Title
Alzheimer's Disease: A Silent Pandemic-A Systematic Review on the Situation and Patent Landscape of the Diagnosis
Date Issued
01 December 2022
Access level
metadata only access
Resource Type
review
Author(s)
Publisher(s)
Bentham Science Publishers
Abstract
Background: Alzheimer's disease (AD) is characterized by cognitive impairment, tau protein deposits, and amyloid beta plaques. AD impacted 44 million people in 2016, and it is estimated to affect 100 million people by 2050. AD is disregarded as a pandemic compared with other diseases. To date, there is no effective treatment or diagnosis. Objective: We aimed to discuss the current tools used to diagnose COVID-19, point out their potential to be adapted for AD diagnosis, and review the landscape of existing patents in the AD field and future perspectives for AD diagnosis. Methods: We carried out a scientific screening following a research strategy in PubMed; Web of Science; the Derwent Innovation Index; the KCI-Korean Journal Database; SciELO; the Russian Science Citation index; and the CDerwent, EDerwent, and MDerwent index databases. Results: A total of 326 from 6,446 articles about AD and 376 from 4,595 articles about COVID-19 were analyzed. Of these, AD patents were focused on biomarkers and neuroimaging with no accurate, validated diagnostic methods, and only 7% of kit development patents were found. In comparison, COVID-19 patents were 60% about kit development for diagnosis; they are highly accurate and are now commercialized. Conclusion: AD is still neglected and not recognized as a pandemic that affects the people and economies of all nations. There is a gap in the development of AD diagnostic tools that could be filled if the interest and effort that has been invested in tackling the COVID-19 emergency could also be applied for innovation.
Start page
355
End page
378
Volume
16
Issue
4
Language
English
OCDE Knowledge area
Neurociencias
Epidemiología
Subjects
Scopus EID
2-s2.0-85138107583
PubMed ID
Source
Recent Patents on Biotechnology
ISSN of the container
18722083
Sponsor(s)
Funding text 1
Funding: This research was funded by Consejo Nacional de Ciencia, Tecnologia e Innovacion Tecnologica de Peru (grant N◦ 024-2019-Fondecyt-BM-INC.INV).
Funding text 2
Acknowledgments: H.L.B.-C. gratefully acknowledged the computational support from the Consejo Nacional de Ciencia, Tecnologia e Innovacion Tecnologica de Peru (Grant 151-2020-FONDECYT).
Sources of information:
Directorio de Producción Científica
Scopus