Feb 24, 2022

Project taps AI to validate Cyclospora inactivation methods

Scott Lenaghan, Ph.D. with the University of Tennessee, is enlisting artificial intelligence and machine learning to speed detection of Cyclospora cayetanensis’ infectious life stage.

“Right now, the only way to know whether it’s viable is a sporulation assay,” he said. “You have to determine that they have sporulated, and the whole process is labor intensive.”

The eventual high-throughput automated system also should significantly increase the number of potential Cyclospora inactivation methods for which researchers are able to screen. As part of the two-year project, Lenaghan and his colleagues plan to validate four inactivation strategies: gamma radiation, ultra-violet light, ozonation and chlorine dioxide gas. In addition, they plan to screen numerous antimicrobials and identify at least two novel inactivation methods.

“Right now, there are no inactivating strategies in the produce environment,” Lenaghan said. “The industry needs some guidance or direction. At least we can give them the data.”

Joining Lenaghan are co-principal investigators Qixin Zhong, Ph.D., and Mark Morgan, Ph.D., both with the University of Tennessee.

Zhong has developed a library of GRAS, or generally recognized as safe, compounds that will be screened for their potential to inactivate Cyclospora. Morgan brings expertise in process engineering with a focus on ozonation and chlorine dioxide.

Cyclospora has a complex life cycle and requires a human host as an intermediary to complete it. An infected human sheds unsporulated – or immature, non-infective – oocysts in their feces. It takes one to two weeks of favorable conditions outside the host for the oocysts to mature, sporulate and become infective to a human who consumes them.