基于人工神经网络和蛋白质指纹图谱的子宫内膜异位症在位内膜的鉴定生物标志物。
Identification biomarkers of eutopic endometrium in endometriosis using artificial neural networks and protein fingerprinting.
机构信息
The 2nd Affiliated Hospital, Department of Gynecology, and Ministry of Education Key Laboratory of Cancer Prevention and Intervention, Zhejiang University School of Medicine, Hangzhou, People's Republic of China.
出版信息
Fertil Steril. 2010 May 1;93(7):2460-2. doi: 10.1016/j.fertnstert.2009.08.061. Epub 2010 Jan 4.
Abstract
Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) protein chip array technology was used to detect biomarkers of eutopic endometrium in endometriosis patients. Five potential biomarkers (6,898 m/z, 5,891 m/z, 5,385 m/z, 6,448 m/z, and 5,425 m/z) were found.
摘要
表面增强激光解吸/电离飞行时间质谱（SELDI-TOF-MS）蛋白质芯片阵列技术用于检测子宫内膜异位症患者在位子宫内膜的生物标志物。发现了 5 个潜在的生物标志物（6,898 m/z、5,891 m/z、5,385 m/z、6,448 m/z 和 5,425 m/z）。
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