ArticleBMC microbiology2025
Microbial community profiling for forensic drowning diagnosis across locations and submersion times.
Article in BMC microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Estimation of postmortem submersion interval based on microbial community composition in human remains recovered from aquatic environments.Journal of forensic sciences · 2026Article
- Machine learning-assisted analysis of saliva-containing mixtures using a saliva-specific multi-CpG-microhaplotype marker system.International journal of legal medicine · 2026Article
- Forensic Microbiomics and Trace Microbial Evidence: Molecular Innovations for Linking Suspects, Objects, and Environments.Microorganisms · 2026Review
- Quantifying human-environment interactions through Bayesian modeling of species-resolved microbial transfer signatures: an exploratory proof-of-concept study.Frontiers in microbiology · 2026Article
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Authors and funding
11 authors.
Funding
Abstract
backgroundDrowning diagnosis has long been a critical issue in forensic research, influenced by various factors such as the environment and decomposition time. While traditional methods such as diatom analysis have limitations in decomposed remains, microbial community profiling offers a promising alternative. With the advancement of high-throughput sequencing technology, forensic microbiology has become a prominent focus in the field, providing new research avenues for drowning diagnosis. During drowning, microbial communities enter the lung tissue along with the water.
methodsIn this study, using a murine model, we collected samples from three rivers at random sites at postmortem intervals (PMI) of 1, 4, and 7 days to comprehensively evaluate the differences in microbial communities between mice subjected to drowning versus postmortem immersion.
resultsThe α-diversity analysis revealed that the observed Operational Taxonomic Units (OTUs) for the drowning group on day 1 was 234.77 ± 16.60, significantly higher than the postmortem immersion group (171.32 ± 9.22), indicating greater initial microbial richness in the drowning group. Additionally, Shannon index analysis showed a significant decline in evenness in the postmortem immersion group on day 7 (1.46 ± 0.09), whereas the drowning group remained relatively stable (2.38 ± 0.15), further indicating a rapid decrease in microbial diversity in the postmortem immersion group over time. PCoA analysis demonstrated that differences in microbial community composition between drowning and postmortem immersion groups were notably stable. Key microbial taxa differentiating the groups were identified through LEfSe analysis, with Enterococcaceae (family), Escherichia-Shigella (genus), and Proteus (genus), emerging as significant markers in drowning cases. A random forest model, trained using microbial community data, exhibited high predictive accuracy (AUC = 0.96) across locations and immersion times and identified microbial markers, including Enterococcaceae (family), Lactobacillales (order), Morganellaceae (family), as critical features influencing model performance.
conclusionThese findings underscore the potential of combining 16 S rRNA sequencing with machine learning as a powerful tool for drowning diagnosis, offering novel insights into forensic microbiology.
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