Search Results (1,712)

Fyn is widely involved in diverse cellular physiological processes, including cell growth and survival, and has been implicated in the regulation of energy metabolism and the pathogenesis of diabetes mellitus through multiple pathways. Fyn plays a role in increasing fat accumulation and promoting insulin resistance, and it also contributes to the development of diabetic complications such as diabetic kidney disease and diabetic retinopathy. The primary mechanism by which Fyn modulates lipid metabolism is that it inhibits AMP-activated protein kinase (AMPK). Additionally, it affects energy homeostasis through regulating specific signal pathways affecting lipid metabolism including pathways related to CD36, through enhancement of adipocyte differentiation, and through modulating insulin signal transduction. Inflammatory stress is one of the fundamental mechanisms in diabetes mellitus and its complications. Fyn also plays a role in inflammatory stress-related signaling cascades such as the Akt/GSK-3β/Fyn/Nrf2 pathway, exacerbating inflammation in diabetes mellitus. Therefore, Fyn emerges as a promising therapeutic target for regulating glucolipid metabolism and alleviating type 2 diabetes mellitus. This review synthesizes research on the role of Fyn in the regulation of energy metabolism and the development of diabetes mellitus, while exploring its specific regulatory mechanisms. Full article

Background/Objectives: Diabetic retinopathy is a leading cause of vision impairment worldwide, and the development of reliable automated classification systems is crucial for early diagnosis and clinical decision-making. This study presents a comprehensive comparative evaluation of two state-of-the-art deep learning families for the task of classifying diabetic retinopathy using retinal fundus images. Methods: The models were trained and tested in both binary and multi-class settings. The experimental design involved partitioning the data into training (70%), validation (20%), and testing (10%) sets. Model performance was assessed using standard metrics, including precision, sensitivity, specificity, F1-score, and the area under the receiver operating characteristic curve. Results: In binary classification, the ResNeXt101-64x4d model and RegNetY32GT model demonstrated outstanding performance, each achieving high sensitivity and precision. For multi-class classification, ResNeXt101-32x8d exhibited strong performance in early stages, while RegNetY16GT showed better balance across all stages, particularly in advanced diabetic retinopathy cases. To enhance transparency, SHapley Additive exPlanations were employed to visualize the pixel-level contributions for each model’s predictions. Conclusions: The findings suggest that while ResNeXt models are effective in detecting early signs, RegNet models offer more consistent performance in distinguishing between multiple stages of diabetic retinopathy severity. This dual approach combining quantitative evaluation and model interpretability supports the development of more robust and clinically trustworthy decision support systems for diabetic retinopathy screening. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) is increasingly recognized as a significant comorbidity in individuals with type 1 diabetes (T1D), despite its historical association with type 2 diabetes. This review focuses on summarizing current findings regarding the role of insulin resistance in the development of MASLD in T1D, as well as examining the relationship between MASLD and diabetes-related complications. We will also briefly discuss the prevalence, diagnostic challenges, associated complications, and potential mechanisms underlying MASLD in T1D. Although insulin resistance is well established in MASLD among those with type 2 diabetes, its role in T1D requires further clarification. Emerging markers, such as the estimated glucose disposal rate, offer early insight into this relationship. MASLD in T1D is linked to both microvascular and macrovascular complications, including nephropathy, retinopathy, neuropathy, and cardiovascular disease. Variability in prevalence estimates reflects inconsistencies among imaging modalities, emphasizing the need for standardized, non-invasive diagnostic approaches. Recognizing and addressing MASLD and its links to insulin resistance and diabetes complications in T1D is vital for mitigating long-term complications and enhancing clinical outcomes. Full article

Diabetic retinopathy (DR) is a progressive, multifactorial complication of diabetes and one of the major global causes of visual impairment. Its pathogenesis involves chronic hyperglycaemia-induced oxidative stress, inflammation, mitochondrial dysfunction, neurodegeneration, and pathological angiogenesis, as well as emerging systemic contributors such as gut microbiota dysregulation. While current treatments, including anti-vascular endothelial growth factor (anti-VEGF) agents, corticosteroids, and laser photocoagulation, have shown clinical efficacy, they are largely limited to advanced stages of DR, require repeated invasive procedures, and do not adequately address early neurovascular and metabolic abnormalities. Resveratrol (RSV), a naturally occurring polyphenol, has emerged as a promising candidate due to its potent antioxidant, anti-inflammatory, neuroprotective, and anti-angiogenic properties. This review provides a comprehensive analysis of the molecular mechanisms by which RSV exerts protective effects in DR, including modulation of oxidative stress pathways, suppression of inflammatory cytokines, enhancement of mitochondrial function, promotion of autophagy, and inhibition of pathological neovascularisation. Despite its promising pharmacological profile, the clinical application of RSV is limited by poor aqueous solubility, rapid systemic metabolism, and low ocular bioavailability. Various routes of administration, including intravitreal injection, topical instillation, and oral and sublingual delivery, have been investigated to enhance its therapeutic potential. Recent advances in drug delivery systems, including nanoformulations, liposomal carriers, and sustained-release intravitreal implants, offer potential strategies to address these challenges. This review also explores RSV’s role in combination therapies, its potential as a disease-modifying agent in early-stage DR, and the relevance of personalised medicine approaches guided by metabolic and genetic factors. Overall, the review highlights the therapeutic potential and the key translational challenges in positioning RSV as a multi-targeted treatment strategy for DR. Full article

Diabetes mellitus poses a significant global health challenge, primarily due to its chronic metabolic dysregulation, leading to widespread tissue and organ damage. This systemic impact results in a range of complications that markedly reduce patients’ quality of life. Therefore it is critical to understand the mechanisms underlying these complications. DJ-1 (also known as PARK7) is a highly conserved multifunctional protein involved in antioxidative defense, metabolic equilibrium, and cellular survival. Recent studies have highlighted that DJ-1 is critically involved in the pathogenesis and progression of diabetic complications, including macrovascular issues like cardiovascular disease and microvascular conditions such as diabetic nephropathy, retinopathy, and neuropathy, suggesting that it may serve as a promising therapeutic target. Importantly, drugs targeting DJ-1 have shown therapeutic effects. This review provides a comprehensive overview of the current under-standing of DJ-1’s role in diabetes-related complications, emphasizing recent research advances. Full article

The retina offers a unique window into both ocular and systemic health, motivating the development of AI-based tools for disease screening and risk assessment. In this study, we present a comprehensive evaluation of six state-of-the-art deep neural networks, including convolutional neural networks and vision transformer architectures, on the Brazilian Multilabel Ophthalmological Dataset (BRSET), comprising 16,266 fundus images annotated for multiple clinical and demographic labels. We explored seven classification tasks: Diabetes, Diabetic Retinopathy (2-class), Diabetic Retinopathy (3-class), Hypertension, Hypertensive Retinopathy, Drusen, and Sex classification. Models were evaluated using precision, recall, F1-score, accuracy, and AUC. Among all models, the Swin-L generally delivered the best performance across scenarios for Diabetes (AUC = 0.88, weighted F1-score = 0.86), Diabetic Retinopathy (2-class) (AUC = 0.98, weighted F1-score = 0.95), Diabetic Retinopathy (3-class) (macro AUC = 0.98, weighted F1-score = 0.95), Hypertension (AUC = 0.85, weighted F1-score = 0.79), Hypertensive Retinopathy (AUC = 0.81, weighted F1-score = 0.97), Drusen detection (AUC = 0.93, weighted F1-score = 0.90), and Sex classification (AUC = 0.87, weighted F1-score = 0.80). These results reflect excellent to outstanding diagnostic performance. We also employed gradient-based saliency maps to enhance explainability and visualize decision-relevant retinal features. Our findings underscore the potential of deep learning, particularly vision transformer models, to deliver accurate, interpretable, and clinically meaningful screening tools for retinal and systemic disease detection. 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

Objective: This study aimed to determine the possibility of creating a new animal model in which diabetic retinopathy (DR) progresses due to hypertension caused by salt loading. Methods: Male Spontaneously Diabetic Torii (SDT) fatty rats were divided into two groups: one group received 0.3% saline water starting at 8 weeks of age for a duration of 16 weeks (salt SDT fatty group), while the control group was provided with tap water (SDT fatty group). In addition, Sprague-Dawley (SD) rats receiving tap water served as normal controls. Retinal function was assessed by electroretinography (ERG) at 8 and 24 weeks of age. At 24 weeks, following perfusion with fluorescein dextran, the eyes were enucleated, and retinal flat mounts were prepared for vascular evaluation. Retinal thickness and the number of retinal folds were assessed histologically, and ultrastructural changes in the retina were examined using transmission electron microscopy. Results: Saline administration did not lead to significant changes in food consumption or body weight among the groups. In the salt SDT fatty group, blood pressure was significantly elevated, while blood glucose levels showed a slight reduction. ERG analysis showed that the amplitude of oscillatory potential (OP)1 waves was suppressed, and the latencies of OP3, OP4, and OP5 waves were prolonged. Although no significant changes were noted in retinal thickness or the number of retinal folds, thickening of the retinal capillary basement membrane was evident in the salt SDT fatty group. Conclusions: Hypertension induced by 0.3% saline promotes DR progression in SDT fatty rats. This model may help clarify the role of hypertension in DR. 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

This study aimed to evaluate the impact of preoperative anti-VEGF injections on pars plana vitrectomy (PPV) outcomes in patients with proliferative diabetic retinopathy (PDR). Material and methods: We analysed 232 eyes with proliferative diabetic vitreoretinopathy treated with posterior vitrectomy. There were 112 women and 120 men. The patients were divided into two groups of 116 eyes each. In 116 eyes (study group), an anti-VEGF injection was administered 3 to 5 days before vitrectomy. The control eyes were not injected with anti-VEGF due to systemic contraindications to anti-VEGF treatment or lack of patient consent. All participants underwent pars plana vitrectomy with silicone oil injection. The oil was removed within 2–3 months after PPV. Results: At 2 years of observation, after removal of silicone oil, visual acuity (VA) was 0.24 ± 0.27 logMAR in the study and 0.37 ± 0.45 logMAR in the control group (p = 0.003). Intraocular pressure was 16.84 ± 6.25 mmHg in the study group and 17.78 ± 6.22 mmHg in the control group (p = 0.04). The mean duration of surgery was 47.62 ± 9.87 and 50.05 ± 9.41 min in the study and control groups, respectively (p = 0.02). The size of intraoperative haemorrhage was 0.97 ± 0.86 dd in the study group and 1.51 ± 1.22 dd in the control group (p = 0.003). The frequency of surgery-induced retinal breaks was 0.34 ± 0.56 in the study group and 0.56 ± 0.76 in the control group (p = 0.003). The recurrence rate of retinal detachment was 0.05 ± 0.22 in the study group and 0.1 ± 0.31 in the control group (p = 0.15). Conclusions: Preoperative anti-VEGF therapy shortens the duration of surgery, reduces complications, and improves long-term outcomes in terms of visual acuity and maintenance of normal eye function. Full article

Background/Objectives: Diabetic macular edema (DME) is the primary cause of vision loss in people with diabetes, and if untreated, it can result in irreversible macular damage. Both fluorescein angiography (FA), the gold standard, and optical coherence tomography angiography (OCTA) are used for evaluation of this disease. The objective of this study was to compare the diagnostic value of both. Methods: We conducted a comparative analysis of 98 patients aged 18–80 years with significant DME and best-corrected visual acuity ≥0.1 according to the Snellen chart. Participants underwent glycated hemoglobin blood test (HbA1c) and ophthalmological examinations, including OCTA and FA. OCTA 3 × 3 mm scans of superficial (SCP) and deep capillary plexus (DCP) along with FA scans were exported to the Gimp computer program. Size of the foveal avascular zone (FAZ), the number of visible microaneurysms (MAs), and ETDRS report number 11 classification of the images were assessed. Results: FAZ size differed significantly in superficial plexus (0.41 mm²), deep plexus (0.43 mm²) OCTA, and FA (0.38 mm²) (p < 0.001). FAZ size in DCP OCTA closely correlated with that of FA (τ = 0.79, p < 0.001). The total number of MAs visualized in the OCTA was significantly lower than in FA (p < 0.001). ETDRS classification of scans revealed that the level of consistency between the examinations was moderate to very strong. Conclusions: OCTA may be useful in evaluating macular ischemia. It is less sensitive in detecting MAs in DME eyes. FAZ has sharper boundaries and is larger when measured in OCTA. Poor glycemic control results in higher incidence of MAs in macula. 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

Quercetin (QE) is a naturally occurring flavonoid found in many fruits, vegetables, and other plant-based foods. It is recognized for its diverse pharmacological activities. Among its many therapeutic potentials, its antidiabetic properties are of particular interest due to the growing worldwide prevalence of diabetes mellitus. QE improves glycemic control by enhancing insulin sensitivity, stimulating glucose uptake, and preserving pancreatic beta cell function. These effects are mediated by the modulation of key molecular pathways, including AMPK, PI3K/Akt, and Nrf2/ARE, as well as by the suppression of oxidative stress and pro-inflammatory cytokines, such as TNF-α and IL-6. Furthermore, QE mitigates the progression of diabetic complications such as nephropathy, retinopathy, and vascular dysfunction, reducing lipid peroxidation and protecting endothelial function. However, the clinical application of quercetin is limited by its low water solubility, poor bioavailability, and extensive phase II metabolism. Advances in formulation strategies, including the use of nanocarriers, co-crystals, and phospholipid complexes, have shown promise in improving its pharmacokinetics. This review elucidates the mechanistic basis of QE quercetin antidiabetic action and discusses strategies to enhance its therapeutic potential in clinical settings. Full article

Background/Objectives: To investigate the incidence of new-onset diabetic retinopathy (DR) in individuals with pre-existing type 2 diabetes (T2D) up to 3 years post SARS-CoV-2 infection. Methods: This retrospective study consisted of 5151 COVID-19 and 5151 propensity-matched non-COVID-19 patients with T2D in the Montefiore Health System between 1 March 2020 and 17 January 2023. The primary outcome was new-onset DR at least 2 months after the index date up to 17 January 2023. Matching for index date between groups was also used to ensure the same follow-up duration. Hazard ratios (HRs) were computed, adjusted for competing risks. Results: T2D patients with COVID-19 had a higher cumulative incidence of DR than T2D patients. The unadjusted HR for COVID-19 status for developing new DR was 2.44 [1.60, 3.73], p < 0.001. The adjusted HR was 1.70 [1.08, 2.70], p < 0.05, and the adjusted HR for prior insulin use was 3.28 [2.10, 5.12], p < 0.001. Sex, ethnicity, and major comorbidities had no significant association with outcome. Conclusions: T2D patients who contracted COVID-19 exhibited a significantly higher risk of developing DR within three years post infection compared to propensity-matched controls. The increased incidence was primarily driven by greater pre-existing insulin usage and SARS-CoV-2 infection in the COVID-19 positive cohort. 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