Evidence map›Paper›PMID 38802694›Full record

ArticleInternational journal of legal medicine2024

A fundamental study on postmortem submersion interval estimation by metabolomics analyzing of gastrocnemius muscle from submersed rat models in freshwater.

Fu-Yuan Zhang, Lin-Lin Wang, Kuo Zeng, Wen-Wen Dong, Hui-Ya Yuan, Xing-Yu Ma, Zi-Wei Wang, Yu Zhao, Rui Zhao, Da-Wei Guan

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Article in International journal of legal medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

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Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Fu-Yuan Zhang *Department of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China.
Lin-Lin Wang *Department of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China.
Kuo Zeng *Institute of Evidence Law and Forensic Science, China University of Political Science and Law, Beijing, China.
Wen-Wen DongDepartment of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China.
Hui-Ya YuanDepartment of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China.
Xing-Yu MaInstitute of Evidence Law and Forensic Science, China University of Political Science and Law, Beijing, China.
Zi-Wei WangDepartment of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China.
Yu ZhaoDepartment of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China.
Rui ZhaoDepartment of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China. rzhao@cmu.edu.cn.
Da-Wei GuanDepartment of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China. dwguan@cmu.edu.cn.

Funding

Key Technologies Research and Development Program 2018YFC0807204Key Technologies Research and Development Program 2022YFC3302002
6 · The paper itself

Abstract

In forensic practice, determining the postmortem submersion interval (PMSI) and cause-of-death of cadavers in aquatic ecosystems has always been challenging task. Traditional approaches are not yet able to address these issues effectively and adequately. Our previous study proposed novel models to predict the PMSI and cause-of-death based on metabolites of blood from rats immersed in freshwater. However, with the advance of putrefaction, it is hardly to obtain blood samples beyond 3 days postmortem. To further assess the feasibility of PMSI estimation and drowning diagnosis in the later postmortem phase, gastrocnemius, the more degradation-resistant tissue, was collected from drowned rats and postmortem submersion model in freshwater immediately after death, and at 1 day, 3 days, 5 days, 7 days, and 10 days postmortem respectively. Then the samples were analyzed with liquid chromatography-tandem mass spectrometry (LC-MS/MS) to investigate the dynamic changes of the metabolites. A total of 924 metabolites were identified. Similar chronological changes of gastrocnemius metabolites were observed in the drowning and postmortem submersion groups. The difference in metabolic profiles between drowning and postmortem submersion groups was only evident in the initial 1 day postmortem, which was faded as the PMSI extension. Nineteen metabolites representing temporally-dynamic patterns were selected as biomarkers for PMSI estimation. A regression model was built based on these biomarkers with random forest algorithm, which yielded a mean absolute error (± SE) of 5.856 (± 1.296) h on validation samples from an independent experiment. These findings added to our knowledge of chronological changes in muscle metabolites from submerged vertebrate remains during decomposition, which provided a new perspective for PMSI estimation.

Indexed as

DrowningFresh WaterImmersionMetabolomicsModels, AnimalMuscle, SkeletalPostmortem ChangesAnimalsBiomarkersChromatography, LiquidMaleRatsRats, Sprague-DawleyTandem Mass SpectrometryBiomarkersBiomarkerMetabolomicsMusclePostmortem submersion intervalRandom forest

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.