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Journal of Zhejiang University SCIENCE B

ISSN 1673-1581(Print), 1862-1783(Online), Monthly

Hydroxychavicol, a polyphenol from Piper betle leaf extract, induces cell cycle arrest and apoptosis in TP53-resistant HT-29 colon cancer cells

Abstract: This study aims to elucidate the antiproliferative mechanism of hydroxychavicol (HC). Its effects on cell cycle, apoptosis, and the expression of c-Jun N-terminal kinase (JNK) and P38 mitogen-activated protein kinase (MAPK) in HT-29 colon cancer cells were investigated. HC was isolated from Piper betle leaf (PBL) and verified by high-performance liquid chromatography (HPLC), nuclear magnetic resonance (NMR), and gas chromatography-mass spectrometry (GC-MS). The cytotoxic effects of the standard drug 5-fluorouracil (5-FU), PBL water extract, and HC on HT-29 cells were measured after 24, 48, and 72 h of treatment. Cell cycle and apoptosis modulation by 5-FU and HC treatments were investigated up to 30 h. Changes in phosphorylated JNK (pJNK) and P38 (pP38) MAPK expression were observed up to 18 h. The half maximal inhibitory concentration (IC50) values of HC (30 ?g/mL) and PBL water extract (380 ?g/mL) were achieved at 24 h, whereas the IC50 of 5-FU (50 ?mol/L) was obtained at 72 h. Cell cycle arrest at the G0/G1 phase in HC-treated cells was observed from 12 h onwards. Higher apoptotic cell death in HC-treated cells compared to 5-FU-treated cells (P<0.05) was observed. High expression of pJNK and pP38 MAPK was observed at 12 h in HC-treated cells, but not in 5-FU-treated HT-29 cells (P<0.05). It is concluded that HC induces cell cycle arrest and apoptosis of HT-29 cells, with these actions possibly mediated by JNK and P38 MAPK.

Key words: Piper betle; Hydroxychavicol (HC); Cell cycle; Apoptosis; c-Jun N-terminal kinase (JNK); P38 mitogen-activated protein kinase (MAPK)

Chinese Summary  <26> 线性离散时间系统H∞控制的极小极大Q-学习设计

李新兴1,奚乐乐2,3,查文中1,彭志红2
1中国电子科技集团公司信息科学研究院,中国北京市,100086
2北京理工大学自动化学院,中国北京市,100081
3鹏城实验室,中国深圳市,518052
摘要:H控制是一种消除系统扰动的有效方式,但是由于需要求解非线性哈密顿-雅克比-伊萨克斯方程,H控制器往往很难得到,即便对于线性系统。本文考虑了线性离散时间系统的H控制器设计问题。为求解涉及的博弈代数黎卡提方程,在离线策略算法基础上提出一种新型无模型极小极大Q-学习算法,并证明离线策略迭代算法是求解博弈代数黎卡提方程的牛顿法。提出的极小极大Q-学习算法采用离轨策略强化学习技术,利用行为策略产生的系统状态数据,可实现对最优控制器和最佳干扰策略的在线学习。不同于当前Q-学习算法,本文提出一种基于梯度的策略提高方法。证明在一定持续激励条件下,对于初始可行的控制策略并结合合适学习率,提出的极小极大Q-学习算法可收敛到鞍点策略。此外,算法收敛所需的持续激励条件可通过选择包含一定噪声激励的合适行为策略实现,且不会引起任何激励噪声偏差。将提出的极小极大Q-学习算法用于受负载扰动的电力系统H负载频率控制器设计,仿真结果表明,最终得到的H负载频率控制器具有良好抗干扰性能。

关键词组:H控制;零和动态博弈;强化学习;自适应动态规划;极小极大Q-学习;策略迭代


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DOI:

10.1631/jzus.B2000446

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On-line Access:

2021-02-07

Received:

2020-08-07

Revision Accepted:

2022-04-22

Crosschecked:

2021-01-06

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