Diabetic retinopathy (DR) is a microvascular disease affecting the eyes of diabetic patients, and is the most prevalent complication of diabetes mellitus. Vision improvement is not possible in the majority of DR patients. Several studies have indicated that microvascular changes, inflammation, oxidative stress, and retinal neurodegeneration are involved in the pathogenesis of DR. Therefore, there is an urgent need for the development of new and effective treatment for DR. Understanding the molecular mechanisms involved in the pathogenesis of disease will pave a way for better treatment and management of DR. This article has emphasized the molecular pathogenesis and treatment of DR.
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http://dx.doi.org/10.2174/0115733998259940231105200251 | DOI Listing |
Sensors (Basel)
December 2024
Computer Science Department, Instituto Nacional de Astrofísica Óptica y Electrónica, Luis Enrrique Erro No. 1, Sta. María Tonantzintla, Puebla 72840, Mexico.
Accurate synthetic image generation is crucial for addressing data scarcity challenges in medical image classification tasks, particularly in sensor-derived medical imaging. In this work, we propose a novel method using a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and nearest-neighbor interpolation to generate high-quality synthetic images for diabetic retinopathy classification. Our approach enhances training datasets by generating realistic retinal images that retain critical pathological features.
View Article and Find Full Text PDFInt J Mol Sci
January 2025
Department of Ophthalmology, National Taiwan University Hospital, No. 7, Chung Shan S. Rd. (Zhongshan S. Rd.), Zhongzheng Dist., Taipei City 100225, Taiwan.
Diabetic retinopathy (DR) is a complication of diabetes, characterized by progressive microvascular dysfunction that can result in vision loss. Chronic hyperglycemia drives oxidative stress, endothelial dysfunction, and inflammation, leading to retinal damage and complications such as neovascularization. Current treatments, including anti-VEGF agents, have limitations, necessitating the exploration of alternative therapeutic strategies.
View Article and Find Full Text PDFInt J Mol Sci
December 2024
Department of Diabetes, Institute of Rural Health, 20-090 Lublin, Poland.
Telomere shortening has been linked to type 2 diabetes (T2D) and its complications. This study aims to determine whether leukocyte telomere length (LTL) could be a useful marker in predicting the onset of complications in patients suffering from T2D. Enrolled study subjects were 147 T2D patients.
View Article and Find Full Text PDFSci Rep
January 2025
Southwest Medical University, Sichuan, 646099, China.
To determine the correlations between six serological inflammatory markers, namely the systemic immune-inflammation index (SII), systemic inflammatory response index (SIRI), aggregate index of systemic inflammation (AISI), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR), and various stages of type 2 diabetic retinopathy (T2DR). Additionally, the diagnostic value of these markers in T2DR was evaluated. Clinical data were collected from a total of 397 patients with type 2 diabetes who visited the ophthalmology department at Mian Yang Central Hospital and the Affiliated Hospital of Southwest Medical University from January 2023 to December 2023.
View Article and Find Full Text PDFJAMA Netw Open
January 2025
Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Importance: Determining spectacle-corrected visual acuity (VA) is essential when managing many ophthalmic diseases. If artificial intelligence (AI) evaluations of macular images estimated this VA from a fundus image, AI might provide spectacle-corrected VA without technician costs, reduce visit time, or facilitate home monitoring of VA from fundus images obtained outside of the clinic.
Objective: To estimate spectacle-corrected VA measured on a standard eye chart among patients with diabetic macular edema (DME) in clinical practice settings using previously validated AI algorithms evaluating best-corrected VA from fundus photographs in eyes with DME.
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