Search Results (763)

The retina is highly sensitive to oxygen and blood supply, and hypoxia plays a key role in retinal diseases such as diabetic retinopathy (DR) and age-related macular degeneration (AMD). Müller glial cells, which are essential for retinal homeostasis, respond to injury and hypoxia with reactive gliosis, characterized by the upregulation of the glial fibrillary acidic protein (GFAP) and vimentin, cellular hypertrophy, and extracellular matrix changes, which can impair retinal function and repair. The retinal pigment epithelium (RPE) supports photoreceptors, forms part of the blood–retinal barrier, and protects against oxidative stress; its dysfunction contributes to retinal degenerative diseases such as AMD, retinitis pigmentosa (RP), and Stargardt disease (SD). Extracellular vesicles (EVs) play a crucial role in intercellular communication, protein homeostasis, and immune modulation, and have emerged as promising diagnostic and therapeutic tools. Understanding the role of extracellular vesicles’ (EVs’) signaling machinery of glial cells and the retinal pigment epithelium (RPE) is critical for developing effective treatments for retinal degeneration. In this study, we investigated the bidirectional EV-mediated crosstalk between RPE and Müller cells under hypoxic conditions and its impact on cellular metabolism and retinal cell integrity. Our findings demonstrate that RPE-derived extracellular vesicles (RPE EVs) induce time-dependent metabolic reprogramming in Müller cells. Short-term exposure (24 h) promotes pathways supporting neurotransmitter cycling, calcium and mineral absorption, and glutamate metabolism, while prolonged exposure (72 h) shifts Müller cell metabolism toward enhanced mitochondrial function and ATP production. Conversely, Müller cell-derived EVs under hypoxia influenced RPE metabolic pathways, enhancing fatty acid metabolism, intracellular vesicular trafficking, and the biosynthesis of mitochondrial co-factors such as ubiquinone. Proteomic analysis revealed significant modulation of key regulatory proteins. In Müller cells, hypoxic RPE-EV exposure led to reduced expression of Dyskerin Pseudouridine Synthase 1 (DKc1), Eukaryotic Translation Termination Factor 1 (ETF1), and Protein Ser/Thr phosphatases (PPP2R1B), suggesting alterations in RNA processing, translational fidelity, and signaling. RPE cells exposed to hypoxic Müller cell EVs exhibited elevated Ribosome-binding protein 1 (RRBP1), RAC1/2, and Guanine Nucleotide-Binding Protein G(i) Subunit Alpha-1 (GNAI1), supporting enhanced endoplasmic reticulum (ER) function and cytoskeletal remodeling. Functional assays also revealed the compromised barrier integrity of the outer blood–retinal barrier (oBRB) under hypoxic co-culture conditions. These results underscore the adaptive but time-sensitive nature of retinal cell communication via EVs in response to hypoxia. Targeting this crosstalk may offer novel therapeutic strategies to preserve retinal structure and function in ischemic retinopathies. Full article

Age-related macular degeneration (AMD) is the leading cause of blindness in developed countries. Predicting its progression is crucial for preventing late-stage AMD, as it is an irreversible retinal disease. Both genetic factors and retinal images are instrumental in diagnosing and predicting AMD progression. Previous studies have explored automated diagnosis using single fundus images and genetic variants, but they often fail to utilize the valuable longitudinal data from multiple visits. Longitudinal retinal images offer a dynamic view of disease progression, yet standard Long Short-Term Memory (LSTM) models assume consistent time intervals between training and testing, limiting their effectiveness in real-world settings. To address this limitation, we propose time-varied Long Short-Term Memory (TV-LSTM), which accommodates irregular time intervals in longitudinal data. Our innovative approach enables the integration of both longitudinal fundus images and AMD-associated genetic variants for more precise progression prediction. Our TV-LSTM model achieved an AUC-ROC of 0.9479 and an AUC-PR of 0.8591 for predicting late AMD within two years, using data from four visits with varying time intervals. Full article

Obstructive sleep apnea (OSA) is a common condition associated with intermittent hypoxia, systemic inflammation, and vascular dysfunction; mechanisms implicated in retinal disease pathogenesis. This real-world retrospective cohort study used data from the TriNetX Research Network to assess whether continuous positive airway pressure (CPAP) therapy reduces retinal disease incidence among adults with OSA and BMI between 25.0 and 30.0 kg/m². After 1:1 propensity score matching, 101,754 patients were included in the analysis. Retinal outcomes included diabetic retinopathy (DR), age-related macular degeneration (AMD), retinal vein occlusion (RVO), and central serous chorioretinopathy (CSC). CPAP use was associated with a modest but statistically significant reduction in DR (3.2% vs. 3.4%, RR: 0.922, p = 0.016) and AMD (2.1% vs. 2.3%, RR: 0.906, p = 0.018), while no significant differences were found for RVO or CSC. These findings support prior evidence linking CPAP to improved retinal microvascular health and suggest a protective effect against specific retinal complications. Limitations include a lack of data on CPAP adherence, OSA severity, and imaging confirmation. Still, this study highlights the importance of interdisciplinary care between sleep and eye health, and the need for further prospective studies to validate CPAP’s role in preventing retinal disease progression in OSA patients. Full article

People with C3 Dense Deposit Disease (C3DDD), a rare autoimmune disease, often also have ocular complications. Due to the rarity of this disease, there is little known about ocular complications in populations across the world. This paper aimed to assess literature on retinal complications in people with C3 Dense Deposit Disease. A scoping review was conducted and three databases (Embase, Medline All, and Web of Science) were searched using agreed search terms and Boolean operators. All references were imported into Covidence for screening by two reviewers. Any conflicts were resolved by a third reviewer. Data were extracted into an Excel spreadsheet and analysis was conducted using SPSS Version 29. After full text screening, 38 studies were included in the review. These studies were from 1990–2023 and most (67%) being case reports. All studies were conducted in the United States (55%) or Europe (45%). Most studies reported drusen-like deposits in the retina (75%) and retinal pigment epithelial detachment (18%) and macular atrophy (11%). Choroidal Neovascularisation (CNV) was found in 16% of cases. People with C3 Dense Deposit Disease are at risk of ocular complications, primarily drusen-like deposits. Further population-based research and progression is needed. Full article

Background: Retinal diseases are becoming an important public health issue, with early diagnosis and timely intervention playing a key role in preventing vision loss. Optical coherence tomography (OCT) remains the leading non-invasive imaging technique for identifying retinal conditions. However, distinguishing between diabetic macular edema (DME) and macular edema resulting from retinal vein occlusion (RVO) can be particularly challenging, especially for clinicians without specialized training in retinal disorders, as both conditions manifest through increased retinal thickness. Due to the limited research exploring the application of deep learning methods, particularly for RVO detection using OCT scans, this study proposes a novel diagnostic approach based on stacked convolutional neural networks. This architecture aims to enhance classification accuracy by integrating multiple neural network layers, enabling more robust feature extraction and improved differentiation between retinal pathologies. Methods: The VGG-16, VGG-19, and ResNet50 models were fine-tuned using the Kermany dataset to classify the OCT images and afterwards were trained using a private OCT dataset. Four stacked models were then developed using these models: a model using the VGG-16 and VGG-19 networks, a model using the VGG-16 and ResNet50 networks, a model using the VGG-19 and ResNet50 models, and finally a model using all three networks. The performance metrics of the model includes accuracy, precision, recall, $F_2$-score, and area under of the receiver operating characteristic curve (AUROC). Results: The stacked neural network using all three models achieved the best results, having an accuracy of 90.7%, precision of 99.2%, a recall of 90.7%, and an $F_2$-score of 92.3%. Conclusions: This study presents a novel method for distinguishing retinal disease by using stacked neural networks. This research aims to provide a reliable tool for ophthalmologists to improve diagnosis accuracy and speed. Full article

Herpes simplex virus type 1 (HSV-1) is associated with eye infections. Specifically, the acute consequences of eye infections have been extensively studied. This review gathers information on possible collateral damage caused by HSV-1 in the retina, such as age-related macular degeneration (AMD), a neurodegenerative disease. The synthesis and accumulation of Amyloid-β peptide (Aβ) is a key hallmark in these types of pathologies. AMD is a disease of multifactorial origin, and viral infections play an important role in its development. It is known that once this virus has entered the eye, it can infect adjacent cells, thus having the ability to infect almost any cell type with great tropism. In the retina, retinal pigment epithelial (RPE) cells are primarily involved in AMD. This work reviews publications that show that RPE can produce Aβ, and once they are infected by HSV-1, the release is promoted. Also, all the information available in the literature that explains how these events may be interconnected has been compiled. This information is valuable when planning new treatments for multifactorial neurodegenerative diseases. Full article

Background/Objectives: Genetic testing is important for diagnosing inherited retinal diseases (IRDs), but further evidence is needed on the utility of singleton genetic testing in an Australian cohort. Methods: A consecutive series of individuals with clinically diagnosed IRDs without prior genetic testing underwent commercial panel-based sequencing (Invitae or Blueprint Genetics), clinical assessment, and multimodal imaging. Retinal images were graded using the Human Phenotype Ontology terms. Binary logistic regression was used to evaluate clinical predictors of a positive molecular diagnosis. Results: Among 140 participants (mean age 49 ± 19 years), genetic testing was undertaken, on average, 23 ± 17 years after the initial clinical IRD diagnosis. Of the 60% who received a probable molecular diagnosis, 40% require further phase testing, highlighting the limitations of singleton genetic testing. USH2A, ABCA4, and RPGR were the most common encountered genes; 67% of the probably solved participants had causative genes with targeted experimental treatments in ongoing human clinical trials. Symptom onset before the age of 30 (OR = 3.06 [95% CI: 1.34–7.18]) and a positive IRD family history (OR = 2.87 [95% CI: 1.27–6.78]) were each associated with higher odds of receiving a molecular diagnosis. Diagnostic rates were comparable across retinal imaging phenotypes (atrophy and autofluorescence patterns in widespread IRD, and the extent of dystrophy in macular IRDs). Conclusions: In an Australian IRD population without prior genetic testing, commercial panels yielded higher diagnostic rates in individuals with IRD onset before the age of 30 and those with an IRD family history. Further research is needed to understand the genetic basis of IRDs, especially isolated and late-onset cases, to improve diagnosis and access to emerging therapies. Full article

The retinal pigment epithelium (RPE), a monolayer of pigmented cells, is critical for visual function through its interaction with the neural retina. In healthy eyes, RPE cells exhibit a uniform hexagonal arrangement, but under stress or disease, such as age-related macular degeneration (AMD), dysmorphic traits like cell enlargement and apparent multinucleation emerge. Multinucleation has been hypothesized to result from cellular fusion, a compensatory mechanism to maintain cell-to-cell contact and barrier function, as well as conserve resources in unhealthy tissue. However, traditional two-dimensional (2D) imaging using apical border markers alone may misrepresent multinucleation due to the lack of lateral markers. We present high-resolution confocal images enabling three-dimensional (3D) visualization of apical (ZO-1) and lateral (α-catenin) markers alongside nuclei. In two RPE damage models, we find that seemingly multinucleated cells are often single cells with displaced neighboring nuclei and lateral membranes. This emphasizes the need for 3D analyses to avoid misidentifying multinucleation and underlying fusion mechanisms. Lastly, images from the NaIO₃ oxidative damage model reveal variability in RPE damage, with elongated, dysmorphic cells showing increased ZsGreen reporter protein expression driven by EMT-linked CAG promoter activity, while more regular RPE cells displayed somewhat reduced green signal more typical of epithelial phenotypes. Full article

Visual field (VF) testing remains a cornerstone in assessing retinal function by measuring how well different parts of the retina detect light. It is essential for early detection, monitoring, and management of many retinal diseases. By mapping retinal sensitivity, VF exams can reveal functional loss before structural changes become visible. This review summarizes how VF testing is applied across key conditions: hydroxychloroquine (HCQ) retinopathy, age-related macular degeneration (AMD), diabetic retinopathy (DR) and macular edema (DME), and inherited disorders including inherited dystrophies such as retinitis pigmentosa (RP). Traditional methods like the Goldmann kinetic perimetry and simple tools such as the Amsler grid help identify large or central VF defects. Automated perimetry (e.g., Humphrey Field Analyzer) provides detailed, quantitative data critical for detecting subtle paracentral scotomas in HCQ retinopathy and central vision loss in AMD. Frequency-doubling technology (FDT) reveals early neural deficits in DR before blood vessel changes appear. Microperimetry offers precise, localized sensitivity maps for macular diseases. Despite its value, VF testing faces challenges including patient fatigue, variability in responses, and interpretation of unreliable results. Recent advances in artificial intelligence, virtual reality perimetry, and home-based perimetry systems are improving test accuracy, accessibility, and patient engagement. Integrating VF exams with these emerging technologies promises more personalized care, earlier intervention, and better long-term outcomes for patients with retinal disease. Full article

Age-related ocular conditions like macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma are leading causes of irreversible vision loss globally. Optical coherence tomography (OCT) provides essential non-invasive visualization of retinal structures for early diagnosis, but manual analysis of these images is labor-intensive and prone to variability. Deep learning (DL) techniques have emerged as powerful tools for automating the segmentation of the retinal layer in OCT scans, potentially improving diagnostic efficiency and consistency. This review systematically evaluates the state of the art in DL-based retinal layer segmentation using the PRISMA methodology. We analyze various architectures (including CNNs, U-Net variants, GANs, and transformers), examine the characteristics and availability of datasets, discuss common preprocessing and data augmentation strategies, identify frequently targeted retinal layers, and compare performance evaluation metrics across studies. Our synthesis highlights significant progress, particularly with U-Net-based models, which often achieve Dice scores exceeding 0.90 for well-defined layers, such as the retinal pigment epithelium (RPE). However, it also identifies ongoing challenges, including dataset heterogeneity, inconsistent evaluation protocols, difficulties in segmenting specific layers (e.g., OPL, RNFL), and the need for improved clinical integration. This review provides a comprehensive overview of current strengths, limitations, and future directions to guide research towards more robust and clinically applicable automated segmentation tools for enhanced ocular disease diagnosis. Full article

Stargardt’s disease (STGD1) is an autosomal recessive juvenile macular degeneration caused by mutations in the ABCA4 gene, impairing clearance of toxic retinoid byproducts in the retinal pigment epithelium (RPE). This leads to lipofuscin accumulation, oxidative stress, photoreceptor degeneration, and central vision loss. Over 1200 pathogenic/likely pathogenic ABCA4 variants highlight the genetic heterogeneity of STGD1, which manifests as progressive central vision loss, with phenotype influenced by deep intronic variants, modifier genes, and environmental factors like light exposure. ABCA4 variants also show variable penetrance and geographical prevalence. With no approved treatment, investigational therapies target different aspects of disease pathology. Small-molecule therapies target vitamin A dimerization (e.g., ALK-001), inhibit lipofuscin accumulation (e.g., soraprazan), or modulate the visual cycle (e.g., emixustat hydrochloride). Gene therapy trials explore ABCA4 supplementation including strategies like RNA exon editing (ACDN-01) and bioengineered ambient light-activated OPSIN. RORA gene therapy (Phase 2/3) addresses oxidative stress, inflammation, lipid metabolism, and complement system dysregulation. Trials like DRAGON (Phase 3, tinlarebant), STARLIGHT (phase 2, bioengineered OPSIN) show promise, but optimizing efficacy remains challenging. With the key problem of establishing genotype–phenotype correlations, the future of STGD1 therapy may rely on approaches targeting oxidative stress, lipid metabolism, inflammation, complement regulation, and genetic repair. Full article

Gene therapy has emerged as a promising treatment for several eye diseases since it may restore vision and stop blindness. Many eye diseases, including retinitis pigmentosa and macular degeneration, have historically been rather difficult to treat and usually cause permanent vision loss. However, thanks to advances in gene therapy, many disorders can now be effectively targeted and genetically changed, providing a safer, more direct, maybe even curative approach. By introducing, altering, or repairing specific genes inside the eye, gene therapy seeks to fix the defective genes causing these disorders, thereby improving general eye health and visual ability. Voretigene neparvovec is one FDA- and EMA-approved treatment for RPE65 mutations. Retinitis pigmentosa, age-related macular degeneration, X-linked retinoschisis, choroideremia, and Stargardt disease are among the several eye disorders still under clinical trials, and experimental treatment is in progress. As research on gene therapy develops, it opens the path for groundbreaking treatments that could fundamentally change the ophthalmic care scene. Full article

Background: Optical Coherence Tomography has become a common imaging technique that enables a non-invasive and detailed visualization of the retina and allows for the identification of various diseases. Through the advancement of technology, the volume and complexity of OCT data have rendered manual analysis infeasible, creating the need for automated means of detection. Methods: This study investigates the ability of state-of-the-art object detection models, including the latest YOLO versions (from v8 to v12), YOLO-World, YOLOE, and RT-DETR, to accurately detect pathological biomarkers in two retinal OCT datasets. The AROI dataset focuses on fluid detection in Age-related Macular Degeneration, while the OCT5k dataset contains a wide range of retinal pathologies. Results: The experiments performed show that YOLOv12 offers the best balance between detection accuracy and computational efficiency, while YOLOE manages to consistently outperform all other models across both datasets and most classes, particularly in detecting pathologies that cover a smaller area. Conclusions: This work provides a comprehensive benchmark of the capabilities of state-of-the-art object detection for medical applications, specifically for identifying retinal pathologies from OCT scans, offering insights and a starting point for the development of future automated solutions for analysis in a clinical setting. Full article

Ocular diseases can significantly affect vision and overall quality of life, with diagnosis often being time-consuming and dependent on expert interpretation. While previous computer-aided diagnostic systems have focused primarily on medical imaging, this paper proposes VisionTrack, a multi-modal AI system for predicting multiple retinal diseases, including Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), drusen, Central Serous Retinopathy (CSR), and Macular Hole (MH), as well as normal cases. The proposed framework integrates a Convolutional Neural Network (CNN) for image-based feature extraction, a Graph Neural Network (GNN) to model complex relationships among clinical risk factors, and a Large Language Model (LLM) to process patient medical reports. By leveraging diverse data sources, VisionTrack improves prediction accuracy and offers a more comprehensive assessment of retinal health. Experimental results demonstrate the effectiveness of this hybrid system, highlighting its potential for early detection, risk assessment, and personalized ophthalmic care. Experiments were conducted using two publicly available datasets, RetinalOCT and RFMID, which provide diverse retinal imaging modalities: OCT images and fundus images, respectively. The proposed multi-modal AI system demonstrated strong performance in multi-label disease prediction. On the RetinalOCT dataset, the model achieved an accuracy of 0.980, F1-score of 0.979, recall of 0.978, and precision of 0.979. Similarly, on the RFMID dataset, it reached an accuracy of 0.989, F1-score of 0.881, recall of 0.866, and precision of 0.897. These results confirm the robustness, reliability, and generalization capability of the proposed approach across different imaging modalities. Full article

Fundus autofluorescence (FAF) is a non-invasive retinal imaging technique that helps visualize naturally occurring fluorophores, such as lipofuscin, and provides valuable insight into retinal diseases—particularly inherited retinal diseases (IRDs). FAF is especially useful in detecting subclinical or early-stage IRDs and in monitoring disease progression over time. In Stargardt disease, areas of decreased autofluorescence correlate with disease progression and have been proposed as a biomarker for future clinical trials. FAF can also help differentiate Stargardt disease from other macular dystrophies. In retinitis pigmentosa, hyperautofluorescent rings are a common feature on FAF and serve as an important marker for disease monitoring, especially as changes align with those seen on other imaging modalities. FAF is valuable in tracking progression of choroideremia and may help identify disease carrier status. FAF has also improved the characterization of mitochondrial retinopathies such as maternally inherited diabetes and deafness. As a rapid and widely accessible imaging modality, FAF plays a critical role in both diagnosis and longitudinal care of patients with IRDs. Full article