AIMC Topic: Amyloid

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Formulation and optimization of silymarin-encapsulated binary micelles for enhanced amyloid disaggregation activity.

Drug development and industrial pharmacy
Silymarin (SLY) is a natural hydrophobic polyphenol that possesses antioxidant and amyloid fibril (Aβ) inhibition activity, but its activity is hindered due to low aqueous solubility. In this study, SLY is encapsulated in binary micelle (SLY-BM) that...

Single-molecule fluorescence imaging and deep learning reveal highly heterogeneous aggregation of amyloid-β 42.

Proceedings of the National Academy of Sciences of the United States of America
Polymorphism in the structure of amyloid fibrils suggests the existence of many different assembly pathways. Characterization of this heterogeneity is the key to understanding the aggregation mechanism and toxicity, but in practice it is extremely di...

Investigating Simultaneity for Deep Learning-Enhanced Actual Ultra-Low-Dose Amyloid PET/MR Imaging.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Diagnostic-quality amyloid PET images can be created with deep learning using actual ultra-low-dose PET images and simultaneous structural MR imaging. Here, we investigated whether simultaneity is required; if not, MR imaging-...

The Budapest Amyloid Predictor and Its Applications.

Biomolecules
The amyloid state of proteins is widely studied with relevance to neurology, biochemistry, and biotechnology. In contrast with nearly amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and antiparallel β-she...

Translating amyloid PET of different radiotracers by a deep generative model for interchangeability.

NeuroImage
It is challenging to compare amyloid PET images obtained with different radiotracers. Here, we introduce a new approach to improve the interchangeability of amyloid PET acquired with different radiotracers through image-level translation. Deep genera...

Modeling autosomal dominant Alzheimer's disease with machine learning.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease.

Improved amyloid burden quantification with nonspecific estimates using deep learning.

European journal of nuclear medicine and molecular imaging
PURPOSE: Standardized uptake value ratio (SUVr) used to quantify amyloid-β burden from amyloid-PET scans can be biased by variations in the tracer's nonspecific (NS) binding caused by the presence of cerebrovascular disease (CeVD). In this work, we p...

Visual interpretation of [F]Florbetaben PET supported by deep learning-based estimation of amyloid burden.

European journal of nuclear medicine and molecular imaging
PURPOSE: Amyloid PET which has been widely used for noninvasive assessment of cortical amyloid burden is visually interpreted in the clinical setting. As a fast and easy-to-use visual interpretation support system, we analyze whether the deep learnin...

Generalization of deep learning models for ultra-low-count amyloid PET/MRI using transfer learning.

European journal of nuclear medicine and molecular imaging
PURPOSE: We aimed to evaluate the performance of deep learning-based generalization of ultra-low-count amyloid PET/MRI enhancement when applied to studies acquired with different scanning hardware and protocols.

Automatic identification of crossovers in cryo-EM images of murine amyloid protein A fibrils with machine learning.

Journal of microscopy
Detecting crossovers in cryo-electron microscopy images of protein fibrils is an important step towards determining the morphological composition of a sample. Currently, the crossover locations are picked by hand, which introduces errors and is a tim...