Invention Description
In the event of a terrorist attack or nuclear accident where large populations of individuals are exposed to radiation, it is critical for authorities to have the ability to quantify and confirm the levels of absorbed radiation. It is also essential for authorities to identify if an individual is exposed to greater than 2Gy where it can help them prioritize the people who are in requirement of immediate medical attention. Identification of the individuals as exposed versus non-exposed can be achieved through a point-of-care test, where a high throughput quantitative analysis method can be used to estimate the absorbed radiation for those who have been exposed. Machine learning based approaches can be used to identify and validate potential biomarkers that can confirm and quantify the absorbed dose of radiation.
Researchers at ASU developed an innovative technology that leverages 20 radiation-responsive biomarkers analyzed via next-generation sequencing and machine learning to predict absorbed radiation doses swiftly and accurately. Designed as a point-of-care solution, the paper-based test can classify radiation exposure rapidly outside conventional laboratory environments, enabling timely medical interventions. Prototype testing has demonstrated effective performance across various radiation levels.
Potential Applications
· Emergency response and triage following radiation exposure incidents
· Medical diagnostics in radiological and nuclear event management
· Military and civilian radiation dose monitoring
· Occupational health screening for workers exposed to radiation
· Healthcare facilities needing rapid biodosimetry solutions
Benefits and Advantages
· Rapid detection of radiation exposure within hours instead of days
· Low-cost, paper-based test format suitable for point-of-care use
· High prediction accuracy by integrating next-generation sequencing and machine learning
· Capability to classify specific radiation dose levels using targeted biomarkers