Authors
Jichao Xu, Xiaohui Chen, Qin Su, Dang'en Gu, Tianchun Lin, Jianmiao Zhang, Liang Han, Ming Cai, Jian Zhao, Quyi Xu
Published in
Fa yi xue za zhi. Volume 42. Issue 3. Pages 215-219. Jun 25, 2026.
Abstract
Conventional diatom-based assays for drowning diagnosis present multiple inherent limitations, including prolonged testing durations, poor adaptability to field examination, compromised detecta‑ bility in aquatic environments with low diatom abundance, and reliance on a single type of forensic evidence. To address the above limitations, our team selected drowning-relevant planktonic microorga‑ nisms, including diatoms, cyanobacteria, and planktonic bacteria, as molecular biomarkers and established a rapid planktonic microorganism DNA detection system based on real-time quantitative polymerase chain reaction (qPCR). Correspondingly, a vehicle-mounted field DNA detection workstation was developed to enable on-site testing. Technology transfer was facilitated through an integrated industry-academia-research collaborative model involving public security forensic research divisions and industrial enterprises. Meanwhile, system performance validation was implemented in accordance with industrial specifications for diatom identification and standardized evaluation criteria for qPCR instruments. The developed system enabled on-site detection to be completed within 1.5 h, with a limit of detection (LOD) of 0.000 1 ng. All core technical parameters of the instrument exceeded existing industrial standards, with the advantages of convenient sample collection, high sensitivity, strong specificity, rapid testing, and ease of operation. This system not only satisfies the practical demands of forensic scene investigations but also complements morphological analysis by establishing an integrated "molecular biology + morphology" evidence framework. Furthermore, it provides a new strategy for the rapid diagnosis of drowning and offers valuable insights into the technology transfer achievements within the field of forensic science.
PMID:
42732980
Bibliographic data and abstract were imported from PubMed on 14 Sep 2026.
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