Features Of Diabetic Retinopathy Research Articles

Effects of Nox4 upregulation on PECAM-1 expression in a mouse model of diabetic retinopathy.

Diabetic Retinopathy (DR) is the leading cause of vision loss in working-age adults. The hallmark features of DR include vascular leakage, capillary loss, retinal ischemia, and aberrant neovascularization. Although the pathophysiology is not fully understood, accumulating evidence supports elevated reactive oxygen species associated with increased activity of NADPH oxidase 4 (Nox4) as major drivers of disease progression. Previously, we have shown that Nox4 upregulation in retinal endothelial cells by diabetes leads to increased vascular leakage by an unknown mechanism. Platelet endothelial cell adhesion molecule 1 (PECAM-1) is a cell surface molecule that is highly expressed in endothelial cells and regulates endothelial barrier function. In the present study, using endothelial cell-specific human Nox4 transgenic (TG) mice and endothelial cell-specific Nox4 conditional knockout (cKO) mice, we investigated the impact of Nox4 upregulation on PECAM-1 expression in mouse retinas and brain microvascular endothelial cells (BMECs). Additionally, cultured human retinal endothelial cells (HRECs) transduced with adenovirus overexpressing human Nox4 were used in the study. We found that overexpression of Nox4 increases PECAM-1 mRNA but has no effect on its protein expression in the mouse retina, BMECs, or HRECs. Furthermore, PECAM-1 mRNA and protein expression was unchanged in BMECs isolated from cKO mice compared to wild type (WT) mice with or without 2 months of diabetes. Together, these findings do not support a significant role of Nox4 in the regulation of PECAM-1 expression in the diabetic retina and endothelial cells. Further studies are warranted to elucidate the mechanism of Nox4-induced vascular leakage by investigating other intercellular junctional proteins in endothelial cells and their implications in the pathophysiology of diabetic retinopathy.

The Genetic Variants of Long Noncoding RNA MEG3 and Its Association to the Clinical Features of Diabetic Retinopathy

Purpose This study aimed to investigate the potential correlation between the single-nucleotide polymorphism (SNP) of maternally expressed gene 3 (MEG3) and the clinical manifestations of diabetic retinopathy (DR). Methods Five loci of MEG3 SNPs including rs4081134 (G/A), rs10144253 (T/C), rs7158663 (G/A), rs3087918 (T/G) and rs11160608 (A/C) were genotyped by TaqMan allelic discrimination in 457 non-DR patients and 280 DR individuals. Results The distribution frequency of MEG3 SNP rs7158663 GA (AOR: 0.683, 95% CI: 0.478-0.975, p = 0.036) and MEG3 SNP rs7158663 GA + AA (AOR: 0.686, 95% CI: 0.487-0.968, p = 0.032) were significantly lower in the DR group. And the MEG3 SNP rs7158663 GA + AA (AOR: 0.610, 95% CI: 0.377-0.985, p = 0.043) demonstrated a significantly lower distribution frequency in the male DR group. Besides, the DR patients with MEG3 SNP rs7158663 GA + AA genotype showed a significantly lower HbA1c level than the DR patients with MEG3 SNP rs7158663 GG genotype (7.29 ± 1.23 versus 7.74 ± 1.49, p = 0.013). Moreover, in the analysis using data from gene expression data series database, a higher MEG3 level was significantly correlated to a lower miR-182 level in the database (p = 0.0114). Conclusions In this study, the distribution frequency of MEG3 SNP rs7158663 GA + AA genotype was lower in DR, while the DR would develop under lower HbA1c level in DM patients with this MEG3 SNP variant.

Arithmetic Optimization for Coinciding Diabetic Retinopathy and Diabetic Macular Edema Grading based on Self-Attention Convolutional Neural Network

This paper proposes a Self-Attention Convolutional Neural Network (SACNN) optimized with Arithmetic Optimization Algorithm (AOA) for coinciding Diabetic Retinopathy (DR) and Diabetic Macular Edema Grading (DMEG) (SACNN-AOA-DR-DMEG). Initially, the input image is collected from 2 openly available benchmark datasets, namely Messidor and ISBI 2018 IDRiD. Then the input image is pre-processing using Altered Phase Preserving Dynamic Range Compression (APPDRC) for reducing noise from the imageries. SACNN receives the pre-processed imageries. The SACNN has three modules: (i) plane attention module, (ii) depth attention module, (iii) Attention Fusion Module. DR and DME features are extracted by plane attention module and depth attention module of SACNN. Attention Fusion Module receives extracted characteristics for categorizing and grading DR and DME disorders. SACNN does not adopt any optimization techniques to guarantee accurate DR and DME grading disorders. That’s why, Arithmetic Optimization Algorithm (AOA) is deemed to optimize the SACNN weight parameters. The proposed technique is implemented in Python. The proposed SACNN-AOA-DR-DMEG method provides 11.18%, 18.99% and 23.76% higher accuracy for diabetic retinopathy grading; 11.52%, 29.62% and 20.38% higher accuracy for DMEG; 33.39%, 22%, 39.26% lower computation time on Messidor data compared with the existing methods, such as AMGNN-DR-DMEG, LCNN-DR-DMEG, and FFN-DR-DMEG respectively.

Modeling early pathophysiological phenotypes of diabetic retinopathy in a human inner blood-retinal barrier-on-a-chip

Diabetic retinopathy (DR) is a microvascular disorder characterized by inner blood-retinal barrier (iBRB) breakdown and irreversible vision loss. While the symptoms of DR are known, disease mechanisms including basement membrane thickening, pericyte dropout and capillary damage remain poorly understood and interventions to repair diseased iBRB microvascular networks have not been developed. In addition, current approaches using animal models and in vitro systems lack translatability and predictivity to finding new target pathways. Here, we develop a diabetic iBRB-on-a-chip that produces pathophysiological phenotypes and disease pathways in vitro that are representative of clinical diagnoses. We show that diabetic stimulation of the iBRB-on-a-chip mirrors DR features, including pericyte loss, vascular regression, ghost vessels, and production of pro-inflammatory factors. We also report transcriptomic data from diabetic iBRB microvascular networks that may reveal drug targets, and examine pericyte-endothelial cell stabilizing strategies. In summary, our model recapitulates key features of disease, and may inform future therapies for DR.

Programmatically Localizing Diabetic Retinopathy Features in 45-Degree Retinal Photographs Using Anatomical Colocation

Background: The aim in this study was to investigate the localization of diabetic retinopathy features at the posterior pole. Methods: This study extracted diabetic retinopathy feature locations from 757 macula-centered 45-degree fundus photographs in the publicly available DDR dataset. Arteriole and venule locations were also extracted from the RITE (n = 35) and IOSTAR (n = 29) datasets. Images were normalized to collocate optic disc and macula positions, and feature positions were collated to generate a frequency distribution matrix. Sørensen–Dice coefficients were calculated to compare the location of different features. Results: Arterioles occurred in two main, distinct arcuate patterns. Venules showed a more diffuse distribution. Microaneurysms were diffusely located around the posterior pole. Hemorrhages and exudates occurred more frequently at the temporal aspect of the macula. Cotton wool spots occurred in a region approximating the radial peripapillary capillaries. Intraretinal microvascular abnormalities and neovascularization were seen throughout the posterior pole, with neovascularization at the disc (n = 65) being more common than neovascularization elsewhere (n = 46). Venous beading occurred primarily between the first and third bifurcations of the venules. Diabetic retinopathy overall was more frequent in the temporal aspect of the macula. The location of cotton wool spots and exudates showed moderate similarity (0.52) when all data were considered, reducing to low similarity (0.18) when areas of low frequency were removed. Conclusions: Diabetic retinopathy occurs throughout the posterior pole but is more frequent in the temporal aspect of the macula. Understanding the location of diabetic retinopathy features may help inform visual search strategies for diabetic retinopathy screening.

Segmentation and Classification of Diabetic Retinopathy using Ensemble Deep Neural Network

Diabetic Retinopathy (DR) is an eye ailment in diabetics, often causing vision loss. Timely retinal screenings can help prevent blindness. While DR cannot be cured, early detection can curb vision decline. Ophthalmologists diagnose DR using Optical Coherence Tomography (OCT) or Fundus Images. Evaluating via fundus images can be complex and potentially imprecise. The study offers a computer-supported diagnosis to aid ophthalmologists. It uses Multi-level Otsu thresholding to segment Fundus Images, with segmented outputs processed further. Advanced feature extraction techniques like the Gray-Level Co-occurrence Matrix (GLCM) and dynamic Flemingo optimization enhance DR feature identification. Additionally, a novel cascaded voting ensemble deep neural network model is introduced, merging multiple algorithmic insights to improve classification. The paper concludes by aligning classifications with a standard grading system, giving clinicians a clear DR severity gauge for better treatment planning.

Artificial intelligence and hemodynamic studies in optical coherence tomography angiography for diabetic retinopathy evaluation: A review.

Diabetic retinopathy (DR) is a rapidly emerging retinal abnormality worldwide, which can cause significant vision loss by disrupting the vascular structure in the retina. Recently, optical coherence tomography angiography (OCTA) has emerged as an effective imaging tool for diagnosing and monitoring DR. OCTA produces high-quality 3-dimensional images and provides deeper visualization of retinal vessel capillaries and plexuses. The clinical relevance of OCTA in detecting, classifying, and planning therapeutic procedures for DR patients has been highlighted in various studies. Quantitative indicators obtained from OCTA, such as blood vessel segmentation of the retina, foveal avascular zone (FAZ) extraction, retinal blood vessel density, blood velocity, flow rate, capillary vessel pressure, and retinal oxygen extraction, have been identified as crucial hemodynamic features for screening DR using computer-aided systems in artificial intelligence (AI). AI has the potential to assist physicians and ophthalmologists in developing new treatment options. In this review, we explore how OCTA has impacted the future of DR screening and early diagnosis. It also focuses on how analysis methods have evolved over time in clinical trials. The future of OCTA imaging and its continued use in AI-assisted analysis is promising and will undoubtedly enhance the clinical management of DR.

Longitudinal Quantitative Ultrawidefield Angiographic Features in Diabetic Retinopathy Treated with Aflibercept from the Intravitreal Aflibercept as Indicated by Real-Time Objective Imaging to Achieve Diabetic Retinopathy Improvement Trial

To report longitudinal trends of quantitative ultrawidefield fluorescein angiography (qUWFA) biomarkers in the Intravitreal Aflibercept as Indicated by Real-Time Objective Imaging to Achieve Diabetic Retinopathy Improvement (PRIME) diabetic retinopathy (DR) clinical trial. Post hoc analysis of the PRIME prospective randomized DR clinical trial comparing intravitreal aflibercept treatment based on the DR severity score (DRSS) or quantitative leakage index for DR improvement (ClinicalTrials.gov identifier: NCT03531294). Patients were enrolled with a DRSS level of 47A to 71A and best-corrected visual acuity of 20/800 or better. Key exclusion criteria were previous intravitreal injection, panretinal photocoagulation, vitrectomy, central-involving macular edema, or vitreous hemorrhage. A previously validated, machine learning-based qUWFA analysis platform was used for panretinal leakage index assessment and differentiation of generalized and perivascular leakage phenotypes. Additionally, microaneurysm count and ischemic index were quantified in panretinal and macular regions. The trends in these biomarkers and therapeutic response were studied over 1 year. Longitudinal trends of qUWFA biomarkers. The impact of these qUWFA metrics on treatment response was assessed by studying their associations with time to 2-step DRSS improvement and number of treatment-free days. Forty eyes from 40 subjects with DR were enrolled. Lower baseline generalized leakage was noted in eyes that attained the 2-step DRSS improvement in < 16 weeks (1.9% vs. 2.8%; P= 0.026). Baseline macular perivascular-generalized leakage ratio had a significant correlation with the number of treatment-free days (r= 0.4; P= 0.012). At the end of 1 year, therapy significantly reduced the mean panretinal (3.9% vs. 5.8%; P= 0.002) and macular (6.2% vs. 12.2%; P= 0.008) generalized leakage indices compared with baseline, as well as the mean panretinal perivascular leakage index (1.5% vs. 2.3%; P= 0.002). The mean panretinal ischemic index demonstrated a small but likely clinically insignificant decrease from 12.5% at baseline to 11.6% at year 1 (P= 0.016). Down-trending leakage indices and microaneurysm counts were demonstrated over 1 year of anti-VEGF therapy. At baseline, DR eyes with lower generalized leakage responded to therapy more rapidly. Eyes with greater perivascular leakage relative to generalized leakage showed a longer-lasting anti-VEGF treatment response. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

TNFAIP8 overexpression aggravates retinal pathophysiological features of diabetic retinopathy

Our previous research shown that tumor necrosis factor-alpha-induced protein 8 (TNFAIP8) is elevated in the plasma extracellular vesicles and vitreous humor in diabetic retinopathy (DR). TNFAIP8 also significantly increases the viability of human retinal microvascular endothelial cells (HRMECs) and promotes cell migration and tube formation in vitro. To comprehensively explore its role in DR, we investigated the effect of TNFAIP8 on DR development using an animal model in this study. A TNFAIP8-overexpressing adeno-associated virus (AAV) vector and streptozotocin-induced mouse model was used. The AAV-TNFAIP8 vector was injected into the mice intravitreally, and the effect was evaluated. The evaluation included analysis of retinal structure and function using electroretinography, optical coherence tomography, and histological assessment. The influence of TNFAIP8 on the avascular area, retinal leukostasis, and the expression levels of inflammatory factors was also determined. TNFAIP8 significantly decreased a/b-wave amplitude and retinal thickness in diabetic mice. Histological assessment showed that TNFAIP8 aggravated pathological abnormalities with distorted organization of the retina. TNFAIP8 also significantly increased the avascular area, leukostasis, and the expression of inflammatory factors, such as TNFα, IL1β, ICAM1, and GFAP, in the retina. The results of this study support the role of TNFAIP8 in DR pathogenesis. A mechanistic understanding of TNFAIP8 may offer novel therapeutic strategies.

In Vitro Evaluation of Apoptosis, Inflammation, Angiogenesis, and Neuroprotection Gene Expression in Retinal Pigmented Epithelial Cell Treated with Interferon α-2b.

Angiogenesis, retinal neuropathy, and inflammation are the main molecular features of diabetic retinopathy (DR) and should be taken into consideration for potential treatment approaches. Retinal pigmented epithelial (RPE) cells play a major role in DR progression. This study evaluated the in vitro effect of interferon (IFN) α-2b on the expression of genes involved in apoptosis, inflammation, neuroprotection, and angiogenesis in RPE cells. RPE cells were cocultured with IFN α-2b at 2 doses (500 and 1,000 IU) and treatment periods (24 and 48 h). The quantitative relative expression of genes (BCL-2, BAX, BDNF, VEGF, and IL-1b) was evaluated in the treated versus control cells through real-time polymerase chain reaction (PCR). The result of this study demonstrated that IFN treatment at 1,000 IU (48 h) led to significant upregulation of BCL-2, BAX, BDNF, and IL-1b; however, the BCL-2/BAX ratio was not statistically altered from 1:1, in any of the treatment patterns. We also showed that VEGF expression was downregulated in RPE cells treated with 500 IU for 24 h. It can be concluded that IFN α-2b was safe (BCL-2/BAX ∼1:1) and enhanced neuroprotection at 1,000 IU (48 h); however-at the same time-IFN α-2b induced inflammation in RPE cells. Moreover, the antiangiogenic effect of IFN α-2b was solely observed in RPE cells treated with 500 IU (24 h). It seems that IFN α-2b in lower doses and short duration exerts antiangiogenic effects and in higher doses and longer duration has neuroprotective and inflammatory effects. Hence, appropriate concentration and duration of treatment, according to the type and stage of the disease, should be considered to achieve success in IFN therapy.

Diabetic retinopathy feature extraction images based on confusion neural network

&lt;p class="15"&gt;The diagnosis of diabetic retinopathy depends on the evaluation of retinal fundus pictures. The current methods have been successful in extracting features from fundus images, but due to the complex blood vessel distribution in these images and the presence of a great deal of noise, simple methods based on threshold segmentation and clustering are vulnerable to feature loss during the extraction process. For example, the small blood vessels in the fundus are lost, and the branches of blood vessels are blurred. In addition, the noise in medical images is mainly distributed in the high-frequency area of the image. The proposed method to segment the retinal fundus vessels in the DRIVE and STARE datasets, the average accuracy of this method is 95.45% and 94.81%, respectively, and the sensitivity and specificity are 73.35%, 75.39% and 97.34%, 95.75%. In addition, compared with related methods, the proposed method has higher segmentation accuracy, and after segmentation, the fundus blood vessels have higher integrity, clear structure, and less loss of small blood vessels.&lt;/p&gt;

495-P: Comparing Detection of Diabetic Retinopathy by Three Artificial Intelligence Platforms in Subjects Not Known to Have Diabetes

Background: The glycemic threshold for the diagnosis of type 2 diabetes (T2D) was determined based upon the prevalence of moderate retinopathy; however, diabetes related complications may occur below that threshold. This study utilized three companies to analyse retinal images using their commercial software and returned data on absence or presence of features of any diabetic retinopathy (DR) on a per-eye basis. Methods: Ophthalmic retinal images from 11,449 subjects (right and left eyes for each, totalling 22,898 images) not known to have diabetes from the Qatar Biobank were subjected to assessment for detection of features of DR using three Conformitè Europëenne (CE)-marked independent commercial Artificial Intelligence (AI) software systems. Results: From 11,449 patients: •DR was recorded as absent in both eyes in 7,094 (62.0%) patients by all 3 methods (Test Negative at patient level).•DR was detected by 2 or 3 of the AI systems in at least one eye in a total of 2,408 (21.0%) patients (Test Positive at patient level). Of these, in 1,532 (13.4%) cases DR was detected in just one eye of any given patient and in 876 (7.6%) cases DR was detected in both eyes of a patient. •Overall, 1,947 (17.0%) patients had at least one eye reported as unassessable by 2 or 3 of the AI systems; hence, No Overall Result for DR features could be obtained for these patients. Results from the three AI companies differed markedly: 17.7%, 32.9% and 46.6% ranked Test-Positive results at patient level. Only 304 right eyes and 280 left eyes were positive for all 3 companies. Conclusion: This is the first direct comparison of three independent AI companies when simultaneously assessing retinal images from more than 10,000 individuals. It showed markedly differing prevalences of diabetic retinopathy between existing AI systems, indicating inconsistencies if wishing to apply these tools to detect retinopathy in subjects not already known to have diabetes, and may differ in patients with known diabetes. Disclosure A.E.Butler: None. E.S.Kilpatrick: None. S.C.Hunt: None. S.Atkin: None.

The Cuckoo Optimization Algorithm Enhanced Visualization of Morphological Features of Diabetic Retinopathy

This research compares strategies for identifying diabetic retinopathy (DR) using fundus image and discusses the efficiency of various image pre-processing techniques to enhance the quality of fundus images. Fundus images in medical image processing often suffer from non-uniform lighting, low contrast, and noise issues, which necessitate image pre-processing to enhance their quality. The study evaluates the effectiveness of several optimization techniques in selecting the best technique for identifying DR. One of the image pre-processing techniques compared in the study involves comparing negative images, dark contrast stretch, light contrast stretch, and partial contrast stretch, which are then evaluated using standard performance metrics such as NIQE, PNSR, MSE, and entropy. The results are further optimized using the Cuckoo Search Algorithm. The proposed technique produces better image quality improvements in several performance metrics, such as MSE, NIQE, PSNR, and entropy. Bright Contrast Stretch outperforms other techniques in NIQE Mean 5.2850, Entropy 5.0193, NIQE Standard deviation 0.2261, and Entropy 0.2612.

Nanoscale coordination polymer Fe-DMY downregulating Poldip2-Nox4-H2O2 pathway and alleviating diabetic retinopathy

Diabetic retinopathy (DR) is a prevalent microvascular complication of diabetes and the leading cause of blindness and severe visual impairment in adults. The high levels of glucose trigger multiple intracellular oxidative stress pathways, such as POLDIP2, resulting in excessive reactive oxygen species (ROS) production and increased expression of vascular cell adhesion molecule-1 (VCAM-1), hypoxia-inducible factor 1α (HIF-1α), and vascular endothelial growth factor (VEGF), causing microvascular dysfunction. Dihydromyricetin (DMY) is a natural flavonoid small molecule antioxidant. However, it exhibits poor solubility in physiological environments, has a short half-life in vivo, and has low oral bioavailability. In this study, we present, for the first time, the synthesis of ultra-small Fe-DMY nano-coordinated polymer particles (Fe-DMY NCPs), formed by combining DMY with low-toxicity iron ions. In vitro and in vivo experiments confirm that Fe-DMY NCPs alleviate oxidative stress-induced damage to vascular endothelial cells by high glucose, scavenge excess ROS, and improve pathological features of DR, such as retinal vascular leakage and neovascularization. Mechanistic validation indicates that Fe-DMY NCPs can inhibit the activation of the Poldip2-Nox4-H2O2 signaling pathway and downregulate vital vascular function indicators such as VCAM-1, HIF-1α, and VEGF. These findings suggest that Fe-DMY NCPs could serve as a safe and effective antioxidant and microangio-protective agent, with the potential as a novel multimeric drug for DR therapy.

Studies of the retinal microcirculation using human donor eyes and high-resolution clinical imaging: Insights gained to guide future research in diabetic retinopathy.

The microcirculation plays a key role in delivering oxygen to and removing metabolic wastes from energy-intensive retinal neurons. Microvascular changes are a hallmark feature of diabetic retinopathy (DR), a major cause of irreversible vision loss globally. Early investigators have performed landmark studies characterising the pathologic manifestations of DR. Previous works have collectively informed us of the clinical stages of DR and the retinal manifestations associated with devastating vision loss. Since these reports, major advancements in histologic techniques coupled with three-dimensional image processing has facilitated a deeper understanding of the structural characteristics in the healthy and diseased retinal circulation. Furthermore, breakthroughs in high-resolution retinal imaging have facilitated clinical translation of histologic knowledge to detect and monitor progression of microcirculatory disturbances with greater precision. Isolated perfusion techniques have been applied to human donor eyes to further our understanding of the cytoarchitectural characteristics of the normal human retinal circulation as well as provide novel insights into the pathophysiology of DR. Histology has been used to validate emerging in vivo retinal imaging techniques such as optical coherence tomography angiography. This report provides an overview of our research on the human retinal microcirculation in the context of the current ophthalmic literature. We commence by proposing a standardised histologic lexicon for characterising the human retinal microcirculation and subsequently discuss the pathophysiologic mechanisms underlying key manifestations of DR, with a focus on microaneurysms and retinal ischaemia. The advantages and limitations of current retinal imaging modalities as determined using histologic validation are also presented. We conclude with an overview of the implications of our research and provide a perspective on future directions in DR research.

Intraretinal Microvascular Abnormalities and Venous Beading Have Different Genetic Profiles in Caucasian Patients with Non-Proliferative Diabetic Retinopathy

Diabetic Retinopathy (DR) is a leading cause of preventable visual impairment in the working age population. Despite the increasing prevalence of DR, there remain gaps in our understanding of its pathophysiology. This is a prospective case-control study comparing the genetic profiles of patients with no DR vs. non-proliferative DR (NPDR) focusing on intraretinal microvascular abnormalities (IRMA) and venous beading (VB) in Caucasians. A total of 596 participants were recruited to the study; 199 with moderate/severe NPDR and 397 with diabetes for at least 5 years without DR. Sixty-four patients were excluded due to technical issues. In total, 532 were analysed; 181 and 351 were in the NPDR group and no DR group, respectively. Those with severe IRMA and VB had distinctly different genetic profiles from each other and from the no DR group, which further supports the theory that these two features of DR might have different etiologies. This also suggests that IRMA and VB are independent risk factors for the development of PDR and may have different pathophysiologies. If these findings are confirmed in larger studies, this could pave the way for personalised treatment options for those more at risk of developing different features of NPDR.

Clinical Features Distinguishing Diabetic Retinopathy Severity Using Artificial Intelligence

Background and Hypothesis:1 in 29 American diabetics suffer from diabetic retinopathy (DR), the weakening of blood vessels in the retina. DR goes undetected in nearly 50% of diabetics, allowing DR to steal the vision of many Americans. We hypothesize that increasing the rate and ease of diagnosing DR by introducing artificial intelligence-based methods in primary medical clinics will increase the long-term preservation of ocular health in diabetic patients.&#x0D; Project Methods:This retrospective cohort study was conducted under approval from the Institutional Review Board of Indiana University School of Medicine. Images were deidentified and no consent was taken due to the nature of this retrospective study. We categorized 676 patient files based upon HbA1c, severity of non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR). Retinal images were annotated to identify common features of DR: microaneurysms, hemorrhages, cotton wool spots, exudates, and neovascularization. The VGG Image Annotator application used for annotations allowed us to save structure coordinates into a separate database for future training of the artificial intelligence system.&#x0D; Results:228 (33.7%) of patients were diagnosed with diabetes, and 143 (62.7%) of those were diagnosed with DR. Two-sample t tests found significant differences between the HbA1c values of all diabetics compared to diabetics without retinopathy (p&lt;0.007) and between all severities of DR versus diabetics without retinopathy (p&lt;0.002). 283 eyes were diagnosed with a form of DR in this study: 37 mild NPDR, 42 moderate NPDR, 56 severe NPDR, and 148 PDR eyes.&#x0D; Potential Impact:With the dataset of coordinates and HbA1c values from this experiment, we aim to train an artificial intelligence system to diagnose DR through retinal imaging. The goal of this system is to be conveniently used in primary medical clinics to increase the detection rate of DR to preserve the ocular health of millions of future Americans.

Finerenone, a Non-Steroidal Mineralocorticoid Receptor Antagonist, Reduces Vascular Injury and Increases Regulatory T-Cells: Studies in Rodents with Diabetic and Neovascular Retinopathy.

Vision loss in diabetic retinopathy features damage to the blood-retinal barrier and neovascularization, with hypertension and the renin-angiotensin system (RAS) having causal roles. We evaluated if finerenone, a non-steroidal mineralocorticoid receptor (MR) antagonist, reduced vascular pathology and inflammation in diabetic and neovascular retinopathy. Diabetic and hypertensive transgenic (mRen-2)27 rats overexpressing the RAS received the MR antagonist finerenone (10 mg/kg/day, oral gavage) or the angiotensin-converting enzyme inhibitor perindopril (10 mg/kg/day, drinking water) for 12 weeks. As retinal neovascularization does not develop in diabetic rodents, finerenone (5 mg/kg/day, i.p.) was evaluated in murine oxygen-induced retinopathy (OIR). Retinal vasculopathy was assessed by measuring gliosis, vascular leakage, neovascularization, and VEGF. Inflammation was investigated by quantitating retinal microglia/macrophages, pro-inflammatory mediators, and anti-inflammatory regulatory T-cells (Tregs). In diabetes, both treatments reduced systolic blood pressure, gliosis, vascular leakage, and microglial/macrophage density, but only finerenone lowered VEGF, ICAM-1, and IL-1ß. In OIR, finerenone reduced neovascularization, vascular leakage, and microglial density, and increased Tregs in the blood, spleen, and retina. Our findings, in the context of the FIDELIO-DKD and FIGARO-DKD trials reporting the benefits of finerenone on renal and cardiovascular outcomes in diabetic kidney disease, indicate the potential of finerenone as an effective oral treatment for diabetic retinopathy.

UK Biobank retinal imaging grading: methodology, baseline characteristics and findings for common ocular diseases

Background/objectivesThis study aims to describe the grading methods and baseline characteristics for UK Biobank (UKBB) participants who underwent retinal imaging in 2009–2010, and to characterise individuals with retinal features suggestive of age-related macular degeneration (AMD), glaucoma and retinopathy.MethodsNon-mydriatic colour fundus photographs and macular optical coherence tomography (OCT) scans were manually graded by Central Administrative Research Facility certified graders and quality assured by clinicians of the Network of Ophthalmic Reading Centres UK. Captured retinal features included those associated with AMD (≥1 drusen, pigmentary changes, geographic atrophy or exudative AMD; either imaging modality), glaucoma (≥0.7 cup-disc ratio, ≥0.2 cup-disc ratio difference between eyes, other abnormal disc features; photographs only) and retinopathy (characteristic features of diabetic retinopathy with or without microaneurysms; either imaging modality). Suspected cases of these conditions were characterised with reference to diagnostic records, physical and biochemical measurements.ResultsAmong 68,514 UKBB participants who underwent retinal imaging, the mean age was 57.3 years (standard deviation 8.2), 45.7% were men and 90.6% were of White ethnicity. A total of 64,367 participants had gradable colour fundus photographs and 68,281 had gradable OCT scans in at least one eye. Retinal features suggestive of AMD and glaucoma were identified in 15,176 and 2184 participants, of whom 125 (0.8%) and 188 (8.6%), respectively, had a recorded diagnosis. Of 264 participants identified to have retinopathy with microaneurysms, 251 (95.1%) had either diabetes or hypertension.ConclusionsThis dataset represents a valuable addition to what is currently available in UKBB, providing important insights to both ocular and systemic health.

Laser Treatment Modalities for Diabetic Retinopathy

Diabetes is a chronic progressive metabolic disorder that is caused by the body's inability to regulate blood glucose levels. If uncontrolled, it can lead to various complications. Among its various complications, long-term diabetes leads to diabetic retinopathy (DR). It is a disease involving blood vessels and the destruction of retinal nerves. It is usually classified into two types: proliferativediabetic retinopathy (PDR) and nonproliferative diabetic retinopathy (NPDR). It progresses and causes loss of vision. The leading cause of loss of vision is diabetic macular edema (DME). The argon laser is used as a modality in the management of PDR. There are various types of laser photocoagulation, such as peripheral retinal laser photocoagulation,focal macular laser photocoagulation, and grid photocoagulation. DR results in various adverse consequences such as vitreous hemorrhage, fibrosis, traction, detachment of the retina, and glaucoma. To assess DR, a detailedfundus examination with a slit lamp biomicroscopeneeds to be done. Seven-standard field stereoscopic-color fundus photography needs to be done for documentation and follow-up. Patients with diabetesmellitus (DM) type 1 have a greater risk of suffering fromDR. Another major complication of the condition is DME, which is characterized by an increase in the permeability of vessels and the thickening of the central part of the retina along with the accumulation of hard exudates on the macula. This article discussesvarious laser therapy modalities for the treatment of DR, their types, mechanisms, and aims. Clinical features of DR include abnormal dilatation of capillaries, and outpouchings in the form of microaneurysm from the capillary wallareone of the earliest and most dangerous changes; later, non-perfusion of the retina occurs, which is associated with cotton wool spots and blot hemorrhages. In patients suffering from PDR and maculopathy, peripheral retinal laser photocoagulation is used as a mode of intervention.