Developing an interpretation model for body fluid identification.

Publisher: Elsevier Science Ireland Country of Publication: Ireland NLM ID: 7902034 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-6283 (Electronic) Linking ISSN: 03790738 NLM ISO Abbreviation: Forensic Sci Int Subsets: MEDLINE

Publication: Limerick : Elsevier Science Ireland
Original Publication: Lausanne, Elsevier Sequoia.

Saliva*/chemistry ; Cervix Mucus*/chemistry ; Menstruation* ; Semen*/chemistry ; Bayes Theorem* ; RNA, Messenger*/analysis ; Real-Time Polymerase Chain Reaction* ; Machine Learning*; Humans ; Female ; Logistic Models ; Discriminant Analysis ; Male ; Body Fluids/chemistry ; Reverse Transcriptase Polymerase Chain Reaction ; Models, Statistical ; Blood Chemical Analysis

Criminal investigations, particularly sexual assaults, frequently require the identification of body fluid type in addition to body fluid donor to provide context. In most cases this can be achieved by conventional methods, however, in certain scenarios, alternative molecular methods are required. An example of this is the detection of menstrual fluid and vaginal material, which are not able to be identified using conventional techniques. Endpoint reverse-transcription PCR (RT-PCR) is currently used for this purpose to amplify body fluid specific messenger RNA (mRNA) transcripts in forensic casework. Real-time quantitative reverse-transcription PCR (RT-qPCR) is a similar method but utilises fluorescent markers to generate quantitative results in the form of threshold cycle (C q ) values. Despite the uncertainty surrounding body fluid identification, most interpretation guidelines utilise categorical statements. Probabilistic modelling is more realistic as it reflects biological variation as well as the known performance of the method. This research describes the application of various machine learning models to single-source mRNA profiles obtained by RT-qPCR and assesses their performance. Multinomial logistic regression (MLR), Naïve Bayes (NB), and linear discriminant analysis (LDA) were used to discriminate between the following body fluid categories: saliva, circulatory blood, menstrual fluid, vaginal material, and semen. We identified that the performance of MLR was somewhat improved when the quantitative dataset of the original C q values was used (overall accuracy of approximately 0.95) rather than presence/absence coded data (overall accuracy of approximately 0.94). This indicates that the quantitative information obtained by RT-qPCR amplification is useful in assigning body fluid class. Of the three classification methods, MLR performed the best. When we utilised receiver operating characteristic curves to observe performance by body fluid class, it was clear that all methods found difficulty in classifying menstrual blood samples. Future work will involve the modelling of body fluid mixtures, which are common in samples analysed as part of sexual assault investigations.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier B.V. All rights reserved.)

Keywords: Body fluididentification; MRNA profiling; Probabilistic interpretation method; Quantitative PCR

0 (RNA, Messenger)

Date Created: 20240430 Date Completed: 20240511 Latest Revision: 20240511

20240512

10.1016/j.forsciint.2024.112032

38688209