Combined metabolomics and tandem machine-learning models for wound age estimation: a novel analytical strategy

Abstract

Wound age estimation is one of the most challenging and indispensable issues for forensic pathologists. Although many methods based on physical findings and biochemical tests can be used to estimate wound age, an objective and reliable method for inferring the time interval after injury remains difficult. In present study, endogenous metabolites of contused skeletal muscle were investigated to estimate the time interval after injury. Animal model of skeletal muscle injury was established using Sprague–Dawley rat, and the contused muscles were sampled at 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, and 48 h post-contusion (n = 9). Then, the samples were analyzed using ultra-performance liquid chromatography coupled with high resolution mass spectrometry. A total of 43 differential metabolites in contused muscle were determined by metabolomics method. They were applied to construct a two-level tandem prediction model for wound age estimation based on multilayer perceptron (MLP) algorithm. As a result, all muscle samples were eventually divided into the following subgroups: 4 h, 8 h, 12 h, 16–20 h, 24–32 h, 36–40 h, and 44–48 h. The tandem model exhibited a robust performance and achieved a prediction accuracy of 92.6% which was much higher than that of the single model. In summary, the MLP-MLP tandem machine-learning model based on metabolomics data can be used as a novel strategy for wound age estimation in future forensic casework.

From flesh to bones: Multi‐omics approaches in forensic science

“…This approach could also be applied to the analysis of other weapons to improve the forensic re-construction surrounding MOD. Instead, Cao et al [180] focused on the application of metabolomics and tandem machine learning for the estimation of wound age inflicted on rats and sampled from 4 to 48 hours postcontusion. They found consistent changes in the metabolomics profile at specific time intervals and highlighted 43 endogenous compounds that could be used for wound ageing.…”