Construction of an Analysis Model of mRNA Markers in Menstrual Blood Based on Naïve Bayes and Multivariate Logistic Regression Methods.
10.12116/j.issn.1004-5619.2021.511207
- Author:
Qi ZHANG
1
;
He-Miao ZHAO
2
;
Kang YANG
3
;
Jing CHEN
2
;
Rui-Qin YANG
1
;
Chong WANG
2
Author Information
1. People's Public Security University of China, Beijing 100038, China.
2. Key Laboratory of Forensic Genetics, Institute of Forensic Science, Ministry of Public Security, Beijing 100038, China.
3. Xi'an Public Security Bureau, Xi'an 710038, China.
- Publication Type:Journal Article
- Keywords:
Naïve Bayes;
body fluid marker identification;
forensic genetics;
mRNA;
matrix metalloproteinase;
menstrual blood;
multivariate logistic regression
- MeSH:
Female;
Humans;
RNA, Messenger/metabolism*;
Bayes Theorem;
Logistic Models;
Menstruation;
Body Fluids;
Saliva;
Semen;
Forensic Genetics/methods*
- From:
Journal of Forensic Medicine
2023;39(5):447-451
- CountryChina
- Language:English
-
Abstract:
OBJECTIVES: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.
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 Naïve Bayes and multivariate logistic regression.
RESULTS: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.
CONCLUSIONS: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.
