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Grant: Alliance Grants

University of Waterloo

$154,000.00

An Integrated Microfluidic and Photonic Platform for Real-time Biomarker Quantification

EF 2022-23

Résumé
Molecular detection has been widely used in clinical diagnostics to detect a molecule that is a biomarker of disease (or health risk) in a sample taken from a patient. It plays a critical role in disease control and pathogen monitoring, such as diabetes mellitus control, covid-19 pathogen tracking and cancer diagnosis. In Canada, about 3.0 million people were living with diagnosed diabetes in 2013-2014 and by 2022, about 2.2 million new cases of diabetes are expected. More than 1.2 million Canadian have contracted with covid-19 virus since early 2020 and more than 24 thousand died of it. In 2020, an estimated 225,800 Canadian are expected to be diagnosed with cancer. In order to address these pressing challenges to Canadian public health and well-beings, it is urgently needed to develop a technology that can be conveniently deployed at low costs to fast track and quantify disease-related biomarkers (i.e., molecules). However, existing molecular detection techniques have to be carried out typically in well-equipped central biomedical laboratories. We propose in this project one solution to miniaturize the molecular diagnostics, enabling point of care (POC) diagnostics where the test results can be obtained without relying complex biomedical or clinical lab facilities. The UWaterloo teams will work closely with the industry partner (AIH)., combine our complementary expertise and experience in micro-photonics, nanofabrication, microfluidics, molecular detection, artificial intelligence and life science, and develop a universal and miniaturized biosensor technology that can quantify the level of biomarkers of interest by measuring the intensity of fluorescently-labelled microbeads using a compact, low-cost microfluidic and photonic platform. Such a platform paves the way for widespread applications of POC to detect various types of analytes, such as glucose, insulin, infectious virus and cancer biomarkers. It can greatly facilitate disease control, enable more effective treatment of rapidly-progressing afflictions, and even make a life-or-death difference with highly mortality rate diseases such as the ongoing covid-19 pandemic. This project provides excellent HQP training in the fields of nanotechnology, life science and artificial intelligence.
Programme
Alliance Grants
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data

Tel que publié

Application id
566333-2021
Name nom
Ban, Dayan D
Department département
Electrical & Computer Engineering
Organization id
33
Institution établissement
University of Waterloo
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2022
Competition year année de concours
2021
Award amount
154000
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
1100
Area of application group en
Health, education and social services
Area of application group fr
Santé publique, éducation et services sociaux
Area of application en
Health, education and social services
Area of application fr
Santé publique, éducation et services sociaux
Research subject code
2518
Research subject group en
Electrical and electronic engineering
Research subject group fr
Génie électrique et électronique
Research subject en
Instrumentation and measurements
Research subject fr
Instrumentation et mesures
Installment
inst:1
Partie
1
Nb partie
1
Application title
An Integrated Microfluidic and Photonic Platform for Real-time Biomarker Quantification
Application summary
Molecular detection has been widely used in clinical diagnostics to detect a molecule that is a biomarker of disease (or health risk) in a sample taken from a patient. It plays a critical role in disease control and pathogen monitoring, such as diabetes mellitus control, covid-19 pathogen tracking and cancer diagnosis. In Canada, about 3.0 million people were living with diagnosed diabetes in 2013-2014 and by 2022, about 2.2 million new cases of diabetes are expected. More than 1.2 million Canadian have contracted with covid-19 virus since early 2020 and more than 24 thousand died of it. In 2020, an estimated 225,800 Canadian are expected to be diagnosed with cancer. In order to address these pressing challenges to Canadian public health and well-beings, it is urgently needed to develop a technology that can be conveniently deployed at low costs to fast track and quantify disease-related biomarkers (i.e., molecules). However, existing molecular detection techniques have to be carried out typically in well-equipped central biomedical laboratories. We propose in this project one solution to miniaturize the molecular diagnostics, enabling point of care (POC) diagnostics where the test results can be obtained without relying complex biomedical or clinical lab facilities. The UWaterloo teams will work closely with the industry partner (AIH)., combine our complementary expertise and experience in micro-photonics, nanofabrication, microfluidics, molecular detection, artificial intelligence and life science, and develop a universal and miniaturized biosensor technology that can quantify the level of biomarkers of interest by measuring the intensity of fluorescently-labelled microbeads using a compact, low-cost microfluidic and photonic platform. Such a platform paves the way for widespread applications of POC to detect various types of analytes, such as glucose, insulin, infectious virus and cancer biomarkers. It can greatly facilitate disease control, enable more effective treatment of rapidly-progressing afflictions, and even make a life-or-death difference with highly mortality rate diseases such as the ongoing covid-19 pandemic. This project provides excellent HQP training in the fields of nanotechnology, life science and artificial intelligence.