Protein science : a publication of the Protein Society
Sep 1, 2025
Kinked- sheets are short peptide motifs that appear as distortions in strands and often mediate formation of reversible amyloid fibrils in prion-like proteins. Standard methods for assigning secondary structures cannot distinguish these esoteric mo...
The PET Imaging Site Qualification Program for amyloid positron emission tomography (PET) in Japan includes visual evaluation of the cylinder phantom. This visual evaluation requires observation of the entire image of the phantom and confirmation of ...
International journal of biological macromolecules
Jun 1, 2025
Protein misfolding and aggregation play a central role in the progression of neurodegenerative diseases such as Alzheimer's and Parkinson's. These aggregates manifest either as structured amyloid fibrils enriched in β-sheet conformations or as irregu...
Background Visual assessment of amyloid PET scans relies on the availability of radiologist expertise, whereas quantification of amyloid burden typically involves MRI for processing and analysis, which can be computationally expensive. Purpose To dev...
Background PET can be used for amyloid-tau-neurodegeneration (ATN) classification in Alzheimer disease, but incurs considerable cost and exposure to ionizing radiation. MRI currently has limited use in characterizing ATN status. Deep learning techniq...
Dipole-spread function (DSF) engineering reshapes the images of a microscope to maximize the sensitivity of measuring the 3D orientations of dipole-like emitters. However, severe Poisson shot noise, overlapping images, and simultaneously fitting high...
PURPOSE: This study was to develop a convolutional neural network (CNN) model with a residual learning framework to predict the full-time 18F-florbetaben (18F-FBB) PET/CT images from corresponding short-time scans.
PURPOSE: The aim of this study was to evaluate random forests (RFs) to identify ROIs on F-florbetapir and F-FDG PET associated with Montreal Cognitive Assessment (MoCA) score.
PURPOSE: To evaluate random forests (RFs) as a supervised machine learning algorithm to classify amyloid brain PET as positive or negative for amyloid deposition and identify key regions of interest for stratification.
A novel data-based machine learning algorithm for predicting amyloid aggregation rates is reported in this paper. Based on a highly nonlinear projection from 16 intrinsic features of a protein and 4 extrinsic features of the environment to the protei...
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