Detection and identification of Neisseria subflava in saliva stains using recombinant polymerase amplification combined with lateral flow dipstick
Year: 2026
Type: article
Abstract: In forensic practice, the unequivocal identification of questioned biological stains is indispensable for evidence triage, investigative guidance, and the attribution of criminal responsibility. Among these traces, saliva is one of the most prevalent yet challenging biological matrices to detect. Classical analytical strategies—chemical colorimetry, enzymatic assays, immunochromatography, vibrational spectroscopy, and microscopical techniques—frequently suffer from elevated false-positive and false-negative rates, insufficient analytical sensitivity, and stringent demands regarding sample integrity and environmental background. These limitations are further exacerbated by the intrinsic scarcity and unpredictable degradation of forensic specimens, which often preclude acquisition of material in requisite quantity and quality. To overcome these constraints, we have developed an integrated assay that couples recombinase polymerase amplification with lateral-flow dipstick technology, selectively targeting Neisseria subflava —a microorganism highly enriched in human saliva—for definitive identification of saliva stains. The optimised system exhibits exceptional analytical parameters: 100% specificity, an overall accuracy of 87.1%, and a limit of detection at 0.625 copies/μL, while retaining functionality on samples aged under ambient conditions for extended periods. Importantly, the isothermal reaction proceeds efficiently at human body temperature, enabling on-scene deployment. This capability allows immediate probative determination at the crime scene and provides critical guidance for subsequent targeted DNA extraction and individual identification workflows.
Source: Frontiers in Microbiology
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7225150489