Liquid Class Optimization For critical decisions and accurate results, it is essential to create accurate & precise liquid classes that mimic the properties of the solution of interest, which can be very difficult when trying to automate an assay on a large liquid handler. The common and standard optimization approach has to be challenged; liquid handlers' built-in classes are affected by the type of liquid, its temperature, type & quality of tips, and plasticware utilized. By creating alternative solutions, such as one to mimic the viscosity of beads, a commonly used yet difficult reagent across many types of assays, and applying the MVS (Multichannel Verification System), one can increase the accuracy & precision of liquid class optimization. In large liquid handlers, such as Hamilton STARs & Tecan Fluents, this optimization approach actually tests the variety by changing the settings. So, as informed by density and viscosity of the liquid in conjunction with using the MVS system, the organizations of the liquid classes reduce the duration taken to avail an assay for production and, therefore, mitigate errors that normally come from liquid handling. Next Generation Sequencing The NGS (Next Generation Sequencing) has gained much scientific attention, especially from Artel. This approach has been informed by the increase in genomic testing numbers, which calls for good data quality and productivity levels. The accuracy of volume transfers is what forms the basis of NGS. Certain liquids are difficult to mix especially volatile solvents, small volume reagents and those which undergo hard mixing processes. This makes the genomic assays consume time and are expensive to process. In this aspect, the MVS comes in using an automated liquid handler or multichannel pipette to determine the amount of liquid transfer (Bruno, Rossella, and Gabriella 78). kmoIn an NGS sample, using an ALH is quite complicated but can be achieved with adequate resource allocation. However, the NGS still acts by enhancing monitoring of the liquid at every transfer stage, including mixing. With this, the essay is always easy to optimize, hence saving money due to fewer reagents used.
Work Cited Bruno, Rossella, and Gabriella Fontanini. "Next generation sequencing for gene fusion analysis in lung cancer: a literature review." Diagnostics 10.8 (2020): 521.