Microbial community profiling for forensic drowning diagnosis across locations and submersion times
These 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.
Authors
- Qingqing Xiang
- Chao Liu (ORCID: https://orcid.org/0000-0002-4252-4019)
- Xiaohui Chen (ORCID: https://orcid.org/0000-0003-1418-8190)
- Ling Chen (ORCID: https://orcid.org/0000-0001-7699-4820)
- Xiaofeng Zhang (ORCID: https://orcid.org/0000-0003-2738-3286)
- Chengliang Yang (ORCID: https://orcid.org/0000-0003-1151-5571)
- Weibin Wu (ORCID: https://orcid.org/0000-0003-3637-754X)
- Qin Su
- Jian Zhao
- Zhonghao Yu
Institutions
- Ministry of Public Security of the People's Republic of China (CN)
- Institute of Forensic Science (CN)
- China National Electric Apparatus Research Institute (China) (CN)
- Southern Medical University (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- BMC Microbiology
- Published
- 2025-04-24
- DOI
- https://doi.org/10.1186/s12866-025-03902-y
- Citations
- 3
- Primary Topic
- Injury Epidemiology and Prevention
- Type
- article
- Field-Weighted Citation Impact
- 5.64
Funders
- National Natural Science Foundation of China
- Ministry of Public Security of the People's Republic of China