The Canadian Foundation for Innovation (CFI) has awarded $500,000 in funding to two York University researchers for unique projects focused on mitigating two key aspects that are having an impact on the COVID-19 pandemic – surface disinfection in hospitals and long-term care homes, and rapid, accessible testing for hospitals, airports and other points of need.
Lassonde School of Engineering Professor James Elder and Faculty of Science Professor Sergey Krylov are the principal investigators for the projects. The funding comes from Canada Foundation for Innovation’s (CFI) Exceptional Opportunities Fund for cutting edge research infrastructure.
Elder’s project, “Agile AI-Powered Autonomous Robotics for COVID-19 Disinfection” was awarded $275,000 by CFI. Krylov’s project “Development of Rapid and Accessible Diagnostics of COVID-19 using Small-molecule Probes Binding SARS-COV-2 Coat Proteins,” received $225,000 from CFI.
“York is thrilled to see Professors James Elder and Sergey Krylov secure these major grants. Their projects demonstrate the University’s ability to respond to the unique challenges posed by COVID-19 in these unprecedented times,” said Vice-President Research & Innovation (VPRI) Amir Asif. “Our researchers are called to serve the public through exploration and discovery and together with our partners in industry, government and community organizations, we are embracing our role in aiding the world’s recovery from the pandemic.”
Using AI to track down COVID-19 on surfaces
Ultraviolet-C (UV-C) light is known to be effective for surface disinfection against pathogens such as COVID-19. Unfortunately, existing delivery methods are incomplete, leaving high-risk “shadow” regions in areas such as the undersides of surfaces and doorknobs unsterilized. This project addresses this problem with agile, fully autonomous, robotic solutions driven by Artificial Intelligence that use recent advances in UV-C LED technology.
While their batteries are charging, these robots will use onboard cameras, computer vision and machine learning algorithms to visually monitor a room for human activity, building a map of high-risk areas. When activated, the robots will autonomously tour the room, optimally positioning and orienting articulating UV-C LED panels to irradiate and disinfect high-risk surface areas, including those not reachable by existing systems.
By ensuring maximal efficacy and coverage, this novel technology will reduce the infection rate at hospitals and long-term care facilities. The fully autonomous operation will protect healthcare workers and lower the strain on essential service personnel. The technology can be applied in many other priority domains.
This is an interdisciplinary research partnership between York University (navigation, computer vision and machine learning algorithms, software), CrossWing Inc. (robotic platform), Baycrest Health Sciences and Mon Sheong Home (long-term care evaluations and user acceptance) and Southlake Regional Health Centre (hospital evaluations).
Diagnostic testing for COVID-19 using small molecule probes from DNA-encoded libraries
Krylov’s project, “Development of Rapid and Accessible Diagnostics of COVID-19 using Small-molecule Probes Binding SARS-COV-2 Coat Proteins,” received $225,000 from CFI.
Effectively dealing with the ongoing pandemic and preventing future outbreaks requires rapid and accessible diagnosis of COVID-19. The current flagship method for diagnosing COVID-19 is based on detection of viral RNA. It is sensitive and specific but slow and requires highly specialized facilities.
Krylov and his research team are developing rapid and accessible COVID-19 diagnostics, which are based on detection of viral coat proteins. The diagnostic tests will be used not only in hospitals but also in other points of need such as airports. They are working on creating a simple test for self-application.
The proposed diagnostics will be facilitated by small-molecule probes (SMPs) selected from DNA-encoded libraries (DELs) for their ability to bind viral coat proteins. The interdisciplinary team has previously made important breakthroughs in the development of the SMP-DEL platform. Before the COVID-19 outbreak, efforts focused on applying this platform to diagnostics and drugs for cancer. Advantageously, it can be directly applied to the development of a new generation of SARS-CoV-2 tests.
In this project, the interdisciplinary research team will partner with COVID-19 testing facilities at two Toronto hospitals and with three companies. The researchers have all required equipment except for an analytical system for characterizing DELs and SMPs. The funding will be used for the system, which will then become an indispensable component of the lab’s long-term SMP-DEL research program as we pivot it towards COVID-19 detection and other critical health issues affecting Canadians.