Middle East

This book chapter advances SDG #3 and #10 by providing a brief history of PET imaging and the radiotracers that have had a significant impact for measuring the three signature AD-related neuropathologies related to AD and provides an overview of the research utilizing PET imaging in the DS population
This book chapter advances SDG #3 and #10 by reviewing the observed epidemiological links between normal and abnormal diurnal and seasonal rhythmicity, cognitive impairment, and ADRD. Then reviewing normal diurnal and seasonal rhythms of brain epigenetic modification and gene expression in model organisms. Finally, reviewing evidence for diurnal and seasonal rhythms of epigenetic modification and gene expression the human brain in aging, Alzheimer's disease, and other brain disorders.
This book chapter advances SDG #3 and #10 by discussing the advantages of performing genetic studies in people with DS, and then discussing the role of reported genes that are known to be associated with AD risk in adults with DS or in the general population. It also discusses how future longitudinal multiomic and imaging study can enhance our understanding of the biology of AD.
Elsevier,

Alzheimer’s Disease: Understanding Biomarkers, Big Data, and Therapy, Volume , 1 January 2021

This book chapter advances SDG #3 and #10 by discussing the operational aspects of deep learning solutions for Alzheimer’s disease, including the review of the advantages and limitations of using deep learning, and future directions on the applications of deep learning to Alzheimer’s disease.
This book chapter advances SDG #3 and #10 by systematically appraises the concepts and promising benefits of AI technology within healthcare for AD risk prediction across communities, and its possible concerns to be tackled prior to large-scale implementation.
Elsevier,

Alzheimer’s Disease: Understanding Biomarkers, Big Data, and Therapy, Volume , 1 January 2021

This book chapter advances SDG #3 and #10 by outlining the applications of big data to Alzheimer’s disease research as well as common methods used to collect and analyze big data. It also explores how big data research could be used to treat, diagnose, and understand Alzheimer’s disease.
Elsevier,

Alzheimer’s Disease: Understanding Biomarkers, Big Data, and Therapy, Volume , 1 January 2021

This book chapter advances SDG #3 and #10 by providing evidence that behavioral treatments are more effective than most pharmacological therapies at managing depression in Alzheimer’s disease.
This book chapter advances SDG #3 and #10 by stressing that a population health approach and a focus on promoting equity in health and access to care are critical to reducing the risk of AD and other dementias.
Elsevier,

Alzheimer’s Disease: Understanding Biomarkers, Big Data, and Therapy, Volume , 1 January 2021

This book chapter advances SDG #3 and #10 by presenting that (1) some of these depression scales do not correlate, suggesting that they assess different aspects of depression; (2) reports of depression in dementia vary based on depression in dementia scale used; and (3) severe memory impairment may impact the ability to assess depression in the patients using self-reports.
This book chapter advances SDG #3 and #10 by reviewing the extant literature on autophagy in AD and covers recent progress on the molecular mechanisms of NAD+-dependent mitophagy/autophagy regulation and mechanisms underlying the anti-AD potential of NAD+. Further studies to define the NAD+-mitophagy/autophagy axis may shed light on novel therapeutics to treat AD and potentially provide insights into other neurodegenerative diseases.

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