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In 2017, we showed that nanoscale physical properties of protein aggregates from the cerebral spinal fluid and blood of patients are altered during Alzheimer's disease (AD) pathogenesis and that these properties can be used as a new class of "physical biomarkers." Using a computational algorithm, developed to integrate these biomarkers and cognitive assessments, we demonstrated an approach to diagnose AD and predict its progression. Real-time diagnostic updates of progression could be made on the basis of the changes in the physical biomarkers and the cognitive assessment scores of patients over time. Additionally, the Nyquist-Shannon sampling theorem was used to determine the minimum number of necessary patient checkups to effectively predict disease progression. This integrated computational approach can generate patient-specific, personalized signatures for AD diagnosis and prognosis. The paper published in Science Advances. Press release from The Ohio State University about the study, and some media news about the study.
In 2017, we showed that nanoscale physical properties of protein aggregates from the cerebral spinal fluid and blood of patients are altered during Alzheimer's disease (AD) pathogenesis and that these properties can be used as a new class of "physical biomarkers." Using a computational algorithm, developed to integrate these biomarkers and cognitive assessments, we demonstrated an approach to diagnose AD and predict its progression. Real-time diagnostic updates of progression could be made on the basis of the changes in the physical biomarkers and the cognitive assessment scores of patients over time. Additionally, the Nyquist-Shannon sampling theorem was used to determine the minimum number of necessary patient checkups to effectively predict disease progression. This integrated computational approach can generate patient-specific, personalized signatures for AD diagnosis and prognosis. The paper published in Science Advances. Press release from The Ohio State University about the study, and some media news about the study.
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SCIENCE ADVANCESno. 1 (2024): eadj7067-eadj7067
Nanomedicinepp.81-116, (2019)
Science Advancesno. 7 (2017): e1700669-e1700669
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