法医学杂志

法医学杂志 ›› 2023, Vol. 39 ›› Issue (5): 447-451.DOI: 10.12116/j.issn.1004-5619.2021.511207

• 论著 • 上一篇    下一篇

利用朴素贝叶斯和多元logistic回归构建月经血mRNA标志分析模型

张琦1,2(), 赵禾苗3, 杨康4, 陈静3, 杨瑞琴1(), 王冲3()   

  1. 1.中国人民公安大学，北京 100038
    2.瓦房店市公安局，辽宁 大连 116300
    3.公安部鉴定中心 法医遗传学公安部重点实验室，北京 100038
    4.西安市公安局，陕西 西安 710038
  • 收稿日期:2021-12-20 发布日期:2023-11-24 出版日期:2023-10-25
  • 通讯作者: 杨瑞琴,王冲
  • 作者简介:杨瑞琴，女，博士，教授，主要从事刑事科学技术研究；E-mail：yangruiqin@ppsuc.edu.cn
    王冲，女，博士，正高级警务技术任职资格，主要从事法医遗传学研究；E-mail：wangannann@126.com
    张琦（1996—），女，硕士，主要从事法医遗传学研究；E-mail：zhangqi226@outlook.com
  • 基金资助:
    中央级公益性科研院所资助项目(2020JB001)

Construction of an Analysis Model of mRNA Markers in Menstrual Blood Based on Naïve Bayes and Multivariate Logistic Regression Methods

Qi ZHANG1,2(), He-miao ZHAO3, Kang YANG4, Jing CHEN3, Rui-qin YANG1(), Chong WANG3()   

  1. 1.People’s Public Security University of China，Beijing 100038，China
    2.Wafangdian Public Security Bureau, Dalian 116300, Liaoning Province, China
    3.Key Laboratory of Forensic Genetics，Institute of Forensic Science，Ministry of Public Security，Beijing 100038，China
    4.Xi’an Public Security Bureau，Xi’an 710038，China
  • Received:2021-12-20 Online:2023-11-24 Published:2023-10-25
  • Contact: Rui-qin YANG,Chong WANG

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摘要：

目的 利用月经血特异性mRNA标志检测技术结合统计学方法，建立基于朴素贝叶斯和多元logistic回归方法的月经血鉴定模型，以定量区分月经血与其他体液。 方法 采集86份月经血、48份外周血、48份阴道分泌物、24份精液和24份唾液样本，经试剂盒提取样本RNA、反转录后得到cDNA，对5种月经血特异性标志，包括基质金属蛋白酶（matrix metalloproteinase，MMP）家族的成员MMP3、MMP7、MMP11，孕激素相关子宫内膜蛋白（progestogens associated endometrial protein，PAEP）和斯钙素-1（stanniocalcin-1，STC1）进行扩增和电泳检测分析。采用朴素贝叶斯和多元logistic回归对检测结果进行分析。 结果 朴素贝叶斯和多元logistic回归法构建的分类模型对月经血归类的准确率达88.37%和91.86%。在非月经血体液中，对外周血、唾液和精液的区分准确率普遍高于90%，分辨阴道分泌物时准确率较低，分别为16.67%和33.33%。 结论 mRNA检测技术结合统计学方法可对月经血建立分类判别模型，可用于区分月经血和其他体液，并对分析结果进行定量描述，在斑迹鉴定中具有一定的应用价值。

关键词: 法医遗传学, 体液斑迹鉴定, 月经血, mRNA, 朴素贝叶斯, 多元logistic回归, 金属蛋白酶

Abstract:

Objective To establish the menstrual blood identification model based on Na?ve Bayes and multivariate logistic regression methods by using specific mRNA markers in menstrual blood detection technology combined with statistical methods, and to quantitatively distinguish menstrual blood from other body fluids. Methods Body fluids including 86 menstrual blood, 48 peripheral blood, 48 vaginal secretions, 24 semen and 24 saliva samples were collected. RNA of the samples was extracted and cDNA was obtained by reverse transcription. Five menstrual blood-specific markers including members of the matrix metalloproteinase （MMP） family MMP3, MMP7, MMP11, progestogens associated endometrial protein （PAEP） and stanniocalcin-1 （STC1） were amplified and analyzed by electrophoresis. The results were analyzed by Na?ve Bayes and multivariate logistic regression. Results The accuracy of the classification model constructed was 88.37% by Na?ve Bayes and 91.86% by multivariate logistic regression. In non-menstrual blood samples, the distinguishing accuracy of peripheral blood, saliva and semen was generally higher than 90%, while the distinguishing accuracy of vaginal secretions was lower, which were 16.67% and 33.33%, respectively. Conclusion The mRNA detection technology combined with statistical methods can be used to establish a classification and discrimination model for menstrual blood, which can distignuish the menstrual blood and other body fluids, and quantitative description of analysis results, which has a certain application value in body fluid stain identification.

Key words: forensic genetics, body fluid marker identification, menstrual blood, mRNA, Na?ve Bayes, multivariate logistic regression, matrix metalloproteinase

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