PET Imaging in Animal Models of Alzheimer's Disease
The successful development and translation of PET imaging agents targeting β-amyloid plaques and hyperphosphorylated tau tangles have allowed for detection of these hallmarks of Alzheimer's disease (AD) antemortem. Amyloid and tau PET have been incorporated into the A/T/N scheme for AD characte...
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Published in | Frontiers in neuroscience Vol. 16; p. 872509 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
Frontiers Media S.A
24.05.2022
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Subjects | |
Online Access | Get full text |
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Summary: | The successful development and translation of PET imaging agents targeting β-amyloid plaques and hyperphosphorylated tau tangles have allowed for
detection of these hallmarks of Alzheimer's disease (AD) antemortem. Amyloid and tau PET have been incorporated into the A/T/N scheme for AD characterization and have become an integral part of ongoing clinical trials to screen patients for enrollment, prove drug action mechanisms, and monitor therapeutic effects. Meanwhile, preclinical PET imaging in animal models of AD can provide supportive information for mechanistic studies. With the recent advancement of gene editing technologies and AD animal model development, preclinical PET imaging in AD models will further facilitate our understanding of AD pathogenesis/progression and the development of novel treatments. In this study, we review the current state-of-the-art in preclinical PET imaging using animal models of AD and suggest future research directions. |
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Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-3 content type line 23 ObjectType-Review-1 This article was submitted to Neurodegeneration, a section of the journal Frontiers in Neuroscience Edited by: Ruiqing Ni, ETH Zürich, Switzerland Reviewed by: Nicolas Tournier, Commissariat à l’Energie Atomique et aux Energies Alternatives (CEA), France; Steven Liang, Massachusetts General Hospital and Harvard Medical School, United States |
ISSN: | 1662-4548 1662-453X 1662-453X |
DOI: | 10.3389/fnins.2022.872509 |