Evaluation of Gradient-Variant Quantum Dot Fluorescence Technology for Rapid Detection of Brucella

Objective To establish a brucellosis monitoring and testing technique applicable for the rapid field screening of natural epidemic diseases.

Methods A rapid testing technique for Brucella was developed based on a double-antibody sandwich testing model using gradient-variant quantum dots as fluorescent tracers. The sensitivity, linearity, precision, and specificity of the technique were evaluated using suspensions of standard Brucella strains. Methodological comparisons across different sample types were conducted to assess the consistency of the test results.

Results The gradient-variant quantum dots detection method was evaluated with standard Brucella strains, exhibiting a sensitivity of 1 × 10³ CFU/mL and a linear correlation coefficient (r) of 0.994 (95% CI, 0.933-1.055). The maximum coefficient of variation was 12.94% in repeated tests, showing good specificity. A comparative assessment of 305 clinical samples was conducted using the Brucella gradient-variant quantum dots detection method, the Rose Bengal plate agglutination test (RBT), and the serum agglutination test (SAT), yielding a Kappa value of 0.95, indicating almost perfect agreement. Additionally, a comparative assessment of 110 environmental samples collected on-site was conducted using the Brucella gradient-variant quantum dots detection method and quantitative real-time PCR (qPCR). The Kappa values for aerosol collection fluid, surface wipes, and wool samples were all above 0.83, demonstrating near-perfect agreement. For fecal and soil samples, the Kappa values were above 0.62, indicating substantial agreement.

Conclusion The Brucella detection method based on gradient-variant quantum dots technology is simple and can be conducted rapidly. The detection method demonstrates high sensitivity, linearity, precision, and specificity. It shows consistent performance in clinical sample testing. It is well-suited for field rapid screening of natural epidemic diseases in field settings and shows good application prospects in the monitoring, prevention, and rapid detection of zoonotic diseases.