Grant: Collaborative Health Research Projects
University of Toronto
$322,767.00
Predicting patient outcome using machine learning technique
FY 2020-21
Summary
Chemical probes are key components of the_x000d_bioimaging toolbox, as they label biomolecules in cells and_x000d_tissues. The new challenge in bioimaging is to design chemical_x000d_probes for three-dimensional (3D) tissue imaging. In this_x000d_work, we discovered that light scattering of metal nanoparticles_x000d_can provide 3D imaging contrast in intact and transparent_x000d_tissues. The nanoparticles can act as a template for the_x000d_chemical growth of a metal layer to further enhance the_x000d_scattering signal. The use of chemically grown nanoparticles in_x000d_whole tissues can amplify the scattering to produce a 1.4_x000d_million-fold greater photon yield than obtained using common_x000d_?uorophores. These probes are non-photobleaching and can_x000d_be used alongside ?uorophores without interference. We_x000d_demonstrated three distinct biomedical applications: (a) molecular imaging of blood vessels, (b) tracking of nanodrug carriers intumors, and (c) mapping of lesions and immune cells in a multiple sclerosis mouse model. Our strategy establishes a distinct yetcomplementary set of imaging probes for understanding disease mechanisms in three dimensions.
Program
Collaborative Health Research Projects
Recipient
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
549560-2020
Name nom
Chan, Warren
Department département
Biomaterials and Biomedical Engineering, Inst. of (IBBME)
Organization id
31
Institution établissement
University of Toronto
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2020
Competition year année de concours
2020
Award amount
322767
Program id
CHRPJ
Program name en
Collaborative Health Research Projects
Program name fr
Projets de recherche concertée sur la santé
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
360
Committee name en
Collaborative Health Research Projects Selection Panel
Committee name fr
Comité de sélection de projets de recherche concertée sur la santé
Area of application code
9999
Area of application group en
Not available
Area of application group fr
Non disponible
Area of application en
Not available
Area of application fr
Non disponible
Research subject code
1901
Research subject group en
Biomedical engineering
Research subject group fr
Génie biomédical
Research subject en
Biomedical technology
Research subject fr
Technologie biomédicale
Installment
inst:1 - 2
Partie
2
Nb partie
2
Application title
Predicting patient outcome using machine learning technique
Keyword
NSERC
Application summary
Chemical probes are key components of the_x000d_bioimaging toolbox, as they label biomolecules in cells and_x000d_tissues. The new challenge in bioimaging is to design chemical_x000d_probes for three-dimensional (3D) tissue imaging. In this_x000d_work, we discovered that light scattering of metal nanoparticles_x000d_can provide 3D imaging contrast in intact and transparent_x000d_tissues. The nanoparticles can act as a template for the_x000d_chemical growth of a metal layer to further enhance the_x000d_scattering signal. The use of chemically grown nanoparticles in_x000d_whole tissues can amplify the scattering to produce a 1.4_x000d_million-fold greater photon yield than obtained using common_x000d_?uorophores. These probes are non-photobleaching and can_x000d_be used alongside ?uorophores without interference. We_x000d_demonstrated three distinct biomedical applications: (a) molecular imaging of blood vessels, (b) tracking of nanodrug carriers intumors, and (c) mapping of lesions and immune cells in a multiple sclerosis mouse model. Our strategy establishes a distinct yetcomplementary set of imaging probes for understanding disease mechanisms in three dimensions.
