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1.
Mod Pathol ; 36(6): 100124, 2023 06.
Article in English | MEDLINE | ID: mdl-36841434

ABSTRACT

Ulcerative colitis is a chronic inflammatory bowel disease that is characterized by a relapsing and remitting course. Assessment of disease activity critically informs treatment decisions. In addition to endoscopic remission, histologic remission is emerging as a treatment target and a key factor in the evaluation of disease activity and therapeutic efficacy. However, manual pathologist evaluation is semiquantitative and limited in granularity. Machine learning approaches are increasingly being developed to aid pathologists in accurate and reproducible scoring of histology, enabling precise quantitation of clinically relevant features. Here, we report the development and validation of convolutional neural network models that quantify histologic features pertinent to ulcerative colitis disease activity, directly from hematoxylin and eosin-stained whole slide images. Tissue and cell model predictions were used to generate quantitative human-interpretable features to fully characterize the histology samples. Tissue and cell predictions showed comparable agreement to pathologist annotations, and the extracted slide-level human-interpretable features demonstrated strong correlations with disease severity and pathologist-assigned Nancy histological index scores. Moreover, using a random forest classifier based on 13 human-interpretable features derived from the tissue and cell models, we were able to accurately predict Nancy histological index scores, with a weighted kappa (κ = 0.91) and Spearman correlation (⍴ = 0.89, P < .001) when compared with pathologist consensus Nancy histological index scores. We were also able to predict histologic remission, based on the absence of neutrophil extravasation, with a high accuracy of 0.97. This work demonstrates the potential of computer vision to enable a standardized and robust assessment of ulcerative colitis histopathology for translational research and improved evaluation of disease activity and prognosis.


Subject(s)
Colitis, Ulcerative , Inflammatory Bowel Diseases , Humans , Colitis, Ulcerative/drug therapy , Artificial Intelligence , Severity of Illness Index , Inflammatory Bowel Diseases/pathology , Intestinal Mucosa/pathology , Colonoscopy
2.
Proc Natl Acad Sci U S A ; 114(33): 8770-8775, 2017 08 15.
Article in English | MEDLINE | ID: mdl-28760994

ABSTRACT

Fibrils and oligomers are the aggregated protein agents of neuronal dysfunction in ALS diseases. Whereas we now know much about fibril architecture, atomic structures of disease-related oligomers have eluded determination. Here, we determine the corkscrew-like structure of a cytotoxic segment of superoxide dismutase 1 (SOD1) in its oligomeric state. Mutations that prevent formation of this structure eliminate cytotoxicity of the segment in isolation as well as cytotoxicity of the ALS-linked mutants of SOD1 in primary motor neurons and in a Danio rerio (zebrafish) model of ALS. Cytotoxicity assays suggest that toxicity is a property of soluble oligomers, and not large insoluble aggregates. Our work adds to evidence that the toxic oligomeric entities in protein aggregation diseases contain antiparallel, out-of-register ß-sheet structures and identifies a target for structure-based therapeutics in ALS.


Subject(s)
Amyotrophic Lateral Sclerosis/metabolism , Superoxide Dismutase-1/metabolism , Amyotrophic Lateral Sclerosis/genetics , Animals , Crystallography, X-Ray/methods , Mice , Motor Neurons/metabolism , Mutation/genetics , Protein Conformation, beta-Strand , Superoxide Dismutase-1/genetics
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