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  • 李如男,郭兴道,陈文杨,沈丽霞,高超,张癸荣.女性关键生育力评价指标雌二醇、抗苗勒管激素及窦卵泡数目的拉曼光谱检测[J].第二军医大学学报,2019,40(11):1223-1230    [点击复制]
  • LI Ru-nan,GUO Xing-dao,CHEN Wen-yang,SHEN Li-xia,GAO Chao,ZHANG Gui-rong.Detection of key female fertility evaluation indicators estradiol, anti-Müllerian hormone and antral follicle count by Raman spectroscopy[J].Acad J Sec Mil Med Univ,2019,40(11):1223-1230   [点击复制]
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女性关键生育力评价指标雌二醇、抗苗勒管激素及窦卵泡数目的拉曼光谱检测
李如男1,2,郭兴道2,陈文杨1,2,沈丽霞1,高超3*,张癸荣1,2*
0
(1. 河北北方学院药学系, 河北省神经药理学重点实验室, 张家口 075000;
2. 北京中科遗传与生殖医学研究院新技术研究室, 北京 102629;
3. 南京医科大学第一附属医院江苏省人民医院生殖医学中心, 南京 210029
*通信作者)
摘要:
目的 选取评价女性生育力的关键指标雌二醇(E2)、抗苗勒管激素(AMH)及窦卵泡数目(AFC),探究其在健康女性和不孕女性血清样本中表面增强拉曼光谱(SERS)的差异性及其应用于临床女性生育力评价初筛的可能价值。方法 收集南京医科大学第一附属医院生殖医学中心育龄期的健康女性和不孕女性血清标本共236份,所有受试者年龄均处于育龄阶段(22~49岁),平均年龄为(30.8±5.1)岁。依据临床检测结果,将血清标本分为E2高值组(>5 000 pmol/L,78例)与E2低值组(<500 pmol/L,86例)、AMH高值组(≥ 1.1 ng/mL,33例)与AMH低值组(<1.1 ng/mL,30例)、AFC高值组(>14个,68例)与AFC低值组(<7个,34例)。建立血清SERS分析法并检测各组样本的拉曼光谱,对信号进行正交偏最小二乘法判别分析(OPLS-DA)、受试者工作特征(ROC)曲线分析及置换检验等统计学处理。结果 血清标本的平均拉曼光谱形态和谱峰E2高值组与低值组之间、AMH高值组与低值组之间、AFC高值组与低值组之间基本相似,但谱峰强弱在3个指标的高值组与低值组之间存在差异。在OPLS-DA模型中,E2、AMH和AFC 3个指标的高值组与低值组之间均具有明显的组间分离趋势,其ROC曲线下面积分别为0.996与0.996、0.995与0.995、1与1。结论 SERS技术具备应用于临床女性生育力初筛的潜力,血清SERS图谱作为不孕不育早期诊断的一种辅助手段值得进一步研究。
关键词:  表面增强拉曼光谱  不育  雌二醇  抗苗勒管激素  窦卵泡数目  正交偏最小二乘法判别分析
DOI:10.16781/j.0258-879x.2019.11.1223
投稿时间:2019-05-20修订日期:2019-07-05
基金项目:
Detection of key female fertility evaluation indicators estradiol, anti-Müllerian hormone and antral follicle count by Raman spectroscopy
LI Ru-nan1,2,GUO Xing-dao2,CHEN Wen-yang1,2,SHEN Li-xia1,GAO Chao3*,ZHANG Gui-rong1,2*
(1. College of Pharmacy, Hebei Key Laboratory of Neuropharmacology, Hebei North University, Zhangjiakou 075000, Hebei, China;
2. New Technology Research Laboratory, Peking Medriv Academy of Genetics and Reproduction, Beijing 102629, China;
3. Reproductive Medicine Center, Jiangsu Province Hospital, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, Jiangsu, China
*Corresponding authors)
Abstract:
Objective To explore the surface-enhanced Raman spectroscopy (SERS) difference of key female fertility indicators, estradiol (E2), anti-Müllerian hormone (AMH) and antral follicle count (AFC) in serum samples of healthy and infertile women, and the possibility of their application in preliminary screening of clinical female fertility. Methods A total of 236 serum samples of healthy and infertile women of childbearing age were collected from Reproductive Medical Center of the First Affiliated Hospital with Nanjing Medical University. The ages of all subjects ranged from 22 to 49 years old, with an average age of (30.8±5.1) years old. The samples were divided into high E2 value group (>5 000 pmol/L, 78 cases) and low E2 value group (<500 pmol/L, 86 cases), high AMH value group (≥ 1.1 ng/mL, 33 cases) and low AMH value group (<1.1 ng/mL, 30 cases), high AFC value group (>14, 68 cases) and low AFC value group (<7, 34 cases). Serum SERS analysis was established and Raman spectra of each group were detected. Orthogonal partial least squares discriminant analysis (OPLS-DA), receiver operating characteristic (ROC) curve and permutation test were used to analyze the signals. Results The Raman spectrum morphology of serum samples was similar between high and low E2 value groups, high and low AMH value groups, and high and low AFC value groups, but the spectral peak intensity of the three indicators was different between the high and low value groups. In the OPLS-DA model, there was an obvious clustering trend in E2, AMH and AFC between the high and low value groups, and the areas under ROC curve were 0.996 and 0.996, 0.995 and 0.995, and 1 and 1 in high and low E2 value groups, high and low AMH value groups, and high and low AFC value groups, respectively. Conclusion SERS has a potential to be used in the primary screening of female fertility. Serum SERS profile as an auxiliary method for early diagnosis of infertility is worthy of further study.
Key words:  surface-enhanced Raman spectroscopy  infertility  estradiol  anti-Müllerian hormone  antral follicle count  orthogonal partial least squares discriminant analysis