A microfluidic system for rapid nucleic acid analysis based on real-time convective PCR is developed. To perform ‘sample-in, answer-out’ nucleic acid analysis, a microfluidic chip is developed to efficiently extract nucleic acid, and meanwhile convective PCR (CPCR) is applied for rapid nucleic acid amplification. With an integrated microfluidic chip consisting of reagent pre-storage chambers, a lysis & wash chamber, an elution chamber and a waste chamber, nucleic acid extraction based on magnetic beads can be automatically performed for a large size of test sample within a limited time. Based on an easy-to-operate strategy, different pre-stored reagents can be conveniently released for consecutive reaction at different steps. To achieve efficient mixing, a portable companion device is developed to introduce properly controlled 3-D actuation to magnetic beads in nucleic acid extraction. In CPCR amplification, PCR reagent can be spontaneously and repeatedly circulated between hot and cool zones of the reactor for space-domain thermal cycling based on pseudo-isothermal heating. A handheld real-time CPCR device is developed to perform nucleic acid amplification and in-situ detection. To extend the detection throughput, multiple handheld real-time CPCR devices can be grouped together by a common control system. It is demonstrated that influenza A (H1N1) viruses with the reasonable concentration down to 1.0 TCID
50
/ml can be successfully detected with the microfluidic system.
As a division of polymerase chain reaction (PCR), convective PCR (CPCR) is able to achieve highly efficient thermal cycling based on free thermal convection with pseudo-isothermal heating, which could be beneficial to point-of-care (POC) nucleic acid analysis. Similar to traditional PCR or isothermal amplification, due to a couple of issues, e.g., reagent, primer design, reactor, reaction dynamics, amplification status, temperature and heating condition, and other reasons, in some cases of CPCR tests, untypical real-time fluorescence curves with positive or negative tests will show up. Especially, when parts of the characteristics between untypical low-positive and negative tests are mixed together, it is difficult to discriminate between them using traditional cycle threshold (Ct) value method. To handle this issue which may occur in CPCR, traditional PCR or isothermal amplification, as an example, instead of using complicated mathematical modeling and signal processing strategy, an artificial intelligence (AI) classification method with artificial neural network (ANN) modeling is developed to improve the accuracy of nucleic acid detection. It has been proven that both the detection specificity and sensitivity can be significantly improved even with a simple ANN model. It can be estimated that, the developed method based on AI modeling can be adopted to solve similar problem with PCR, or isothermal amplification methods.
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