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A novel method for determining postmortem interval based on the metabolomics of multiple organs combined with ensemble learning techniques

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Abstract

Determining postmortem interval (PMI) is one of the most challenging and essential endeavors in forensic science. Developments in PMI estimation can take advantage of machine learning techniques. Currently, applying an algorithm to obtain information on multiple organs and conducting joint analysis to accurately estimate PMI are still in the early stages. This study aimed to establish a multi-organ stacking model that estimates PMI by analyzing differential compounds of four organs in rats. In a total of 140 rats, skeletal muscle, liver, lung, and kidney tissue samples were collected at each time point after death. Ultra-performance liquid chromatography coupled with high-resolution mass spectrometry was used to determine the compound profiles of the samples. The original data were preprocessed using multivariate statistical analysis to determine discriminant compounds. In addition, three interrelated and increasingly complex patterns (single organ optimal model, single organ stacking model, multi-organ stacking model) were established to estimate PMI. The accuracy and generalized area under the receiver operating characteristic curve of the multi-organ stacking model were the highest at 93% and 0.96, respectively. Only 1 of the 14 external validation samples was misclassified by the multi-organ stacking model. The results demonstrate that the application of the multi-organ combination to the stacking algorithm is a potential forensic tool for the accurate estimation of PMI.

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Data and code availability

Part of the relevant code is within the Supporting Information files. All relevant data and code are available from the corresponding author upon request.

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Acknowledgements

We thank the National Natural Science Foundation of China. Thanks for the equipment support provided by the School of Forensic Medicine, Hebei Medical University, and the assistance of Professor Chun-ling Ma and Professor Di Wen.

Funding

This work was supported by grants from the National Key R&D Program of China (grant no. 2018YFC0807204) and the National Natural Science Foundation of China (81971795, 81901924).

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Authors and Affiliations

  1. School of Forensic Medicine, Shanxi Medical University, Yuci District, No. 98, University Street, Wujinshan Town, Jinzhong, Shanxi, 030604, People’s Republic of China

    Xiao-jun Lu, Jian Li, Xue Wei, Na Li, Li-hong Dang, Guo-shuai An, Qiu-xiang Du, Qian-qian Jin, Jie Cao, Ying-yuan Wang & Jun-hong Sun

  2. Criminal Investigation Detachment, Baotou Public Security Bureau, No. 191, Jianshe Road, Qingshan District, Baotou City, Inner Mongolia Autonomous Region, 014030, People’s Republic of China

    Xiao-jun Lu

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  1. Xiao-jun Lu
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  2. Jian Li
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  4. Na Li
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Contributions

Jun-hong Sun, Ying-yuan Wang, and Jie Cao conceived the idea; Xiao-jun Lu, Jian Li, and Xue Wei conducted the data analysis and drafted the manuscript; Na Li, Li-hong Dang, and Guo-shuai An established the animal model; Qiu-xiang Du and Qian-qian Jin conceived the study and performed some interpretation of the results. All authors read and approved the final version of the manuscript.

Corresponding authors

Correspondence to Jie Cao, Ying-yuan Wang or Jun-hong Sun.

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This article does not contain any studies with human participants performed by any of the authors and there is no patient consent. All experiments of rats were performed according to the ‘Guiding Principles in the Use and Care of Animals’ (NIH publication no. 85–23, revised 1996) and were approved by the Institutional Animal Care and Use Committee of Shanxi Medical University, China (rat batch number: SCXK [Jin] [2009-0001].

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The authors declare no competing interests.

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If there is any conflict, please consider Ph.D. Jun-hong Sun as the primary corresponding author.

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Lu, Xj., Li, J., Wei, X. et al. A novel method for determining postmortem interval based on the metabolomics of multiple organs combined with ensemble learning techniques. Int J Legal Med 137, 237–249 (2023). https://doi.org/10.1007/s00414-022-02844-8

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