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Type 2 diabetes mellitus has a worldwide prevalence of 10.5% in the adult population (20-79 years), and by 2045, the prevalence is expected to keep rising to one in eight adults living with diabetes. Mild cognitive impairment has a global prevalence of 19.7% in adults aged 50 years. Both conditions have shown a concerning increase in prevalence rates over the past 10 years, highlighting a growing public health challenge. Future forecasts indicate that the prevalence of dementia (no estimations done for individuals with mild cognitive impairment) is expected to nearly triple by 2050. Type 2 diabetes mellitus is a risk factor for the development of cognitive impairment, and such impairment increase the likelihood of poor glycemic/metabolic control. High phytate intake has been shown to be a protective factor against the development of cognitive impairment in observational studies. Diary phytate intake might reduce the micro- and macrovascular complications of patients with type 2 diabetes mellitus through different mechanisms. We describe the protocol of the first trial (the PHYND trial) that evaluate the effect of daily phytate supplementation over 56 weeks with a two-arm double-blind placebo-controlled study on the progression of mild cognitive impairment, cerebral iron deposition, and retinal involvement in patients with type 2 diabetes mellitus. Our hypothesis proposes that phytate, by inhibiting advanced glycation end product formation and chelating transition metals, will improve cognitive function and attenuate the progression from Mild Cognitive Impairment to dementia in individuals with type 2 diabetes mellitus and mild cognitive impairment. Additionally, we predict that phytate will reduce iron accumulation in the central nervous system, mitigate neurodegenerative changes in both the central nervous system and retina, and induce alterations in biochemical markers associated with neurodegeneration., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Pujol, Sanchis, Tamayo, Nicolau, Grases, Espino, Estremera, Rigo, Amengual, Rodríguez, Ribes, Gomila, Simó-Servat and Masmiquel.)

Purpose: The worldwide prevalence of diabetes mellitus (DM) continues to increase. As DM is linked to various ophthalmological comorbidities, it is crucial to understand the incidence and the treatment patterns of these complications to minimise the treatment burden for the patient and the healthcare system. This study aims to evaluate the incidence and prevalence of diabetic macular oedema (DME) and proliferative diabetic retinopathy (PDR) and to analyse intravitreal (IVT) treatment patterns and responses in the Finnish population with diabetes., Methods: A nationwide data register containing details of over 20-year-old individuals with diabetes was used in the analyses., Results: The incidence and prevalence of DME and PDR among the Finnish population with diabetes either declined or remained stable during 2007-2017 (Incidence rate: DME -40.8%, PDR -65.3%; prevalence rate: DME +4.7%, PDR -11.2%). During the same period, number of persons suffering from diabetes increased by +58.3%. The total number of IVT injections increased by 261.7%; the number of patients receiving IVT treatments increased by 133.6% from 2011 to 2017, reflecting changes in patient numbers in the ophthalmology departments. Furthermore, irrespective of the rising number of patients with diabetes, the numbers with visual impairment declined by 75.8% among DME and by 75.7% among PDR patients in 2007-2017., Conclusions: Regardless of the considerable increase in the workload of ophthalmology departments, the healthcare system has been able to reduce both the age and sex standardised incidence of DME and PDR among the diabetic population suffering from a visual impairment associated with this disease., (© 2024 The Authors. Acta Ophthalmologica published by John Wiley & Sons Ltd on behalf of Acta Ophthalmologica Scandinavica Foundation.)

Objectives: Anti-vascular endothelial growth factor (VEGF) therapy restores retinal architecture and enhances vision in diabetic macular edema (DME). Bevacizumab is an off-label anti-VEGF drug that effectively treats DME. The safety and efficacy of bevacizumab biosimilars, which are more affordable than the original medication, still need to be established. This study aimed to assess the cost-effectiveness, efficacy, and safety of biosimilars for treating patients with naïve DME across various price ranges that are accessible in the Indian market., Materials and Methods: Two biosimilars, BevaciRelTM (Reliance Life Sciences Pvt. Ltd.) and ZyBev (Cadila Healthcare Limited), were compared to their original, Avastin (Roche Products [India] Pvt. Ltd.), in a randomized, control study. Three end-notes were used to assess safety and efficacy: persistence, improvement, and adverse events. Cost-effective analysis was carried out using a decision-tree analysis model., Results: This study included 69 (59%) men and 54 (41%) women with naïve DME. The cohort had an average log MAR visual acuity of 0.87 ± 0.22, and the central retinal thickness at baseline on OCT was 398.5 ± 37.61 μm. The visual acuity showed a similar improvement, and there was a decrease in central retinal thickness as observed on OCT across the groups. The incremental cost-effectiveness ratio was 10.8., Conclusions: The biosimilars of bevacizumab are safe and efficacious in treating DME in a cost-effective manner., (Copyright © 2024 Copyright: © 2024 Indian Journal of Pharmacology.)

The precise segmentation of retinal vasculature is crucial for the early screening of various eye diseases, such as diabetic retinopathy and hypertensive retinopathy. Given the complex and variable overall structure of retinal vessels and their delicate, minute local features, the accurate extraction of fine vessels and edge pixels remains a technical challenge in the current research. To enhance the ability to extract thin vessels, this paper incorporates a pyramid channel attention module into a U-shaped network. This allows for more effective capture of information at different levels and increased attention to vessel-related channels, thereby improving model performance. Simultaneously, to prevent overfitting, this paper optimizes the standard convolutional block in the U-Net with the pre-activated residual discard convolution block, thus improving the model's generalization ability. The model is evaluated on three benchmark retinal datasets: DRIVE, CHASE_DB1, and STARE. Experimental results demonstrate that, compared to the baseline model, the proposed model achieves improvements in sensitivity (Sen) scores of 7.12%, 9.65%, and 5.36% on these three datasets, respectively, proving its strong ability to extract fine vessels. [ABSTRACT FROM AUTHOR]

Bibliometric analysis is a rigorous method to analyze significant quantities of bibliometric data to assess their impact on a particular field. This study used bibliometric analysis to investigate the academic research on diabetes detection and classification from 2000 to 2023. The PRISMA 2020 framework was followed to identify, filter, and select relevant papers. This study used the Web of Science database to determine relevant publications concerning diabetes detection and classification using the keywords "diabetes detection", "diabetes classification", and "diabetes detection and classification". A total of 863 publications were selected for analysis. The research applied two bibliometric techniques: performance analysis and science mapping. Various bibliometric parameters, including publication analysis, trend analysis, citation analysis, and networking analysis, were used to assess the performance of these articles. The analysis findings showed that India, China, and the United States are the top three countries with the highest number of publications and citations on diabetes detection and classification. The most frequently used keywords are machine learning, diabetic retinopathy, and deep learning. Additionally, the study identified "classification", "diagnosis", and "validation" as the prevailing topics for diabetes identification. This research contributes valuable insights into the academic landscape of diabetes detection and classification. [ABSTRACT FROM AUTHOR]

The early diagnosis of diabetic retinopathy (DR) can effectively prevent irreversible vision loss and assist ophthalmologists in providing timely and accurate treatment plans. However, the existing methods based on deep learning have a weak perception ability of different scale information in retinal fundus images, and the segmentation capability of subtle lesions is also insufficient. This paper aims to address these issues and proposes MLNet for DR lesion segmentation, which mainly consists of the Multi-Scale Attention Block (MSAB) and the Lesion Perception Block (LPB). The MSAB is designed to capture multi-scale lesion features in fundus images, while the LPB perceives subtle lesions in depth. In addition, a novel loss function with tailored lesion weight is designed to reduce the influence of imbalanced datasets on the algorithm. The performance comparison between MLNet and other state-of-the-art methods is carried out in the DDR dataset and DIARETDB1 dataset, and MLNet achieves the best results of 51.81% mAUPR, 49.85% mDice, and 37.19% mIoU in the DDR dataset, and 67.16% mAUPR and 61.82% mDice in the DIARETDB1 dataset. The generalization experiment of MLNet in the IDRiD dataset achieves 59.54% mAUPR, which is the best among other methods. The results show that MLNet has outstanding DR lesion segmentation ability. [ABSTRACT FROM AUTHOR]

To solve the issue of diagnosis accuracy of diabetic retinopathy (DR) and reduce the workload of ophthalmologists, in this paper we propose a prior-guided attention fusion Transformer for multi-lesion segmentation of DR. An attention fusion module is proposed to improve the key generator to integrate self-attention and cross-attention and reduce the introduction of noise. The self-attention focuses on lesions themselves, capturing the correlation of lesions at a global scale, while the cross-attention, using pre-trained vessel masks as prior knowledge, utilizes the correlation between lesions and vessels to reduce the ambiguity of lesion detection caused by complex fundus structures. A shift block is introduced to expand association areas between lesions and vessels further and to enhance the sensitivity of the model to small-scale structures. To dynamically adjust the model's perception of features at different scales, we propose the scale-adaptive attention to adaptively learn fusion weights of feature maps at different scales in the decoder, capturing features and details more effectively. The experimental results on two public datasets (DDR and IDRiD) demonstrate that our model outperforms other state-of-the-art models for multi-lesion segmentation., (© 2024. The Author(s).)

Reliable automatic diagnosis of Diabetic Retinopathy (DR) and Macular Edema (ME) is an invaluable asset in improving the rate of monitored patients among at-risk populations and in enabling earlier treatments before the pathology progresses and threatens vision. However, the explainability of screening models is still an open question, and specifically designed datasets are required to support the research. We present MAPLES-DR (MESSIDOR Anatomical and Pathological Labels for Explainable Screening of Diabetic Retinopathy), which contains, for 198 images of the MESSIDOR public fundus dataset, new diagnoses for DR and ME as well as new pixel-wise segmentation maps for 10 anatomical and pathological biomarkers related to DR. This paper documents the design choices and the annotation procedure that produced MAPLES-DR, discusses the interobserver variability and the overall quality of the annotations, and provides guidelines on using the dataset in a machine learning context., (© 2024. The Author(s).)

Diabetic retinopathy (DR) is a serious ocular condition that requires effective monitoring and treatment by ophthalmologists. However, constructing a reliable DR grading model remains a challenging and costly task, heavily reliant on high-quality training sets and adequate hardware resources. In this paper, we investigate the knowledge transferability of large-scale pre-trained models (LPMs) to fundus images based on prompt learning to construct a DR grading model efficiently. Unlike full-tuning which fine-tunes all parameters of LPMs, prompt learning only involves a minimal number of additional learnable parameters while achieving a competitive effect as full-tuning. Inspired by visual prompt tuning, we propose Semantic-oriented Visual Prompt Learning (SVPL) to enhance the semantic perception ability for better extracting task-specific knowledge from LPMs, without any additional annotations. Specifically, SVPL assigns a group of learnable prompts for each DR level to fit the complex pathological manifestations and then aligns each prompt group to task-specific semantic space via a contrastive group alignment (CGA) module. We also propose a plug-and-play adapter module, Hierarchical Semantic Delivery (HSD), which allows the semantic transition of prompt groups from shallow to deep layers to facilitate efficient knowledge mining and model convergence. Our extensive experiments on three public DR grading datasets demonstrate that SVPL achieves superior results compared to other transfer tuning and DR grading methods. Further analysis suggests that the generalized knowledge from LPMs is advantageous for constructing the DR grading model on fundus images.

Background: Diabetic retinopathy (DR), one of the most common microvascular complications of diabetes mellitus (DM), is a major contributor of vision impairment and blindness worldwide. Studies have shown that air pollution exposure is adversely associated with DM. However, evidence is scarce regarding how air pollution exposure affects DR. This study aimed to investigate the association between ambient air pollution exposure and DR risk., Methods: The study population was based on the Fujian Eye Study (FJES), an ophthalmologic, epidemiologic survey investigating the eye health condition of residents in Fujian Province from 2018 to 2019. Daily average concentrations of ambient air pollutants (PM 2.5 , PM 10 , SO 2 , NO 2 , and O 3 ) were acquired from a high-resolution air quality dataset in China from 2013 to 2018. We used a logistic regression model to examine the associations between DR risk and long-term air pollution at various exposure windows., Results: A total of 2405 out of the 8211 participants were diagnosed with diabetes, among whom 183 had DR. Ambient air pollution, especially particulate matter (i.e., PM 2.5 and PM 10 ) and NO 2 were positively associated with DR prevalence among all the study subjects. Ambient SO 2 and O 3 concentrations were not associated with DR prevalence. PM 2.5 and NO 2 seemed to be borderline significantly associated with increased prevalence of DR in subjects with DM, especially under the model adjusted for sex, age, BMI, SBP, and DBP., Conclusions: These findings showed that long-term exposure to ambient particulate matter and NO 2 was associated with a high DR risk in Fujian province, where ambient air pollution is relatively low., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Traditional Chinese medicine (TCM) is useful in disease treatment and prevention. Genipin is an active TCM compound used to treat diabetic retinopathy (DR). In this study, a network pharmacology (NP)-based approach was employed to investigate the therapeutic mechanisms underlying genipin administration in DR., Methods: The potential targets of DR were identified using the gene expression omnibus (GEO) database. TCM database screening and NP were used to predict the potential active targets and pathways of genipin in DR. Cell viability was tested in vitro to determine the effects of different doses of glucose and genipin on Human Retinal Microvascular Endothelial Cells (hRMECs). CCK-8, CCK-F, colony formation, CellTiter-Lum, Annexin V-FITC, wound healing, Transwell, tube-forming, reactive oxygen species (ROS), and other assay kits were used to detect the effects of genipin on hRMECs during high levels of glucose. In vivo, a streptozotocin (STZ)-mouse intraocular genipin injection (IOI.) model was used to explore the effects of genipin on diabetes-induced retinal dysfunction. Western blotting was performed to identify the cytokines involved in proliferation, apoptosis, angiogenesis, ROS, and inflammation. The protein expression of the AKT/ PI3K/ HIF-1α and AGEs/ RAGE pathways was also examined., Results: Approximately 14 types of TCM, and nearly 300 active ingredients, including genipin, were identified. The NP approach successfully identified the HIF-1α and AGEs-RAGE pathways, with the EGR1 and UCP2 genes, as key targets of genipin in DR. In the in vitro and in vivo models, we discovered that high glucose increased cell proliferation, apoptosis, angiogenesis, ROS, and inflammation. However, genipin application regulated cell proliferation and apoptosis, inhibited angiogenesis, and reduced ROS and inflammation in the HRMECs exposed to high glucose. Furthermore, the retinal thickness in the genipin-treated group was lower than that in the untreated group. AKT/ PI3K/ HIF-1α and AGEs/ RAGE signaling was increased by high glucose levels; however, genipin treatment decreased AKT/ PI3K and AGEs/ RAGE pathway expressions. Genipin also increased HIF-1α phosphorylation, oxidative phosphorylation of ATP synthesis, lipid peroxidation, and the upregulation of oxidoreductase. Genipin was found to protect HG-induced hRMECs and the retina of STZ-mice, based on; 1 the inhibition of UCP2 and Glut1 decreased intracellular glucose, and glycosylation; 2 the increased presence of HIF-1α, which increased oxidative phosphorylation and decreased substrate phosphorylation; 3 the increase in oxidative phosphorylation from ATP synthesis increased lipid peroxidation and oxidoreductase activity, and; 4 the parallel effect of phosphorylation and glycosylation on vascular endothelial growth factor (VEGF), MMP9, and Scg3., Conclusion: Based on NP, we demonstrated the potential targets and pathways of genipin in the treatment of DR and confirmed its effective molecular mechanism in vitro and in vivo. Genipin protects cells and tissues from high glucose levels by regulating phosphorylation and glycosylation. The activation of the HIF-1α pathway can also be used to treat DR. Our study provides new insights into the key genes and pathways associated with the prognosis and pathogenesis of DR., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier GmbH. All rights reserved.)

Diabetic retinopathy (DR) is a major complication of diabetes mellitus and is a leading cause of vision loss globally. A prompt and accurate diagnosis is crucial for ensuring favorable visual outcomes, highlighting the need for increased access to medical care. The recent remarkable advancements in artificial intelligence (AI) have raised high expectations for its role in disease diagnosis and prognosis prediction across various medical fields. In addition to achieving high precision comparable to that of ophthalmologists, AI-based diagnosis of DR has the potential to improve medical accessibility, especially through telemedicine. In this review paper, we aim to examine the current role of AI in the diagnosis of DR and explore future directions.

Background: Diabetic retinopathy (DR) is one of the most common complications of diabetes mellitus. The global burden is immense with a worldwide prevalence of 8.5%. Recent advancements in artificial intelligence (AI) have demonstrated the potential to transform the landscape of ophthalmology with earlier detection and management of DR., Objective: This study seeks to provide an update and evaluate the accuracy and current diagnostic ability of AI in detecting DR versus ophthalmologists. Additionally, this review will highlight the potential of AI integration to enhance DR screening, management, and disease progression., Methods: A systematic review of the current landscape of AI's role in DR will be undertaken, guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) model. Relevant peer-reviewed papers published in English will be identified by searching 4 international databases: PubMed, Embase, CINAHL, and the Cochrane Central Register of Controlled Trials. Eligible studies will include randomized controlled trials, observational studies, and cohort studies published on or after 2022 that evaluate AI's performance in retinal imaging detection of DR in diverse adult populations. Studies that focus on specific comorbid conditions, nonimage-based applications of AI, or those lacking a direct comparison group or clear methodology will be excluded. Selected papers will be independently assessed for bias by 2 review authors (JS and DM) using the Quality Assessment of Diagnostic Accuracy Studies tool for systematic reviews. Upon systematic review completion, if it is determined that there are sufficient data, a meta-analysis will be performed. Data synthesis will use a quantitative model. Statistical software such as RevMan and STATA will be used to produce a random-effects meta-regression model to pool data from selected studies., Results: Using selected search queries across multiple databases, we accumulated 3494 studies regarding our topic of interest, of which 1588 were duplicates, leaving 1906 unique research papers to review and analyze., Conclusions: This systematic review and meta-analysis protocol outlines a comprehensive evaluation of AI for DR detection. This active study is anticipated to assess the current accuracy of AI methods in detecting DR., International Registered Report Identifier (irrid): DERR1-10.2196/57292., (©Jaime Angeles Sesgundo III, David Collin Maeng, Jumelle Aubrey Tukay, Maria Patricia Ascano, Justine Suba-Cohen, Virginia Sampang. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 27.05.2024.)

Objective: To examine the therapeutic mechanism of astragaloside IV (AS-IV) in the management of retinal ganglion cell (RGC) injury induced by high glucose (HG), a comprehensive approach involving the integration of network pharmacology and conducting in vitro and in vivo experiments was utilized., Methods: A rat model of diabetic retinopathy (DR) injury was created by administering streptozotocin through intraperitoneal injection. Additionally, a model of RGC injury induced by HG was established using a glucose concentration of 0.3 mmol/mL. Optical coherence tomography (OCT) images were captured 8 weeks after the injection of AS-IV. AS-IV and FBS were added to the culture medium and incubated for 48 h. The viability of cells was assessed using a CCK-8 assay, while the content of reactive oxygen species (ROS) was measured using DCFH-DA. Apoptosis was evaluated using Annexin V-PI. To identify the targets of AS-IV, hyperglycemia, and RGC, publicly available databases were utilized. The Metascape platform was employed for conducting GO and KEGG enrichment analyses. The STRING database in conjunction with Cytoscape 3.7.2 was used to determine common targets of protein-protein interactions (PPIs) and to identify the top 10 core target proteins in the RGC based on the MCC algorithm. qRT-PCR was used to measure the mRNA expression levels of the top10 core target proteins in RGCs., Results: OCT detection indicated that the thickness of the outer nucleus, and inner and outer accessory layers of the retina increased in the AS-IV treated retina compared to that in the DM group but decreased compared to that in the CON group. Coculturing RGC cells with AS-IV after HG induction resulted in a significant increase in cell viability and a decrease in ROS and apoptosis, suggesting that AS-IV can reduce damage to RGC cells caused by high glucose levels by inhibiting oxidative stress. There were 14 potential targets of AS-IV in the treatment of RGC damage induced by high glucose levels. The top 10 core target proteins identified by the MCC algorithm were HIF1α, AKT1, CTNNB1, SMAD2, IL6, SMAD3, IL1β, PPARG, TGFβ1, and NOTCH3. qRT-PCR analysis showed that AS-IV could upregulate the mRNA expression levels of SMAD3, TGF-β1, and NOTCH3, and downregulate the mRNA expression levels of HIF1α, AKT1, CTNNB1, SMAD2, SMAD3, and IL-1β in high glucose-induced RGC cells., Conclusion: The findings of this study validate the efficacy of astragaloside IV in the treatment of DR and shed light on the molecular network involved. Specifically, HIF1α, AKT1, CTNNB1, SMAD2, SMAD3, and IL-1β were identified as the crucial candidate molecules responsible for the protective effects of astragaloside IV on RGCs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Diabetic retinopathy is not cured efficiently and changes of lifestyle measures may delay early retinal injury in diabetes. The aim of our study was to investigate the effects of reduced daily light exposure on retinal vascular changes in streptozotocin (STZ)-induced model of DM with emphasis on inflammation, Aqp4 expression, visual cycle and cholesterol metabolism-related gene expression in rat retina and RPE. Male Wistar rats were divided into the following groups: 1. control; 2. diabetic group (DM) treated with streptozotocin (100 mg/kg); 3. group exposed to light/dark cycle 6/18 h (6/18); 4. diabetic group exposed to light/dark cycle 6/18 h (DM+6/18). Retinal vascular abnormalities were estimated based on lectin staining, while the expression of genes involved in the visual cycle, cholesterol metabolism, and inflammation was determined by qRT-PCR. Reduced light exposure alleviated vasculopathy, gliosis and the expression of IL-1 and TNF-α in the retina with increased perivascular Aqp4 expression. The expression of genes involved in visual cycle and cholesterol metabolism was significantly up-regulated in RPE in DM+6/18 vs. DM group. In the retina only the expression of APOE was significantly higher in DM+6/18 vs. DM group. Reduced light exposure mitigates vascular changes and gliosis in DM via its anti-inflammatory effect, increased retinal cholesterol turnover and perivascular Aqp4 expression., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objective: Diabetes mellitus is a chronic disease with high morbidity and mortality, affecting 537 million adults worldwide. Spain is the second European country in prevalence, with 14.8% in the population aged twenty/seventy-nine years; with 11.6 cases per 1,000 people/year. Diabetic retinopathy (DR) is the fifth cause of vision loss worldwide and the seventh cause of blindness/visual impairment among members of the National Organization of the Blind in Spain (ONCE). Early detection of DR prevents blindness in diabetics and is conditioned by glycosylated hemoglobin. The aim of this paper was to analyze the management of diabetic patients in Aljarafe region (Seville) and identify opportunities for improvement in the coordination of their follow-up between the Primary Care physician and the ophthalmologist., Methods: A retrospective observational study (2016-2019) was carried out, with patients registered in the diabetic census of the twenty-eight municipalities of Aljarafe. The primary care and hospital health history, and telemedicine program were consulted. About statistical analysis, for qualitative variables, totals and percentages were calculated; for quantitative variables, mean and standard deviation (if normally distributed) and median and quartiles (if non-normally distributed)., Results: There were 17,175 diabetics registered in Aljarafe (5.7% of the population); 14,440 patients (84.1%) had some determination of hemoglobin during the period, 9,228 (63.9%) had all of them in the appropriate range. Fundoscopic control was performed on 12,040 diabetics (70.1%), and of those who did not, 346 (10.6%) had all of them out of range. There were 1,878 (10.9%) patients without fundoscopic or metabolic control, 1,019 (54.3%) were women, 1,219 (64.9%) were under sixty-five years of age, 1,019 (54.3%) had severe comorbidity., Conclusions: Most patients have adequate screening, and more than half have determinations within range. However, a significant percentage with no glycated hemoglobin within range lack fundoscopic control, and another smaller group lack fundoscopic or metabolic control, with inter-municipal variability. We propose to improve communication channels between levels.

Background: Glaucoma and diabetic retinopathy (DR) are the leading causes of irreversible retinal damage leading to blindness. Early detection of these diseases through regular screening is especially important to prevent progression. Retinal fundus imaging serves as the principal method for diagnosing glaucoma and DR. Consequently, automated detection of eye diseases represents a significant application of retinal image analysis. Compared with classical diagnostic techniques, image classification by convolutional neural networks (CNN) exhibits potential for effective eye disease detection., Methods: This paper proposes the use of MATLAB - retrained AlexNet CNN for computerized eye diseases identification, particularly glaucoma and diabetic retinopathy, by employing retinal fundus images. The acquisition of the database was carried out through free access databases and access upon request. A transfer learning technique was employed to retrain the AlexNet CNN for non-disease (Non_D), glaucoma (Sus_G) and diabetic retinopathy (Sus_R) classification. Moreover, model benchmarking was conducted using ResNet50 and GoogLeNet architectures. A Grad-CAM analysis is also incorporated for each eye condition examined., Results: Metrics for validation accuracy, false positives, false negatives, precision, and recall were reported. Validation accuracies for the NetTransfer (I-V) and netAlexNet ranged from 89.7% to 94.3%, demonstrating varied effectiveness in identifying Non_D, Sus_G, and Sus_R categories, with netAlexNet achieving a 93.2% accuracy in the benchmarking of models against netResNet50 at 93.8% and netGoogLeNet at 90.4%., Conclusions: This study demonstrates the efficacy of using a MATLAB-retrained AlexNet CNN for detecting glaucoma and diabetic retinopathy. It emphasizes the need for automated early detection tools, proposing CNNs as accessible solutions without replacing existing technologies., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Arias-Serrano I et al.)

Objective: This study aimed to analyze the effect of optical coherence tomography angiography (OCTA) on diabetic macular edema (DME) staging and assess the efficacy of laser photocoagulation., Methods: Eighty-six patients (141 eyes) with suspected DME who visited our hospital from August 2019 to March 2022 were selected and underwent fundus angiography and OCTA. The two examination methods were compared in terms of their efficacy in macular edema staging. Subsequently, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of OCTA in diagnosing DME were assessed using fundus angiography as the gold standard. In patients with clinically significant macular edema (CSME) treated with laser photocoagulation, the central concave non-perfused zone (FAZ), vascular density (VD), central macular retinal thickness (CRT), whole retinal blood flow density (FD-300), superficial capillary plexus (SCP), and deep capillary plexus (DCP) were measured using the OCTA 3 mm × 3 mm mode before treatment, at 3 months after treatment, and at 6 months after treatment. SCP, deep capillary plexus (DCP), blood flow density (VD), best corrected visual acuity (BCVA), and central retinal thickness (CRT) were recorded before treatment, 3 months after treatment, and 6 months after treatment. The correlation between BCVA and pre-treatment OCTA parameters at 6 months after treatment was analyzed using Pearson's correlation., Results: Fundus angiography was performed in 86 patients (141 eyes) with suspected DME. Of the 141 eyes, 44 had no leakage, 52 had diffuse edema, 40 had focal macular edema, and 5 had eyes ischemia. A total of 97 eyes showed CSME on fundus angiography. Using fundus angiography as the gold standard, OCTA exhibited a sensitivity of 97.94 %, a specificity of 63.64 %, and an accuracy of 87.23 % in diagnosing CSME. The Kappa value between OCTA and fundus angiography was 0.674. The receiver operating characteristic curve revealed that the area under the curve (AUC) of OCTA in diagnosing CSME was 0.808 (95 % confidence interval: 0.717-0.899). The BCVA was higher, while the CRT was lower in CSME patients at 3 and 6 months after treatment (P<0.05). No significant difference was observed in the OCTA parameters in CSME patients at 3 months after treatment compared with that before treatment (P>0.05). Similarly, no significant difference was found in the FD300 of CSME patients at 6 months after treatment compared with that before treatment (P>0.05). However, the FAZ area, DCP-VD (overall, central concave, and paracentral concave), and SCP-VD (overall, central concave, and paracentral concave) were higher in CSME patients at 6 months after treatment compared with that before treatment (P<0.05). Pearson's correlation showed that BCVA was positively correlated with pre-treatment FAZ area, DCP-VD, and SCP-VD (r>0, P<0.05), and negatively associated with CRT (r<0, P<0.05)., Conclusion: OCTA exhibited high sensitivity and specificity in diagnosis and staging DME. It adeptly captures the microvascular and visual changes in the central macular recess before and after laser photocoagulation therapy, which can quantitatively guide the follow-up treatment of DME., Competing Interests: Declaration of competing interest The authors declared no conflict of interest existing in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Purpose: The Fat Sand Rat (Psammomys obesus) recapitulates several features of human pre-proliferative diabetic retinopathy, but data are restricted to wild animals, incompatible with stringent biomedical research criteria. To overcome this barrier, we characterized retinal changes in a colony of P. obsesus maintained under strictly controlled housing conditions., Methods: Animals were maintained on low or high caloric energy diets, and raised under either standard (12 h light/12 h dark) or shortened (5 h light/5 h dark) photoperiods. Visual responses were tested by electroretinography, while structural/molecular changes were assayed by immunochemistry and molecular biology (RNAseq and qPCR)., Results: Whereas high calorie diet alone did not induce hyperglycemia, coupled with short photoperiod >80 % animals developed severe hyper-insulinemia by 15 weeks, and 16 % animals further developed hyperglycemia. In these groups, electroretinography showed significant declines in visual responses in both hyper-insulinemic and hyperglycemic animals, especially in photopic (cone) responses. Transcriptomics analysis of hyperglycemic compared to low caloric controls revealed major upregulation in pathways involved in glial activation, extracellular matrix remodeling, inflammation, cytokine production, partial ischemic responses and angiogenesis. Western blotting against rhodopsin and cone opsin also showed decreased levels in both groups, overall decreases being greater for cones than rods in hyperglycemic animals., Conclusions: P. obesus maintained in rigorously monitored captive conditions, albeit showing attenuated responses to dietary overload compared to wild counterparts, nevertheless do develop some retinal features of diabetic retinopathy-like degeneration. Such a colony with known sanitary status opens their broader use for biomedical research., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Diabetic eye detection has become a major concern across the globe, which could be effectively addressed by automated detection using a deep convolutional neural network (DCNN). CNN models have better detection and classification accuracy than other state-of-theart models. In this paper, a differential evolution (DE)-optimized CNN has been proposed for the single-step classification of diabetic retinopathy (DR) and glaucoma images. DE has been used to find out the optimized values of four hyper-parameters of CNN, i.e., the number of filters in the first layer, the filter size, the number. of convolution layers, and the number of strides. Simulation has been done using three publicly available datasets, and the accuracy obtained is 87.8%, 92.3%, and 88.7%, respectively, which outperforms other models. No other state-of-the-art model has used DE for hyper-parameter tuning in CNN models. Also, no other additional segmentation approach or handcrafted features have been used. The model has been kept simple to reduce computational costs. [ABSTRACT FROM AUTHOR]

Automated segmentation of diabetic retinopathy (DR) lesions is crucial for assessing DR severity and diagnosis. Most previous segmentation methods overlook the detrimental impact of texture information bias, resulting in suboptimal segmentation results. Additionally, the role of lesion shape is not thoroughly considered. In this paper, we propose a lightweight frequency recalibration network (LFRC-Net) for simultaneous multi-lesion DR segmentation, which integrates a frequency recalibration module into the bottleneck layers of the encoder to analyze texture information and shape features together. The module utilizes a Gaussian pyramid to generate features at different scales, constructs a Laplacian pyramid using a difference of Gaussian filter, and then analyzes object features in different frequency domains with the Laplacian pyramid. The high-frequency component handles texture information, while the low-frequency area focuses on learning the shape features of DR lesions. By adaptively recalibrating these frequency representations, our method can differentiate the objects of interest. In the decoder, we introduce a residual attention module (RAM) to enhance lesion feature extraction and efficiently suppress irrelevant information. We evaluate the proposed model's segmentation performance on two public datasets, IDRiD and DDR, and a private dataset, an ultra-wide-field fundus images dataset. Extensive comparative experiments and ablation studies are conducted across multiple datasets. With minimal model parameters, our approach achieves an mAP_PR of 60.51%, 34.83%, and 14.35% for the segmentation of EX, HE, and MA on the DDR dataset and also obtains excellent results for EX and SE on the IDRiD dataset, which validates the effectiveness of our network. [ABSTRACT FROM AUTHOR]

Magnesium (Mg), an essential nutrient with a wide area of physiological roles, stands as a cofactor in over 600 enzymatic reactions involved in the synthesis of proteins and nucleic acids, DNA repair, neuromuscular functions, neuronal transmission, cardiac rhythm regulation, and the modulation of metabolic pathways, as well as acting as a natural blocker for the calcium channels. Our objective was to highlight the most recent clinical data with respect to daily Mg intake (DMI) and metabolic traits, particularly type 2 diabetes mellitus (DM). This was a PubMed-based review of the English-language medical papers across different key terms of search; the time frame was from January 2019 until April 2024. We included (clinically relevant) original studies and excluded cases reports, series, reviews, editorials, opinion, experimental studies, and non-human data as well as studies that did not specifically assessed DMI and only provided assays of serum Mg, studies on patients diagnosed with type 1 or secondary DM. A total of 30 studies were included and we organized the key findings into several sections as follows. Studies investigating DMI in relationship with the adherence to local recommendations in diabetic subjects (n = 2, one transversal and another retrospective cohort; N = 2823) found that most of them had lower DMI. Deficient DMI was correlated with the risk of developing/having DM across five studies (n = 5, one prospective and four of cross-sectional design; N = 47,166). An inverse correlation between DMI and DM prevalence was identified, but these data are presented amid a rather heterogeneous spectrum. Four novel studies (N = 7279) analysed the relationship between DMI and DM control according to various methods (HbA1c, fasting and postprandial glycaemia, and insulin); the association may be linear in diabetic subjects only at certain levels of DMI; additionally, the multifactorial influence on HBA1c should take into consideration this dietary determinant, as well, but there are no homogenous results. Three studies concerning DMI and diabetic complications (one cross-sectional, one prospective, and another case–control study) in terms of retinopathy (n = 1, N = 3794) and nephropathy (n = 2, N = 4805) suggested a lower DMI was associated with a higher risk of such complications. Additionally, two other studies (one prospective and one retrospective cohort) focused on mortality (N = 6744), which, taking only certain mortality indicators into consideration, might be decreased in the subgroups with a higher DMI. Seven studies (N = 30,610) analysed the perspective of DMI in the general population with the endpoint of different features amid glucose profile, particularly, insulin resistance. Concerning HOMA-IR, there were three confirmatory studies and one non-confirmatory, while fasting plasma glucose was highlighted as inversely correlated with a DMI (n = 1). The highest level of evidence regarding Mg supplementation effects on glucose metabolism stands on seven randomised controlled trials (N = 350). However, the sample size was reduced (from 14 to 86 individuals per study, either diabetic or pre-diabetic) and outcomes were rather discordant. These clinical aspects are essential from a multidisciplinary perspective and further trials are mandatory to address the current areas of discordant results. [ABSTRACT FROM AUTHOR]

Background: Diabetic macular edema (DME) is the main cause of irreversible vision loss in patients with diabetes mellitus (DM), resulting in a certain burden to patients and society. With the increasing incidence of DME, more and more researchers are focusing on it., Methods: The papers related to DME between 2012 and 2022 from the Web of Science core Collection were searched in this study. Based on CiteSpace and VOS viewer, these publications were analyzed in terms of spatiotemporal distribution, author distribution, subject classification, topic distribution, and citations., Results: A total of 5165 publications on DME were included. The results showed that the research on DME is on a steady growth trend. The country with the highest number of published documents was the US. Wong Tien Yin from Tsinghua University was the author with the most published articles. The journal of Retina, the Journal of Retinal and Vitreous Diseases had a large number of publications. The article "Mechanisms of macular edema: Beyond the surface" was the highly cited literature and "Aflibercept, bevacizumab, or ranibizumab for diabetic macular edema" had the highest co-citation frequency. The treatment, diagnosis, pathogenesis, as well as etiology and epidemiological investigation of DME, have been the current research direction. Deep learning has been widely used in the medical field for its strong feature representation ability., Conclusions: The study revealed the important authoritative literature, journals, institutions, scholars, countries, research hotspots, and development trends in in the field of DME. This indicates that communication and cooperation between disciplines, universities, and countries are crucial. It can advance research in DME and even ophthalmology., Competing Interests: The authors have no conflicts of interest to disclose.All data generated or analyzed during this study are included in this published article [and its supplementary information files]., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

The accuracy and interpretability of artificial intelligence (AI) are crucial for the advancement of optical coherence tomography (OCT) image detection, as it can greatly reduce the manual labor required by clinicians. By prioritizing these aspects during development and application, we can make significant progress towards streamlining the clinical workflow. In this paper, we propose an explainable ensemble approach that utilizes transfer learning to detect fundus lesion diseases through OCT imaging. Our study utilized a publicly available OCT dataset consisting of normal subjects, patients with dry age-related macular degeneration (AMD), and patients with diabetic macular edema (DME), each with 15 samples. The impact of pre-trained weights on the performance of individual networks was first compared, and then these networks were ensemble using majority soft polling. Finally, the features learned by the networks were visualized using Grad-CAM and CAM. The use of pre-trained ImageNet weights improved the performance from 68.17% to 92.89%. The ensemble model consisting of the three CNN models with pre-trained parameters loaded performed best, correctly distinguishing between AMD patients, DME patients and normal subjects 100% of the time. Visualization results showed that Grad-CAM could display the lesion area more accurately. It is demonstrated that the proposed approach could have good performance of both accuracy and interpretability in retinal OCT image detection., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. This does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2024 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Diabetic retinopathy (DR) is a serious ocular complication that can pose a serious risk to a patient's vision and overall health. Currently, the automatic grading of DR is mainly using deep learning techniques. However, the lesion information in DR images is complex, variable in shape and size, and randomly distributed in the images, which leads to some shortcomings of the current research methods, i.e., it is difficult to effectively extract the information of these various features, and it is difficult to establish the connection between the lesion information in different regions. To address these shortcomings, we design a multi-scale dynamic fusion (MSDF) module and combine it with graph convolution operations to propose a multi-scale dynamic graph convolutional network (MDGNet) in this paper. MDGNet firstly uses convolution kernels with different sizes to extract features with different shapes and sizes in the lesion regions, and then automatically learns the corresponding weights for feature fusion according to the contribution of different features to model grading. Finally, the graph convolution operation is used to link the lesion features in different regions. As a result, our proposed method can effectively combine local and global features, which is beneficial for the correct DR grading. We evaluate the effectiveness of method on two publicly available datasets, namely APTOS and DDR. Extensive experiments demonstrate that our proposed MDGNet achieves the best grading results on APTOS and DDR, and is more accurate and diverse for the extraction of lesion information., (© 2024. The Author(s).)

Diabetic Retinopathy (DR), an ocular complication of diabetes, is a leading cause of blindness worldwide. Traditionally, DR is monitored using Color Fundus Photography (CFP), a widespread 2-D imaging modality. However, DR classifications based on CFP have poor predictive power, resulting in suboptimal DR management. Optical Coherence Tomography Angiography (OCTA) is a recent 3-D imaging modality offering enhanced structural and functional information (blood flow) with a wider field of view. This paper investigates automatic DR severity assessment using 3-D OCTA. A straightforward solution to this task is a 3-D neural network classifier. However, 3-D architectures have numerous parameters and typically require many training samples. A lighter solution consists in using 2-D neural network classifiers processing 2-D en-face (or frontal) projections and/or 2-D cross-sectional slices. Such an approach mimics the way ophthalmologists analyze OCTA acquisitions: (1) en-face flow maps are often used to detect avascular zones and neovascularization, and (2) cross-sectional slices are commonly analyzed to detect macular edemas, for instance. However, arbitrary data reduction or selection might result in information loss. Two complementary strategies are thus proposed to optimally summarize OCTA volumes with 2-D images: (1) a parametric en-face projection optimized through deep learning and (2) a cross-sectional slice selection process controlled through gradient-based attribution. The full summarization and DR classification pipeline is trained from end to end. The automatic 2-D summary can be displayed in a viewer or printed in a report to support the decision. We show that the proposed 2-D summarization and classification pipeline outperforms direct 3-D classification with the advantage of improved interpretability., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Ramin Tadayoni and Gwenolé Quellec report financial support was provided by French National Research Agency. Ramin Tadayoni and Béatrice Cochener report a relationship with Carl Zeiss Meditec that includes: consulting or advisory. Pierre Deman, Employee (ADCIS, Evolucare Technologies), Laurent Borderie, Employee (Evolucare Technologies), Hugang Ren and Niranchana Mannivanan, Employees (Carl Zeiss Meditec), (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Diabetic retinopathy (DR) is the most prevalent cause of visual impairment in adults worldwide. Typically, patients with DR do not show symptoms until later stages, by which time it may be too late to receive effective treatment. DR Grading is challenging because of the small size and variation in lesion patterns. The key to fine-grained DR grading is to discover more discriminating elements such as cotton wool, hard exudates, hemorrhages, microaneurysms etc. Although deep learning models like convolutional neural networks (CNN) seem ideal for the automated detection of abnormalities in advanced clinical imaging, small-size lesions are very hard to distinguish by using traditional networks. This work proposes a bi-directional spatial and channel-wise parallel attention based network to learn discriminative features for diabetic retinopathy grading. The proposed attention block plugged with a backbone network helps to extract features specific to fine-grained DR-grading. This scheme boosts classification performance along with the detection of small-sized lesion parts. Extensive experiments are performed on four widely used benchmark datasets for DR grading, and performance is evaluated on different quality metrics. Also, for model interpretability, activation maps are generated using the LIME method to visualize the predicted lesion parts. In comparison with state-of-the-art methods, the proposed IDANet exhibits better performance for DR grading and lesion detection., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Since different disease grades require different treatments from physicians, i.e., the low-grade patients may recover with follow-up observations whereas the high-grade may need immediate surgery, the accuracy of disease grading is pivotal in clinical practice. In this paper, we propose a Triplet-Branch Network with ContRastive priOr-knoWledge embeddiNg (TBN-CROWN) for the accurate disease grading, which enables physicians to accordingly take appropriate treatments. Specifically, our TBN-CROWN has three branches, which are implemented for representation learning, classifier learning and grade-related prior-knowledge learning, respectively. The former two branches deal with the issue of class-imbalanced training samples, while the latter one embeds the grade-related prior-knowledge via a novel auxiliary module, termed contrastive embedding module. The proposed auxiliary module takes the features embedded by different branches as input, and accordingly constructs positive and negative embeddings for the model to deploy grade-related prior-knowledge via contrastive learning. Extensive experiments on our private and two publicly available disease grading datasets show that our TBN-CROWN can effectively tackle the class-imbalance problem and yield a satisfactory grading accuracy for various diseases, such as fatigue fracture, ulcerative colitis, and diabetic retinopathy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Vitreous replacement is a commonly employed method for treating a range of ocular diseases, including posterior vitreous detachment, complex retinal detachment, diabetic retinopathy, macular hole, and ocular trauma. Various clinical substitutes for vitreous include air, expandable gas, silicone oil, heavy silicone oil, and balanced salt solution. However, these substitutes have drawbacks such as short retention time, cytotoxicity, high intraocular pressure, and the formation of cataracts, rendering them unsuitable for long-term treatment. Polymeric hydrogels possess the potential to serve as ideal vitreous substitutes due to their structure-mimicking to natural vitreous and adjustable mechanical properties. Replacement with hydrogels as the tamponade can help maintain the shape of the eyeball, apply pressure to the detached retina, and ensure the metabolic transport of substances without impairing vision. This literature review examines the required properties of artificial vitreous, including the optical properties, rheological properties, expansive force action, and physiological and biochemical functions of chemically and physically crosslinked hydrogels. The strategies for enhancing the biocompatibility and injectability of hydrogels are also summarized and discussed. From a clinical ophthalmology perspective, this paper presents the latest developments in vitreous replacement, providing clinicians with a comprehensive understanding of hydrogel clinical applications, which offers guidance for future design directions and methodologies for hydrogel development., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Masson SAS.. All rights reserved.)

Diabetic retinopathy (DR) is a severe microvascular complication of diabetes and is one of the primary causes of blindness in the working-age population in Europe and the United States. At present, no cure is available for DR, but early detection and timely intervention can prevent the rapid progression of the disease. Several treatments for DR are known, primarily ophthalmic treatment based on glycemia, blood pressure, and lipid control, which includes laser photocoagulation, glucocorticoids, vitrectomy, and antivascular endothelial growth factor (anti-VEGF) medications. Despite the clinical efficacy of the aforementioned therapies, none of them can entirely shorten the clinical course of DR or reverse retinopathy. MicroRNAs (miRNAs) are vital regulators of gene expression and participate in cell growth, differentiation, development, and apoptosis. MicroRNAs have been shown to play a significant role in DR, particularly in the molecular mechanisms of inflammation, oxidative stress, and neurodegeneration. The aim of this review is to systematically summarize the signaling pathways and molecular mechanisms of miRNAs involved in the occurrence and development of DR, mainly from the pathogenesis of oxidative stress, inflammation, and neovascularization. Meanwhile, this article also discusses the research progress and application of miRNA-specific therapies for DR., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2024 Yahan Luo and Chunxia Li.)

Diagnosing and screening for diabetic retinopathy is a well-known issue in the biomedical field. A component of computer-aided diagnosis that has advanced significantly over the past few years as a result of the development and effectiveness of deep learning is the use of medical imagery from a patient's eye to identify the damage caused to blood vessels. Issues with unbalanced datasets, incorrect annotations, a lack of sample images, and improper performance evaluation measures have negatively impacted the performance of deep learning models. Using three benchmark datasets of diabetic retinopathy, we conducted a detailed comparison study comparing various state-of-the-art approaches to address the effect caused by class imbalance, with precision scores of 93%, 89%, 81%, 76%, and 96%, respectively, for normal, mild, moderate, severe, and DR phases. The analyses of the hybrid modeling, including CNN analysis and SHAP model derivation results, are compared at the end of the paper, and ideal hybrid modeling strategies for deep learning classification models for automated DR detection are identified.

Oxidative stress is an important mechanism of aging, and in turn, aging can also aggravate oxidative stress, which leads to a vicious cycle. In the process of the brain converting light into visual signals, the eye is stimulated by harmful blue-light radiation directly. Thus, the eye is especially vulnerable to oxidative stress and becomes one of the organs most seriously involved during the aging process. Cataracts, age-related macular degeneration (AMD), glaucoma, diabetic retinopathy (DR), and dry eye are inextricably linked to the aging process and oxidative stress. Chlorogenic acid (CGA) has been demonstrated to have antioxidant and anti-inflammatory activities, and its validity has been established experimentally in numerous fields, including cardiovascular disease, metabolic disorders, cancers, and other chronic diseases. There has previously been evidence of CGA's therapeutic effect in the field of ophthalmopathy. Considering that many ophthalmic drugs lead to systemic side effects, CGA may act as a natural exogenous antioxidant for patients to take regularly, controlling their condition while minimizing side effects. In this paper, in vitro and in vivo studies of CGA in the treatment of age-related eye diseases are reviewed, and the prospects of CGA's antioxidant application for the eye are discussed. The aim of this review is to summarize the relevant knowledge and provide theoretical support for future research., (© 2024 The Authors. Pharmacology Research & Perspectives published by British Pharmacological Society and American Society for Pharmacology and Experimental Therapeutics and John Wiley & Sons Ltd.)

Diabetic retinopathy (DR) is a major microvascular complication of diabetes, affecting a substantial portion of diabetic patients worldwide. Timely intervention is pivotal in mitigating the risk of blindness associated with DR, yet early detection remains a challenge due to the absence of early symptoms. Screening programs have emerged as a strategy to address this burden, and this paper delves into the role of artificial intelligence (AI) in advancing DR screening in Japan. There are two pathways for DR screening in Japan: a health screening pathway and a clinical referral path from physicians to ophthalmologists. AI technologies that realize automated image classification by applying deep learning are emerging. These technologies have exhibited substantial promise, achieving sensitivity and specificity levels exceeding 90% in prospective studies. Moreover, we introduce the potential of Generative AI and large language models (LLMs) to transform healthcare delivery, particularly in patient engagement, medical records, and decision support. Considering the use of AI in DR screening in Japan, we propose to follow a seven-step framework for systematic screening and emphasize the importance of integrating AI into a well-designed screening program. Automated scoring systems with AI enhance screening quality, but their effectiveness depends on their integration into the broader screening ecosystem. LLMs emerge as an important tool to fill gaps in the screening process, from personalized invitations to reporting results, facilitating a seamless and efficient system. However, it is essential to address concerns surrounding technical accuracy and governance before full-scale integration into the healthcare system. In conclusion, this review highlights the challenges in the current screening pathway and the potential for AI, particularly LLM, to revolutionize DR screening in Japan. The future direction will depend on leadership from ophthalmologists and stakeholders to address long-standing challenges in DR screening so that all people have access to accessible and effective screening.

Objective: To innovatively evaluate the impact of renal impairment in young work age patients with proliferative diabetic retinopathy (PDR) on their visuality after vitrectomy., Methods: To find out whether it is possible to better predict the improvement of visual acuity after vitrectomy in working-age people without adding additional preoperative testing. In view of the fact that diabetic retinopathy and diabetic nephropathy are common diabetic complications of microvascular damage, it is considered whether preoperative renal function can be used as this evaluation index. This paper studies the design under this theme. This retrospective study included 306 patients (306 eyes) diagnosed with PDR and undergoing vitrectomy in our hospital from January 2016 to June 2023. Relevant baseline data were collected, including age, history of kidney disease and clinical laboratory test results. According to the International Standard Logarithmic Visual Acuity Checklist, the best corrected visual acuity was tested on the first day of admission and one month after surgery, and the difference between the two was subtracted. A difference >0 was defined as "vision improved". Patients were classified as vision-improved group (n=245) and non-improved group (n=61). The differences in baseline serum urea nitrogen, creatinine, uric acid, Cystatin C, estimated glomerular filtration rate (eGFR) and urine protein distribution between the two groups were statistically analyzed, binary regression analysis was performed for meaningful parameters, and random forest model ranked the characteristics in importance., Results: 1.A higher level of serum cystatin C [1.02(0.80,1.48) mg/L vs 0.86(0.67,1.12) mg/L, P<0.001] and a lower eGFR [82.3(50.33, 115.11) ml/(min/1.73m²) vs 107.69(73.9, 126.01) ml/(min/1.73m²), P=0.002] appeared in the non-(vision-)improved group compared with the vision-improved group. 2. The occurrence of preoperation proteinuria history of nephropathy take a larger proportion in non-improved group. 3. Univariate regression analysis showed history of nephrology (OR=1.887, P=0.028), preoperative serum urea nitrogen (OR=0.939, P=0.043), cystatin C (Cys-C) concentration (OR=0.75, P=0.024), eGFR (OR=1.011, P=0.003) and proteinuria (OR=3.128, P<0.001) were influencing factors to postoperative visual acuity loss in young working age PDR patients. Excluding other confounding factors, preoperative proteinuria is an independent risk factor for postoperative vision improvement in working-age PDR populations (OR=2.722, P=0.009). 4. The accuracy of the prediction random forst model is 0.81. The model appears to be superior in terms of positive prediction., Conclusion: In young work aged PDR patients undergoing vitrectomy, preoperative urine protein can be an independent indicator of postoperative visual loss. Aggressive correction of kidney injury before surgery may help improve postoperative vision in patients with PDR., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zheng, Feng, Xing, Wang, Zhao and Zhang.)

Image processing with the help of machine learning algorithms is breaking barriers in various fields of study, especially in the medical field. Deep learning image classification algorithms have made disease detection based on images in an easy manner thus assists the medical professional to taking quick decisions. This paper discusses various deep learning algorithms and transfer learning methods used to classify various eye diseases. Kaggle Eye dataset is used for this purpose. We compared four deep learning algorithms, namely EfficientNetB3, Inception V3, VGG 19 and Convolutional Neural Network models. Various categories of eye diseases, namely Cataract, Diabetic Retinopathy, Glaucoma are considered for classification with normal eye with the help of the scanned images. The strengths and weaknesses of these models are compared based on Precision, Recall, Accuracy and F1 score. In an identical testing environment, EfficientNet B3 outperforms the other algorithms and provides better accuracy for the classification of eye diseases. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a major complication of diabetes mellitus and is a leading cause of vision loss globally. A prompt and accurate diagnosis is crucial for ensuring favorable visual outcomes, highlighting the need for increased access to medical care. The recent remarkable advancements in artificial intelligence (AI) have raised high expectations for its role in disease diagnosis and prognosis prediction across various medical fields. In addition to achieving high precision comparable to that of ophthalmologists, AI-based diagnosis of DR has the potential to improve medical accessibility, especially through telemedicine. In this review paper, we aim to examine the current role of AI in the diagnosis of DR and explore future directions. [ABSTRACT FROM AUTHOR]

Simple Summary: Eales' Disease, an ocular condition characterized by unknown pathophysiology, exhibits a higher prevalence among young males. Since the etiology of the disease remains unclear, a comprehensive exploration of clinical presentations and diagnostic approaches is undertaken to deepen insights into its current understanding. Furthermore, potential cofactors that may trigger Eales' Disease are reviewed, investigating the association between Eales' Disease and other ocular conditions such as diabetic retinopathy and glaucoma. Additionally, diverse therapeutic interventions across disease stages are examined to assess the efficacy and possibility of repurposing treatments from related conditions. Through this review, the aim is to extend and elucidate potential study areas to facilitate a comprehensive understanding of Eales' Disease and aid patients in preventing the disease or finding optimal treatment strategies. Eales' Disease is an idiopathic peripheral retinal vasculopathy first described by British ophthalmologist Henry Eales in 1880. Most prevalent in healthy young males, Eales' Disease often presents with symptoms of sudden blurry or decreased vision and floaters. Although no clear, standardized stage of the disease exists, it progresses through three overlapping phases—peripheral periphlebitis, ischemic capillary ischemia, and retinal neovascularization. The etiology of Eales' Disease is unknown and appears to be multifactorial, but post-TB hypersensitivity to tuberculoprotein and M. tuberculosis DNA is the most potential cause in the etiology of Eales' Disease. With a thorough examination of the clinical presentation and diagnosis of Eales' Disease—incorporating the latest clinical findings related to the condition—the investigation for Eales' Disease extends to explore recent potential connections with other ocular conditions or possible cofactors, such as glaucoma, uncontrolled diabetes, drug abuse, or inherited medical conditions. Moreover, focusing on critical insights into the treatment of Eales' Disease across its various stages of progression, the overarching goal of the paper is to refine and suggest possible future diagnostic and therapeutic strategies. Widening our understanding of pathophysiology and utilizing various treatment options for individual patients holds immense potential for advancing ocular medicine and optimizing patient care for people with this disease with unknown pathophysiology. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has emerged as a transformative technology across various fields, and its applications in the medical domain, particularly in ophthalmology, has gained significant attention. The vast amount of high-resolution image data, such as optical coherence tomography (OCT) images, has been a driving force behind AI growth in this field. Age-related macular degeneration (AMD) is one of the leading causes for blindness in the world, affecting approximately 196 million people worldwide in 2020. Multimodal imaging has been for a long time the gold standard for diagnosing patients with AMD, however, currently treatment and follow-up in routine disease management are mainly driven by OCT imaging. AI-based algorithms have by their precision, reproducibility and speed, the potential to reliably quantify biomarkers, predict disease progression and assist treatment decisions in clinical routine as well as academic studies. This review paper aims to provide a summary of the current state of AI in AMD, focusing on its applications, challenges, and prospects. [ABSTRACT FROM AUTHOR]

Melatonin is a ubiquitous molecule found in living organisms, ranging from bacteria to plants and mammals. It possesses various properties, partly due to its robust antioxidant nature and partly owed to its specific interaction with melatonin receptors present in almost all tissues. Melatonin regulates different physiological functions and contributes to the homeostasis of the entire organism. In the human eye, a small amount of melatonin is also present, produced by cells in the anterior segment and the posterior pole, including the retina. In the eye, melatonin may provide antioxidant protection along with regulating physiological functions of ocular tissues, including intraocular pressure (IOP). Therefore, it is conceivable that the exogenous topical administration of sufficiently high amounts of melatonin to the eye could be beneficial in several instances: for the treatment of eye pathologies like glaucoma, due to the IOP-lowering and neuroprotection effects of melatonin; for the prevention of other dysfunctions, such as dry eye and refractive defects (cataract and myopia) mainly due to its antioxidant properties; for diabetic retinopathy due to its metabolic influence and neuroprotective effects; for macular degeneration due to the antioxidant and neuroprotective properties; and for uveitis, mostly owing to anti-inflammatory and immunomodulatory properties. This paper reviews the scientific evidence supporting the use of melatonin in different ocular districts. Moreover, it provides data suggesting that the topical administration of melatonin as eye drops is a real possibility, utilizing nanotechnological formulations that could improve its solubility and permeation through the eye. This way, its distribution and concentration in different ocular tissues may support its pleiotropic therapeutic effects. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has emerged as a transformative tool in the field of ophthalmology, revolutionizing disease diagnosis and management. This paper provides a comprehensive overview of AI applications in various retinal diseases, highlighting its potential to enhance screening efficiency, facilitate early diagnosis, and improve patient outcomes. Herein, we elucidate the fundamental concepts of AI, including machine learning (ML) and deep learning (DL), and their application in ophthalmology, underscoring the significance of AI-driven solutions in addressing the complexity and variability of retinal diseases. Furthermore, we delve into the specific applications of AI in retinal diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), Macular Neovascularization, retinopathy of prematurity (ROP), retinal vein occlusion (RVO), hypertensive retinopathy (HR), Retinitis Pigmentosa, Stargardt disease, best vitelliform macular dystrophy, and sickle cell retinopathy. We focus on the current landscape of AI technologies, including various AI models, their performance metrics, and clinical implications. Furthermore, we aim to address challenges and pitfalls associated with the integration of AI in clinical practice, including the "black box phenomenon", biases in data representation, and limitations in comprehensive patient assessment. In conclusion, this review emphasizes the collaborative role of AI alongside healthcare professionals, advocating for a synergistic approach to healthcare delivery. It highlights the importance of leveraging AI to augment, rather than replace, human expertise, thereby maximizing its potential to revolutionize healthcare delivery, mitigate healthcare disparities, and improve patient outcomes in the evolving landscape of medicine. [ABSTRACT FROM AUTHOR]

Antioxidants, usually administered orally through the systemic route, are known to counteract the harmful effects of oxidative stress on retinal cells. The formulation of these antioxidants as eye drops might offer a new option in the treatment of oxidative retinopathies. In this review, we will focus on the use of some of the most potent antioxidants in treating retinal neuropathies. Melatonin, known for its neuroprotective qualities, may mitigate oxidative damage in the retina. N-acetyl-cysteine (NAC), a precursor to glutathione, enhances the endogenous antioxidant defense system, potentially reducing retinal oxidative stress. Idebenone, a synthetic analogue of coenzyme Q10, and edaravone, a free radical scavenger, contribute to cellular protection against oxidative injury. Epigallocatechin-3-gallate (EGCG), a polyphenol found in green tea, possesses anti-inflammatory and antioxidant effects that could be beneficial in cases of retinopathy. Formulating these antioxidants as eye drops presents a localized and targeted delivery method, ensuring effective concentrations reach the retina. This approach might minimize systemic side effects and enhance therapeutic efficacy. In this paper, we also introduce a relatively new strategy: the alkylation of two antioxidants, namely, edaravone and EGCG, to improve their insertion into the lipid bilayer of liposomes or even directly into cellular membranes, facilitating their crossing of epithelial barriers and targeting the posterior segment of the eye. The synergistic action of these antioxidants may offer a multifaceted defense against oxidative damage, holding potential for the treatment and management of oxidative retinopathies. Further research and clinical trials will be necessary to validate the safety and efficacy of these formulations, but the prospect of antioxidant-based eye drops represents a promising avenue for future ocular therapies. [ABSTRACT FROM AUTHOR]

Worldwide stroke is the second leading cause of death and the third leading cause of death and disability combined. The estimated global economic burden by stroke is over US$891 billion per year. Within three decades (1990–2019), the incidence increased by 70%, deaths by 43%, prevalence by 102%, and DALYs by 143%. Of over 100 million people affected by stroke, about 76% are ischemic stroke (IS) patients recorded worldwide. Contextually, ischemic stroke moves into particular focus of multi-professional groups including researchers, healthcare industry, economists, and policy-makers. Risk factors of ischemic stroke demonstrate sufficient space for cost-effective prevention interventions in primary (suboptimal health) and secondary (clinically manifested collateral disorders contributing to stroke risks) care. These risks are interrelated. For example, sedentary lifestyle and toxic environment both cause mitochondrial stress, systemic low-grade inflammation and accelerated ageing; inflammageing is a low-grade inflammation associated with accelerated ageing and poor stroke outcomes. Stress overload, decreased mitochondrial bioenergetics and hypomagnesaemia are associated with systemic vasospasm and ischemic lesions in heart and brain of all age groups including teenagers. Imbalanced dietary patterns poor in folate but rich in red and processed meat, refined grains, and sugary beverages are associated with hyperhomocysteinaemia, systemic inflammation, small vessel disease, and increased IS risks. Ongoing 3PM research towards vulnerable groups in the population promoted by the European Association for Predictive, Preventive and Personalised Medicine (EPMA) demonstrates promising results for the holistic patient-friendly non-invasive approach utilising tear fluid-based health risk assessment, mitochondria as a vital biosensor and AI-based multi-professional data interpretation as reported here by the EPMA expert group. Collected data demonstrate that IS-relevant risks and corresponding molecular pathways are interrelated. For examples, there is an evident overlap between molecular patterns involved in IS and diabetic retinopathy as an early indicator of IS risk in diabetic patients. Just to exemplify some of them such as the 5-aminolevulinic acid/pathway, which are also characteristic for an altered mitophagy patterns, insomnia, stress regulation and modulation of microbiota-gut-brain crosstalk. Further, ceramides are considered mediators of oxidative stress and inflammation in cardiometabolic disease, negatively affecting mitochondrial respiratory chain function and fission/fusion activity, altered sleep–wake behaviour, vascular stiffness and remodelling. Xanthine/pathway regulation is involved in mitochondrial homeostasis and stress-driven anxiety-like behaviour as well as molecular mechanisms of arterial stiffness. In order to assess individual health risks, an application of machine learning (AI tool) is essential for an accurate data interpretation performed by the multiparametric analysis. Aspects presented in the paper include the needs of young populations and elderly, personalised risk assessment in primary and secondary care, cost-efficacy, application of innovative technologies and screening programmes, advanced education measures for professionals and general population—all are essential pillars for the paradigm change from reactive medical services to 3PM in the overall IS management promoted by the EPMA. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy (VTDR), which is a leading cause of visual impairment and blindness worldwide. However, previous automated VTDR detection methods have mainly relied on manual feature extraction and classification, leading to errors. This paper proposes a novel VTDR detection and classification model that combines different models through majority voting. Our proposed methodology involves preprocessing, data augmentation, feature extraction, and classification stages. We use a hybrid convolutional neural network-singular value decomposition (CNN-SVD) model for feature extraction and selection and an improved SVM-RBF with a Decision Tree (DT) and K-Nearest Neighbor (KNN) for classification. We tested ourmodel on the IDRiD dataset and achieved an accuracy of 98.06%, a sensitivity of 83.67%, and a specificity of 100% for DR detection and evaluation tests, respectively. Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection. [ABSTRACT FROM AUTHOR]

Diagnosing and screening for diabetic retinopathy is a well-known issue in the biomedical field. A component of computer-aided diagnosis that has advanced significantly over the past few years as a result of the development and effectiveness of deep learning is the use of medical imagery from a patient's eye to identify the damage caused to blood vessels. Issues with unbalanced datasets, incorrect annotations, a lack of sample images, and improper performance evaluation measures have negatively impacted the performance of deep learning models. Using three benchmark datasets of diabetic retinopathy, we conducted a detailed comparison study comparing various state-of-the-art approaches to address the effect caused by class imbalance, with precision scores of 93%, 89%, 81%, 76%, and 96%, respectively, for normal, mild, moderate, severe, and DR phases. The analyses of the hybrid modeling, including CNN analysis and SHAP model derivation results, are compared at the end of the paper, and ideal hybrid modeling strategies for deep learning classification models for automated DR detection are identified. [ABSTRACT FROM AUTHOR]

Objective: To innovatively evaluate the impact of renal impairment in young work age patients with proliferative diabetic retinopathy (PDR) on their visuality after vitrectomy. Methods: To find out whether it is possible to better predict the improvement of visual acuity after vitrectomy in working-age people without adding additional preoperative testing. In view of the fact that diabetic retinopathy and diabetic nephropathy are common diabetic complications of microvascular damage, it is considered whether preoperative renal function can be used as this evaluation index. This paper studies the design under this theme. This retrospective study included 306 patients (306 eyes) diagnosed with PDR and undergoing vitrectomy in our hospital from January 2016 to June 2023. Relevant baseline data were collected, including age, history of kidney disease and clinical laboratory test results. According to the International Standard Logarithmic Visual Acuity Checklist, the best corrected visual acuity was tested on the first day of admission and one month after surgery, and the difference between the two was subtracted. A difference >0 was defined as "vision improved". Patients were classified as vision-improved group (n=245) and non-improved group (n=61). The differences in baseline serum urea nitrogen, creatinine, uric acid, Cystatin C, estimated glomerular filtration rate (eGFR) and urine protein distribution between the two groups were statistically analyzed, binary regression analysis was performed for meaningful parameters, and random forest model ranked the characteristics in importance. Results: 1.A higher level of serum cystatin C [1.02(0.80,1.48) mg/L vs 0.86 (0.67,1.12) mg/L, P<0.001] and a lower eGFR [82.3(50.33, 115.11) ml/(min/1.73m²) vs 107.69(73.9, 126.01) ml/(min/1.73m²), P=0.002] appeared in the non-(vision-)improved group compared with the vision-improved group. 2. The occurrence of preoperation proteinuria history of nephropathy take a larger proportion in non-improved group. 3. Univariate regression analysis showed history of nephrology (OR=1.887, P=0.028), preoperative serum urea nitrogen (OR=0.939, P=0.043), cystatin C (Cys-C) concentration (OR=0.75, P=0.024), eGFR (OR=1.011, P=0.003) and proteinuria (OR=3.128, P<0.001) were influencing factors to postoperative visual acuity loss in young working age PDR patients. Excluding other confounding factors, preoperative proteinuria is an independent risk factor for postoperative vision improvement in working-age PDR populations (OR=2.722, P=0.009). 4. The accuracy of the prediction random forst model is 0.81. The model appears to be superior in terms of positive prediction. Conclusion: In young work aged PDR patients undergoing vitrectomy, preoperative urine protein can be an independent indicator of postoperative visual loss. Aggressive correction of kidney injury before surgery may help improve postoperative vision in patients with PDR. [ABSTRACT FROM AUTHOR]

The cellular-level visualization of retinal microstructures such as blood vessel wall components, not available with other imaging modalities, is provided with unprecedented details by dark-field imaging configurations; however, the interpretation of such images alone is sometimes difficult since multiple structural disturbances may be present in the same time. Particularly in eyes with retinal pathology, microstructures may appear in high-resolution retinal images with a wide range of sizes, sharpnesses, and brightnesses. In this paper we show that motion contrast and phase gradient imaging modalities, as well as the simultaneous acquisition of depth-resolved optical coherence tomography (OCT) images, provide additional insight to help understand the retinal neural and vascular structures seen in dark-field images and may enable improved diagnostic and treatment plans. [ABSTRACT FROM AUTHOR]

A retina disease caused by high glucose levels in the blood is called Diabetic Retinopathy (DR) and is the world's leading cause of blindness. To avoid or delay vision degradation and loss, early diagnosis and treatment are required. As a result, the creation of an automated method for accurate DR identification is essential. For this, in this paper, a 3D-Convolution Neural Network (3D-CNN) with Deer Hunting Optimization (DHO) algorithm is proposed for detecting and classifying DR images. The proposed 3D-CNN-DHO approach includes four phases such as pre-processing, segmentation, feature extraction, and classification. The contrast of the DR image is first improved using a Contrast-Limited Adaptive Histogram Equalization (CLAHE) approach. Subsequently, the threshold-based effective segmentation is carried out. Then, the Resnet50 model is implemented to extract the features from the image. Finally, 3D-CNN-DHO-based classifier model is implemented to categorize the various DR stages. The experiments are carried out in detail and evaluated on the Messidor DR benchmark dataset. The acquired experimental result demonstrated the 3D-CNN-DHO model's outstanding qualities by achieving optimal specificity, sensitivity, recall, precision, and accuracy. [ABSTRACT FROM AUTHOR]

Ultra-wide-field fundus imaging (UFI) provides comprehensive visualization of crucial eye components, including the optic disk, fovea, and macula. This in-depth view facilitates doctors in accurately diagnosing diseases and recommending suitable treatments. This study investigated the application of various deep learning models for detecting eye diseases using UFI. We developed an automated system that processes and enhances a dataset of 4697 images. Our approach involves brightness and contrast enhancement, followed by applying feature extraction, data augmentation and image classification, integrated with convolutional neural networks. These networks utilize layer-wise feature extraction and transfer learning from pre-trained models to accurately represent and analyze medical images. Among the five evaluated models, including ResNet152, Vision Transformer, InceptionResNetV2, RegNet and ConVNext, ResNet152 is the most effective, achieving a testing area under the curve (AUC) score of 96.47% (with a 95% confidence interval (CI) of 0.931–0.974). Additionally, the paper presents visualizations of the model's predictions, including confidence scores and heatmaps that highlight the model's focal points—particularly where lesions due to damage are evident. By streamlining the diagnosis process and providing intricate prediction details without human intervention, our system serves as a pivotal tool for ophthalmologists. This research underscores the compatibility and potential of utilizing ultra-wide-field images in conjunction with deep learning. [ABSTRACT FROM AUTHOR]