Construction of an Analysis Model of mRNA Markers in Menstrual Blood Based on Naïve Bayes and Multivariate Logistic Regression Methods / 法医学杂志
Journal of Forensic Medicine
; (6): 447-451, 2023.
Article
in En
| WPRIM
| ID: wpr-1009376
Responsible library:
WPRO
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.
Key words
Full text:
1
Index:
WPRIM
Main subject:
Saliva
/
Semen
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Body Fluids
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RNA, Messenger
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Logistic Models
/
Bayes Theorem
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Forensic Genetics
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Menstruation
Limits:
Female
/
Humans
Language:
En
Journal:
Journal of Forensic Medicine
Year:
2023
Type:
Article