This study supports SDG 3 and 10 by reporting that Māori and Pacific people with type 2 diabetes have consistently poorer health outcomes than European patients, indicating the need for specific policies and interventions to better manage type 2 diabetes in these subpopulations.
Elsevier,

The Lancet Global Health, Volume 9, Issue 4, April 2021, Pages e489–e551

This Lancet Global Health Commission advances addresses SDG 3 directly, and SDGs 1, 2, 4, 5, 8 and 10 indirectly, by comprehensively demonstrating how improving eye health by treating and preventing vision impairment and vision loss can not only advance SDG 3—improving health and wellbeing for all—but also contribute to poverty reduction, zero hunger, quality education, gender equality, and decent work and economic growth. The findings of this report frame eye health as a development issue and highlight that, with a growing ageing population globally, urgent and concerted action is needed to meet unmet eye health needs globally, including incorporating equitable eye care into countries’ universal health coverage plans.
Recent pay and hiring discrimination allegations have resulted in high-dollar settlements for Google and a hospitality management company. Developments discussed in this news article cover topics related to SDG 5 (gender equality) and SDG 10 (reduced inequalities).
This book chapter advances SDG #3 and #10 by providing therapeutic strategies that can be employed in clinical trials for AD in DS will be discussed as well as their underlying scientific rationale.
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 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.

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