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The cGAS-STING pathway in diabetic retinopathy and age-related macular degeneration.

ABSTRACT: Diabetic retinopathy and age-related macular degeneration are common retinal diseases with shared pathophysiology, including oxidative stress-induced inflammation. Cellular mechanisms responsible for converting oxidative stress into retinal damage are ill-defined but have begun to clarify. One common outcome of retinal oxidative stress is mitochondrial damage and subsequent release of mitochondrial DNA into the cytosol. This leads to activation of the cGAS-STING pathway, resulting in interferon release and disease-amplifying inflammation. This review summarizes the evolving link between aberrant cGAS-STING signaling and inflammation in common retinal diseases and provides prospective for targeting this system in diabetic retinopathy and age-related macular degeneration. Further defining the roles of this system in the retina is expected to reveal new disease pathology and novel therapeutic approaches.

SUBMITTER: Hu B

PROVIDER: S-EPMC10194038 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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The cGAS-STING pathway in diabetic retinopathy and age-related macular degeneration.

Hu Bo B Ma Jian-Xing JX Duerfeldt Adam S AS

Future medicinal chemistry 20230401 8

Diabetic retinopathy and age-related macular degeneration are common retinal diseases with shared pathophysiology, including oxidative stress-induced inflammation. Cellular mechanisms responsible for converting oxidative stress into retinal damage are ill-defined but have begun to clarify. One common outcome of retinal oxidative stress is mitochondrial damage and subsequent release of mitochondrial DNA into the cytosol. This leads to activation of the cGAS-STING pathway, resulting in interferon ...[more]

PMID: 37166075

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Project description:Diabetic retinopathy (DR), retinal vein occlusion (RVO), and age-related macular degeneration (AMD) pose significant global health challenges, often resulting in vision impairment and blindness. Automatic detection of these conditions is crucial, particularly in underserved rural areas with limited access to ophthalmic services. Despite remarkable advancements in artificial intelligence, especially convolutional neural networks (CNNs), their complexity can make interpretation difficult. In this study, we curated a dataset consisting of 15,089 color fundus photographs (CFPs) obtained from 8110 patients who underwent fundus fluorescein angiography (FFA) examination. The primary objective was to construct integrated models that merge CNNs with an attention mechanism. These models were designed for a hierarchical multilabel classification task, focusing on the detection of DR, RVO, AMD, and other fundus conditions. Furthermore, our approach extended to the detailed classification of DR, RVO, and AMD according to their respective subclasses. We employed a methodology that entails the translation of diagnostic information obtained from FFA results into CFPs. Our investigation focused on evaluating the models' ability to achieve precise diagnoses solely based on CFPs. Remarkably, our models showcased improvements across diverse fundus conditions, with the ConvNeXt-base + attention model standing out for its exceptional performance. The ConvNeXt-base + attention model achieved remarkable metrics, including an area under the receiver operating characteristic curve (AUC) of 0.943, a referable F1 score of 0.870, and a Cohen's kappa of 0.778 for DR detection. For RVO, it attained an AUC of 0.960, a referable F1 score of 0.854, and a Cohen's kappa of 0.819. Furthermore, in AMD detection, the model achieved an AUC of 0.959, an F1 score of 0.727, and a Cohen's kappa of 0.686. Impressively, the model demonstrated proficiency in subclassifying RVO and AMD, showcasing commendable sensitivity and specificity. Moreover, our models enhanced interpretability by visualizing attention weights on fundus images, aiding in the identification of disease findings. These outcomes underscore the substantial impact of our models in advancing the detection of DR, RVO, and AMD, offering the potential for improved patient outcomes and positively influencing the healthcare landscape.

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Dataset Information

The cGAS-STING pathway in diabetic retinopathy and age-related macular degeneration.

Publications

The cGAS-STING pathway in diabetic retinopathy and age-related macular degeneration.

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