Zhang Qi, Zhao He-Miao, Yang Kang, Chen Jing, Yang Rui-Qin, Wang Chong
People's Public Security University of China, Beijing 100038, China.
Wafangdian Public Security Bureau, Dalian 116300, Liaoning Province, China.
Fa Yi Xue Za Zhi. 2023 Oct 25;39(5):447-451. doi: 10.12116/j.issn.1004-5619.2021.511207.
To establish the menstrual blood identification model based on Naïve 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.
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 Naïve Bayes and multivariate logistic regression.
The accuracy of the classification model constructed was 88.37% by Naïve 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.
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.
通过在月经血检测技术中使用特定的mRNA标记物并结合统计方法，建立基于朴素贝叶斯和多元逻辑回归方法的月经血识别模型，以定量区分月经血与其他体液。
收集了包括86份月经血、48份外周血、48份阴道分泌物、24份精液和24份唾液样本的体液。提取样本的RNA并通过逆转录获得cDNA。对包括基质金属蛋白酶（MMP）家族成员MMP3、MMP7、MMP11、孕激素相关子宫内膜蛋白（PAEP）和骨钙素-1（STC1）在内的5种月经血特异性标记物进行扩增并通过电泳分析。结果采用朴素贝叶斯和多元逻辑回归进行分析。
构建的分类模型的准确率，朴素贝叶斯法为88.37%，多元逻辑回归法为91.86%。在非月经血样本中，外周血、唾液和精液的区分准确率普遍高于90%，而阴道分泌物的区分准确率较低，分别为16.67%和33.33%。
mRNA检测技术结合统计方法可用于建立月经血的分类和鉴别模型，能够区分月经血和其他体液，并对分析结果进行定量描述，在体液斑痕鉴定中具有一定的应用价值。
