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An excerpt from LCGC's e-learning tutorial on developing better GC methods at CHROMacademy.com
To obtain sensitive, robust, and reproducible gas chromatography (GC) methods, each stage of the chromatographic process needs to be carefully considered and optimized. It is also important to record and report as much detail within the method specification so that the method can be reproduced between operators, instruments, and laboratories. Table 1 represents a "blueprint" method specification with all of the information that is necessary to faithfully specify and reproduce a split–splitless GC method. Table 2 provides a blueprint starting point for the method development of a sample with unknown composition, but known to contain "trace" target analytes. Even if you are not developing methods - check the blueprint specifications against your GC methods. Do your methods contain all of the necessary details?
Table 1: Requirements for a properly specified splitless gas chromatography method with flame ionization detection (FID).
Table 2: Blueprint method specification for initial method development of a trace analysis using FID.
AI and GenAI Applications to Help Optimize Purification and Yield of Antibodies From Plasma
October 31st 2024Deriving antibodies from plasma products involves several steps, typically starting from the collection of plasma and ending with the purification of the desired antibodies. These are: plasma collection; plasma pooling; fractionation; antibody purification; concentration and formulation; quality control; and packaging and storage. This process results in a purified antibody product that can be used for therapeutic purposes, diagnostic tests, or research. Each step is critical to ensure the safety, efficacy, and quality of the final product. Applications of AI/GenAI in many of these steps can significantly help in the optimization of purification and yield of the desired antibodies. Some specific use-cases are: selecting and optimizing plasma units for optimized plasma pooling; GenAI solution for enterprise search on internal knowledge portal; analysing and optimizing production batch profitability, inventory, yields; monitoring production batch key performance indicators for outlier identification; monitoring production equipment to predict maintenance events; and reducing quality control laboratory testing turnaround time.