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Amyloid Fibrils

Amyloid Fibrils

Amyloid fibrils are aggregates of misfolded proteins. Under most circumstances, misfolded proteins are either refolded by chaperone proteins or degraded by the proteasome. However, in the case of a mutation or a disease, these proteins can accumulate to form large clusters and often further assemble to form elongated fibers, called fibrils.
Amyloid deposits were observed as early as 1639 in the liver and the spleen. In 1854, Rudolph Virchow performed iodine staining,...

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Related Experiment Video

Updated: Jul 27, 2025

Detecting Amyloid-β Accumulation via Immunofluorescent Staining in a Mouse Model of Alzheimer's Disease

Detecting Amyloid-β Accumulation via Immunofluorescent Staining in a Mouse Model of Alzheimer's Disease

Published on: April 19, 2021

Predicting amyloid-beta pathology in the general population.

Phuong Thuy Nguyen Ho¹, Joyce van Arendonk^1,2, Rebecca M E Steketee¹

¹Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Centre, Rotterdam, the Netherlands.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association

|June 12, 2023

Summary

Predicting amyloid beta (Aβ) positivity in aging adults is crucial for early Alzheimer's disease detection. New models using accessible factors show promise for identifying at-risk individuals cost-efficiently.

Keywords:

Alzheimer's disease amyloid-beta pathology dementia machine learning prediction models

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Related Experiment Videos

Last Updated: Jul 27, 2025

Detecting Amyloid-β Accumulation via Immunofluorescent Staining in a Mouse Model of Alzheimer's Disease

Detecting Amyloid-β Accumulation via Immunofluorescent Staining in a Mouse Model of Alzheimer's Disease

Published on: April 19, 2021

Full- versus Sub-Regional Quantification of Amyloid-Beta Load on Mouse Brain Sections

Full- versus Sub-Regional Quantification of Amyloid-Beta Load on Mouse Brain Sections

Published on: May 19, 2022

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Published on: November 14, 2017

Area of Science:

Neurology
Biomarker Discovery
Gerontology

Background:

Accurate prediction models for amyloid beta (Aβ) positivity in the aging population are needed for early Alzheimer's disease (AD) risk identification.
Such models could serve as cost-effective tools for identifying individuals at risk.

Purpose of the Study:

To develop and validate models for predicting Aβ positivity using easily obtainable predictors.
To assess the generalizability of these models in a population-based sample.

Main Methods:

Developed Aβ prediction models in the Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) Study (n=4,119).
Included predictors such as demographics, cognition, daily functioning, health, and lifestyle factors.
Validated models in the population-based Rotterdam Study (n=500).

Main Results:

The best model in the A4 Study (AUC=0.73) included age, APOE ε4, family history, cognition, walking, and sleep.
This model achieved higher accuracy in the Rotterdam Study (AUC=0.85).
The improvement over a model with only age and APOE ε4 was marginal.

Conclusions:

Developed Aβ prediction models using inexpensive, non-invasive measures.
Successfully applied models to a general population sample representative of older non-demented adults.
These models offer a viable approach for risk stratification in the general aging population.