Research on accurate pipetting complementation model for high-throughput molecular detection platform

Abstract

The incidence of infectious diseases has risen in recent years, leading to a significant surge in the demand for medical molecular detection. High-throughput molecular detection platforms play a crucial role in facilitating rapid and efficient molecular detection. Among the various techniques employed in high-throughput molecular detection, microliquid transfer stands out as one of the most frequently utilized methods. However, ensuring the accuracy of liquid transfer poses a challenge due to variations in the physical and chemical properties of different samples and reagents. In this study, a pipetting complementation model was developed specifically for the serum, paraffin oil, and throat swabs. The aim was to enhance the transfer accuracy of diverse liquids in the context of high-throughput molecular detection, ultimately ensuring detection reliability and stability. The experimental findings revealed notable improvements in pipetting accuracy after compensating for the three liquids. In particular, the pipetting error rates decreased by 52.5, 96, and 71.4% for serum, paraffin oil, and throat swabs, respectively. These results underscore the model's effectiveness in providing reliable support for the precise transfer of liquids on the high-throughput molecular detection platform.