A novel isothermal detection for recombinase polymerase amplification with lateral flow (LF-RPA) was established for Borrelia burgdorferi (B. burgdorferi) detection in this study. This assay with high sensitivity and specificity can get a visible result without any additional equipment in 30 min. We designed a pair of primers according to recA gene of B. burgdorferi strains and a methodology evaluation was performed. The results showed that the RPA assay based on the recA gene was successfully applied in B. burgdorferi detection, and its specific amplification was only achieved from the genomic DNA of B. burgdorferi. The detection limit of the new assay was about 25 copies of the B. burgdorferi genomic DNA. Twenty Lyme borreliosis patients’ serum samples were detected by LF-RPA assay, real-time qPCR and nested-PCR. Results showed the LF-RPA assay is more effective than nested-PCR for its shorter reaction time and considerably higher detection rate. This method is of great value in clinical rapid detection for Lyme borreliosis. Using the RPA assay might be a megatrend for DNA detection in clinics and endemic regions. 相似文献
The prediction and optimization of weld quality characteristics in small scale resistance spot welding of TC2 titanium alloy were investigated. Grey relational analysis, neural network and genetic algorithm were applied separately. Quality characteristics were selected as nugget diameter, failure load, failure displacement and failure energy. Welding parameters to be optimized were set as electrode force, welding current and welding time. Grey relational analysis was conducted for a rough estimation of the optimum welding parameters. Results showed that welding current played a key role in weld quality improvement. Different back propagation neural network architectures were then arranged to predict multiple quality characteristics. Interaction effects of welding parameters were analyzed with the proposed neural network. Failure load was found more sensitive to the change of welding parameters than nugget diameter. Optimum welding parameters were determined by genetic algorithm. The predicted responses showed good agreement with confirmation experiments.