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Updated: Jul 23, 2025

Author Spotlight: Diatom Testing for Forensic Drowning Examination

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Published on: November 10, 2023

Inference of drowning sites using bacterial composition and random forest algorithm.

Qin Su^1,2, Chengliang Yang³, Ling Chen³

¹Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.

Frontiers in Microbiology

|July 13, 2023

Summary

This summary is machine-generated.

Forensic microbiology using 16S ribosomal RNA gene (16S rDNA) sequencing can identify drowning sites. A random forest model accurately predicted drowning locations based on bacterial genus in drowned rabbit lungs.

Keywords:

16S rDNA amplicon sequencing Pearl River bacterial composition drowning site random forest

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Area of Science:

Forensic microbiology
Environmental microbiology
Molecular biology

Background:

Determining the drowning site is crucial in forensic investigations, especially for bodies recovered from rivers.
Aquatic microorganisms, particularly bacteria, entering the bloodstream during drowning are a focus for forensic science.
16S ribosomal RNA gene (16S rDNA) amplicon sequencing offers a novel approach to analyze bacterial communities.

Purpose of the Study:

To evaluate the potential of 16S rDNA amplicon sequencing for inferring drowning sites.
To investigate differences in bacterial composition in river water and drowned animal lung tissues.
To develop a predictive model for identifying drowning locations.

Main Methods:

Collected water and lung tissue samples from drowned rabbits at different sites along the Pearl River.
Performed 16S rDNA amplicon sequencing to analyze bacterial communities.
Developed a random forest model using bacterial genus-level data from lung tissues.

Main Results:

Significant differences in bacterial composition were observed between different river regions and corresponding lung tissues.
The random forest model achieved 100% accuracy in predicting drowning sites using lung bacterial genus data.
Bacterial species found in river water were not consistently detected in drowned lung tissues.

Conclusions:

16S rDNA amplicon sequencing is a promising tool for forensic determination of drowning sites.
A random forest model based on lung bacterial composition can accurately predict drowning locations.
The study highlights the potential of microbial analysis in forensic investigations of drowning.