School of Electronic and Information Engineering, Soochow University, Suzhou, 215006, China; CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China.
CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China.
Biosens Bioelectron. 2022 Mar 1;199:113873. doi: 10.1016/j.bios.2021.113873. Epub 2021 Dec 8.
For digital polymerase chain reaction (PCR), data classification is always a crucial task. The dynamic real-time amplification process information of each partition is always ignored in typical digital PCR analysis, which can easily lead to inaccurate outcomes. In this work, an integrated device that offers real-time chip-based digital PCR analysis was established. In addition, an enhanced process-based classification model (PAM) was built and trained. And then the device and the analytical model were employed in classification tasks for different concentrations of Epstein-Barr Virus (EBV) plasmid quantification assays. The results indicated that the real-time analysis device achieved a linearity of 0.97, the classification method was able to distinguish the false-positive curves, and the recognition error of positive wells was decreased by 64.4% compared with typical static analysis techniques when low concentrations of samples were tested. With these advantages, it is supposed that the real-time digital PCR analysis apparatus and the improved classification method can be employed to enhance the performance of digital PCR technology.
对于数字聚合酶链反应（PCR），数据分类始终是一项关键任务。在典型的数字 PCR 分析中，各分区的动态实时扩增过程信息通常会被忽略，这容易导致结果不准确。在这项工作中，建立了一种集成的实时基于芯片的数字 PCR 分析设备。此外，还构建并训练了一种增强的基于过程的分类模型（PAM）。然后，该设备和分析模型被用于不同浓度 Epstein-Barr 病毒（EBV）质粒定量检测的分类任务。结果表明，实时分析设备的线性度为 0.97，分类方法能够区分假阳性曲线，与典型的静态分析技术相比，当测试低浓度样本时，阳性孔的识别错误减少了 64.4%。具有这些优势，实时数字 PCR 分析仪器和改进的分类方法可用于提高数字 PCR 技术的性能。
