HPLC 2023 Grant Support
The EuChemS-DAC Sample Preparation Study Group and Network will support one European PhD student working in sample preparation research by covering their early registration fees for HPLC 2023, due to be held 18–22 June in Düsseldorf, Germany. To apply for this grant, applicants must:
(i) send a copy of their submitted abstract and give the type of preferred presentation (oral or poster);
(ii) send a detailed resume;
(iii) include a recommendation letter from their supervisor.
All information must be collected and emailed to sampleprep@tuc.gr by 20 March 2023. The subject line of the email must state: Application for HPLC2023 student grant.
Applicants must not have received another grant from EuChemS-DAC Sample Preparation Study Group and Network during 2022, nor applied for another 2023 PhD grant to the Network.
For further information on the HPLC conference, programme, and venue please visit: https://www.hplc2023-duesseldorf.com/
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.
