Machine learning-based detection of adventitious microbes in T-cell therapy cultures using long-read sequencing

• Main Authors: Strutt, James P. B., Natarajan, Meenubharathi, Lee, Elizabeth, Teo, Denise Bei Lin, Sin, Wei-Xiang, Cheung, Ka-Wai, Chew, Marvin, Thazin, Khaing, Barone, Paul W., Wolfrum, Jacqueline M., Williams, Rohan B. H., Rice, Scott A., Springs, Stacy L.
• 其他作者: Singapore-MIT Alliance for Research and Technology
• 格式: Article
• 語言: English
• 出版: 2024
• 主題: Medicine, Health and Life Sciences; Adventitious agents; Machine learning
• 在線閱讀: https://hdl.handle.net/10356/173925

Assuring that cell therapy products are safe before releasing them for use in patients is critical. Currently, compendial sterility testing for bacteria and fungi can take 7-14 days. The goal of this work was to develop a rapid untargeted approach for the sensitive detection of microbial contaminant...