based machine learning closed loop algorithm built on epilepsy-related signatures, autonomic signals, and chronotherapy, as a means for overcoming DRE, improving the response, and reducing the toxicity of current therapies.
尽管抗癫痫药物的研发取得了进展,但仍有三分之一的患者出现耐药性癫痫(DRE)。DRE与生活质量差以及不明原因猝死风险增加有关。自主神经系统和生物钟学在DRE中发挥作用。在本文中,我们对DRE的潜在机制进行了叙述性综述,并描述了一些与自主神经和时间治疗相关的参数,这些参数有助于治疗反应的程度。变异性描述了许多生物系统的功能,这些系统是动态的,会随时间不断变化。这些系统对于应对持续的内部和外部触发因素是必需的,以维持体内平衡和正常功能。生物系统内和个体间的变异性均已得到描述。我们提出了一个平台,该平台包括基于个性化的机器学习闭环算法,该算法基于癫痫相关特征、自主神经信号和时间治疗构建,作为克服DRE、改善反应和降低当前治疗毒性的一种手段。