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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: Aug 5, 2025

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

Independent study demonstrates amyloid probability score accurately indicates amyloid pathology.

Ilana Fogelman¹, Tim West¹, Joel B Braunstein¹

¹C2N Diagnostics, St. Louis, Missouri, USA.

Annals of Clinical and Translational Neurology

|March 28, 2023

Summary

The PrecivityAD blood test, using the amyloid probability score (APS), accurately identifies brain amyloid plaques in individuals with cognitive impairment. This Alzheimer's disease biomarker test shows high sensitivity and specificity.

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Area of Science:

Neurology
Biomarker Discovery
Medical Diagnostics

Background:

The amyloid probability score (APS) is derived from the PrecivityAD® blood test, utilizing plasma Aβ42/40 ratio, ApoE proteotype, and age.
It assesses the likelihood of brain amyloid plaques in individuals with cognitive impairment undergoing Alzheimer's disease evaluation.

Purpose of the Study:

To independently validate the efficacy of the pre-established APS algorithm and its cutoff values.
To confirm the algorithm's ability to differentiate between amyloid-positive and amyloid-negative individuals.

Main Methods:

Diagnostic performance analysis of the PrecivityAD test in 200 Australian Imaging, Biomarker & Lifestyle Flagship Study of Aging (AIBL) participants.
Inclusion of cognitively impaired individuals and healthy controls with available blood samples and amyloid PET imaging.

Main Results:

In individuals aged 60+ with CDR ≥0.5, the APS algorithm predicted amyloid PET with 84.9% sensitivity and 96% specificity (excluding inconclusive results).
A high APS correlated with a greater likelihood of abnormal amyloid plaques and ongoing amyloid accumulation.

Conclusions:

The study confirms APS as a reliable indicator for predicting brain amyloid plaques.
Exploratory findings suggest APS may also be effective for screening in cognitively unimpaired individuals, warranting further research.