UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Quantitative Assessment of Experimental Ocular Inflammatory Disease

Bradley, LJ; Ward, A; Hsue, MCY; Liu, J; Copland, DA; Dick, AD; Nicholson, LB; (2021) Quantitative Assessment of Experimental Ocular Inflammatory Disease. Frontiers in Immunology , 12 , Article 630022. 10.3389/fimmu.2021.630022. Green open access

[thumbnail of 630022_Manuscript-Frontiers2021.pdf]
Preview
Text
630022_Manuscript-Frontiers2021.pdf - Accepted Version

Download (10MB) | Preview

Abstract

Ocular inflammation imposes a high medical burden on patients and substantial costs on the health-care systems that mange these often chronic and debilitating diseases. Many clinical phenotypes are recognized and classifying the severity of inflammation in an eye with uveitis is an ongoing challenge. With the widespread application of optical coherence tomography in the clinic has come the impetus for more robust methods to compare disease between different patients and different treatment centers. Models can recapitulate many of the features seen in the clinic, but until recently the quality of imaging available has lagged that applied in humans. In the model experimental autoimmune uveitis (EAU), we highlight three linked clinical states that produce retinal vulnerability to inflammation, all different from healthy tissue, but distinct from each other. Deploying longitudinal, multimodal imaging approaches can be coupled to analysis in the tissue of changes in architecture, cell content and function. This can enrich our understanding of pathology, increase the sensitivity with which the impacts of therapeutic interventions are assessed and address questions of tissue regeneration and repair. Modern image processing, including the application of artificial intelligence, in the context of such models of disease can lay a foundation for new approaches to monitoring tissue health.

Type: Article
Title: Quantitative Assessment of Experimental Ocular Inflammatory Disease
Open access status: An open access version is available from UCL Discovery
DOI: 10.3389/fimmu.2021.630022
Publisher version: https://doi.org/10.3389/fimmu.2021.630022
Language: English
Additional information: © 2021 Bradley, Ward, Hsue, Liu, Copland, Dick and Nicholson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Keywords: Uveitis, EAU, OCT, image processing, automated analysis
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Ophthalmology
URI: https://discovery.ucl.ac.uk/id/eprint/10131261
Downloads since deposit
32Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item