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Quantitative neuropathological assessment to investigate cerebral multi-morbidity

Overview of attention for article published in Alzheimer's Research & Therapy, November 2014
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Title
Quantitative neuropathological assessment to investigate cerebral multi-morbidity
Published in
Alzheimer's Research & Therapy, November 2014
DOI 10.1186/s13195-014-0085-y
Pubmed ID
Authors

Johannes Attems, Janna H Neltner, Peter T Nelson

Abstract

The aging brain is characterized by the simultaneous presence of multiple pathologies, and the prevalence of cerebral multi-morbidity increases with age. To understand the impact of each subtype of pathology and the combined effects of cerebral multi-morbidity on clinical signs and symptoms, large clinico-pathological correlative studies have been performed. However, such studies are often based on semi-quantitative assessment of neuropathological hallmark lesions. Here, we discuss some of the new methods for high-throughput quantitative neuropathological assessment. These methods combine increased quantitative rigor with the added technical capacity of computers and networked analyses. There are abundant new opportunities - with specific techniques that include slide scanners, automated microscopes, and tissue microarrays - and also potential pitfalls. We conclude that quantitative and digital neuropathologic approaches will be key resources to further elucidate cerebral multi-morbidity in the aged brain and also hold the potential for changing routine neuropathologic diagnoses.

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Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 21%
Student > Master 5 21%
Student > Doctoral Student 3 13%
Student > Bachelor 2 8%
Professor 2 8%
Other 3 13%
Unknown 4 17%
Readers by discipline Count As %
Neuroscience 7 29%
Medicine and Dentistry 5 21%
Agricultural and Biological Sciences 3 13%
Psychology 2 8%
Immunology and Microbiology 1 4%
Other 1 4%
Unknown 5 21%