AIMC Topic: Amyloid

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The clinical feasibility of deep learning-based classification of amyloid PET images in visually equivocal cases.

European journal of nuclear medicine and molecular imaging
PURPOSE: Although most deep learning (DL) studies have reported excellent classification accuracy, these studies usually target typical Alzheimer's disease (AD) and normal cognition (NC) for which conventional visual assessment performs well. A clini...

The Classifying Autoencoder: Gaining Insight into Amyloid Assembly of Peptides and Proteins.

The journal of physical chemistry. B
Despite the importance of amyloid formation in disease pathology, the understanding of the primary structure?activity relationship for amyloid-forming peptides remains elusive. Here we use a new neural-network based method of analysis: the classifyin...

Fluorescent silicon nanoparticles inhibit the amyloid fibrillation of insulin.

Journal of materials chemistry. B
Amyloid fibrillation of proteins is likely a key factor leading to the development of amyloidosis-associated diseases. Inhibiting amyloid fibrillation has become a crucial therapeutic strategy. Water-soluble, fluorescent silicon nanoparticles (SiNPs)...

Ultra-Low-Dose F-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs.

Radiology
Purpose To reduce radiotracer requirements for amyloid PET/MRI without sacrificing diagnostic quality by using deep learning methods. Materials and Methods Forty data sets from 39 patients (mean age ± standard deviation [SD], 67 years ± 8), including...

Adaptive template generation for amyloid PET using a deep learning approach.

Human brain mapping
Accurate spatial normalization (SN) of amyloid positron emission tomography (PET) images for Alzheimer's disease assessment without coregistered anatomical magnetic resonance imaging (MRI) of the same individual is technically challenging. In this st...

Amino acid composition predicts prion activity.

PLoS computational biology
Many prion-forming proteins contain glutamine/asparagine (Q/N) rich domains, and there are conflicting opinions as to the role of primary sequence in their conversion to the prion form: is this phenomenon driven primarily by amino acid composition, o...

Combined thioflavin T-Congo red fluorescence assay for amyloid fibril detection.

Methods and applications in fluorescence
Fluorescence represents one of the most powerful tools for the detection and structural characterization of the pathogenic protein aggregates, amyloid fibrils. The traditional approaches to the identification and quantification of amyloid fibrils are...

Classification of amyloid status using machine learning with histograms of oriented 3D gradients.

NeuroImage. Clinical
Brain amyloid burden may be quantitatively assessed from positron emission tomography imaging using standardised uptake value ratios. Using these ratios as an adjunct to visual image assessment has been shown to improve inter-reader reliability, howe...

Prediction of Peptide and Protein Propensity for Amyloid Formation.

PloS one
Understanding which peptides and proteins have the potential to undergo amyloid formation and what driving forces are responsible for amyloid-like fiber formation and stabilization remains limited. This is mainly because proteins that can undergo str...

Navigating Homogeneous Graph Paths Through Amyloidogenic and Non-Amyloidogenic Hexapeptides.

Journal of computational chemistry
Hexapeptides are increasingly applied as model systems for studying the amyloidogenic properties of oligo- and polypeptides. It is possible to construct 64 million different hexapeptides from the twenty proteinogenic amino acid residues. Today's expe...