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: Age-related macular degeneration (AMD) is an eye disorder affecting predominantly the older people above the age of 50 years in which the macular region of the retina deteriorates, resulting in the loss of central vision. The key factors associated with the pathogenesis of AMD are age, smoking, dietary, and genetic risk factors. There are few associated and plausible genes involved in AMD pathogenesis. Common genetic variants (with a minor allele frequency of >5% in the population) near the complement genes explain 40–60% of the heritability of AMD. The complement system is a group of proteins that work together to destroy foreign invaders, trigger inflammation, and remove debris from cells and tissues. Genetic changes in and around several complement system genes, including the CFH, contribute to the formation of drusen and progression of AMD. Similarly, Matrix metalloproteinases (MMPs) that are normally involved in tissue remodeling also play a critical role in the pathogenesis of AMD. MMPs are involved in the degradation of cell debris and lipid deposits beneath retina but with age their functions get affected and result in the drusen formation, succeeding to macular degeneration. In this review, AMD pathology, existing knowledge about the normal and pathological role of complement system proteins and MMPs in the eye is reviewed. The scattered data of complement system proteins, MMPs, drusenogenesis, and lipofusogenesis have been gathered and discussed in detail. This might add new dimensions to the understanding of molecular mechanisms of AMD pathophysiology and might help in finding new therapeutic options for AMD.
: Age-related macular degeneration (AMD) is an eye disorder affecting predominantly the older people above the age of 50 years in which the macular region of the retina deteriorates, resulting in the loss of central vision. The key factors associated with the pathogenesis of AMD are age, smoking, dietary, and genetic risk factors. There are few associated and plausible genes involved in AMD pathogenesis. Common genetic variants (with a minor allele frequency of >5% in the population) near the complement genes explain 40–60% of the heritability of AMD. The complement system is a group of proteins that work together to destroy foreign invaders, trigger inflammation, and remove debris from cells and tissues. Genetic changes in and around several complement system genes, including the CFH, contribute to the formation of drusen and progression of AMD. Similarly, Matrix metalloproteinases (MMPs) that are normally involved in tissue remodeling also play a critical role in the pathogenesis of AMD. MMPs are involved in the degradation of cell debris and lipid deposits beneath retina but with age their functions get affected and result in the drusen formation, succeeding to macular degeneration. In this review, AMD pathology, existing knowledge about the normal and pathological role of complement system proteins and MMPs in the eye is reviewed. The scattered data of complement system proteins, MMPs, drusenogenesis, and lipofusogenesis have been gathered and discussed in detail. This might add new dimensions to the understanding of molecular mechanisms of AMD pathophysiology and might help in finding new therapeutic options for AMD.
Diabetic Retinopathy (DR) is a serious eye condition that occurs due to high blood sugar levels in patients with Diabetes Mellitus. If left untreated, DR can potentially result in blindness. Using automated neural network-based methods to grade DR shows potential for early detection. However, the uneven and non-quadrilateral forms of DR lesions provide difficulties for traditional Convolutional Neural Network (CNN)-based architectures. To address this challenge and explore a novel algorithm architecture, this work delves into the usage of contrasting cluster assignments in retinal fundus images with the Swapping Assignments between multiple Views (SwAV) algorithm for DR grading. An ablation study was made where SwAV outperformed other CNN and Transformer-based models, independently and in ensemble configurations with an accuracy of 87.00% despite having fewer parameters and layers. The proposed approach outperforms existing state-of-the-art models regarding classification metrics, complexity, and prediction time. The findings offer great potential for medical practitioners, allowing for more accurate diagnosis of DR and earlier treatments to avoid visual loss.
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