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Researchers at Tianjin Medical University General Hospital have identified a critical link between protein O-GlcNAcylation and Hippo signaling in driving vascular dysfunction in diabetic retinopathy. This study highlights the role of disturbed metabolic homeostasis in contributing to diabetic vascular complications, particularly diabetic retinopathy, which is a leading cause of blindness in the working-age population. The findings suggest that targeting the O-GlcNAc-Hippo axis could be a potential therapeutic strategy for treating diabetic retinopathy. [Extracted from the article]

A study conducted by Shahid Beheshti University of Medical Sciences in Iran aimed to assess the knowledge of eye diseases among the Iranian population in the Gilan province. The research found varying levels of awareness among participants, with lower socioeconomic status and education correlating with lower knowledge levels. Participants with better vision were more likely to have higher levels of knowledge about eye conditions. The study highlights the importance of education and access to information in promoting awareness of eye health issues. [Extracted from the article]

Researchers at the Moscow Regional Research and Clinical Institute have conducted a study on diabetic retinopathy, a common microvascular complication of diabetes. The study focused on changes in the vascular membrane of the eye in pregnant women with diabetes to predict the risk of diabetic retinopathy progression. By analyzing the thickness of the vascular layer of the ciliary body during pregnancy, researchers were able to predict the progression of diabetic retinopathy to the next stage. This research provides valuable insights into the early detection and management of diabetic retinopathy in pregnant women with diabetes. [Extracted from the article]

A recent report from Taipei Medical University discusses the impact of methylglyoxal on diabetic retinopathy, a common retinal vascular disease that can lead to vision loss. The research highlights how elevated glucose levels in diabetes can increase the formation of methylglyoxal, which can cause oxidative stress and damage to retinal cells. The study also explores potential pathways for detoxifying methylglyoxal to mitigate its harmful effects. For more information, the full article is available in Biomedicines, and the researchers involved can be contacted for further details. [Extracted from the article]

A recent study conducted at the University of Porto explored the feasibility of opportunistic diabetic retinopathy screening using handheld fundus cameras in primary care settings. Doctors and nurses were surveyed, with 87% believing in the clinical utility of these cameras for increasing access to retinopathy screening. The main barriers identified included limited time, equipment cost, and lack of skills in retinal image acquisition. The research concluded that while healthcare professionals support the use of handheld fundus cameras, strategies such as training and telemedicine-based diagnosis are needed for optimal implementation. [Extracted from the article]

A study conducted at Al-Shifa Trust Eye Hospital in Rawalpindi, Pakistan, compared the hexagonality of endothelial cells in individuals with type 2 diabetes and those without diabetes. The research found that the hexagonality of endothelial cells was significantly lower in type 2 diabetics compared to nondiabetics. The study concluded that there was a notable difference in the hexagonality of endothelial cells between the two groups. [Extracted from the article]

Researchers at Thapar Institute of Engineering & Technology in Patiala, India, have developed a new method for segmenting retinal blood vessels in fundus imaging using deep learning. The study focused on diabetic retinopathy, a common complication of diabetes that affects the eyes. The proposed method achieved high accuracy rates on different datasets, demonstrating its efficacy in detecting retinal vessels even in the presence of various pathologies and vasculatures. This research has been peer-reviewed and offers valuable insights into improving early diagnosis and treatment of diabetic retinopathy. [Extracted from the article]

Researchers at the Shanghai University of Traditional Chinese Medicine have conducted a study on the effects of chlorogenic acid in alleviating non-proliferative diabetic retinopathy by targeting TNFR1 in retinal endothelial cells. The study found that chlorogenic acid reduced retinal inflammation and leukocyte adhesion, ultimately improving blood-retinal barrier function in diabetic mice. This research, funded by various organizations, provides insights into the potential therapeutic benefits of chlorogenic acid in diabetic retinopathy. [Extracted from the article]

Researchers from the Medical University of Warsaw have published a study on diabetic retinopathy, highlighting pregnancy as a significant risk factor for its progression. The study emphasizes the challenges faced by doctors in managing pregnant women, as systemic changes during pregnancy can impact the development of diabetic retinopathy. The research also discusses management strategies and guidelines for diabetic retinopathy in pregnancy. For more information, the full article can be accessed through the Journal of Education, Health and Sport. [Extracted from the article]

A recent study conducted at the City University of Macau in the People's Republic of China focused on diabetic retinopathy, a common complication of diabetes that can lead to vision loss. The research highlighted the importance of early detection and treatment to prevent vision impairment. The study utilized deep learning techniques to address challenges such as unbalanced datasets and sparse focal areas, ultimately proposing innovative methods to improve the accuracy and effectiveness of diagnosing diabetic retinopathy grades. [Extracted from the article]

Researchers at Imam Abdulrahman Bin Faisal University in Dammam, Saudi Arabia, have developed a hybrid intelligent approach using artificial intelligence technology to detect diabetic retinopathy (DR) in retinal fundus images. The study employed machine learning models such as support vector machine (SVM), k-nearest neighbors (KNN), and Histogram Gradient Boosting (HGB) to achieve a high accuracy rate of 96.9% in classifying DR. This approach offers a faster and more accurate method for early detection of DR, potentially preventing complete blindness or partial vision loss. The research has been peer-reviewed and provides a valuable pathway for clinical decision support systems in the field of ophthalmology. [Extracted from the article]

Researchers at Johns Hopkins University conducted a study to compare different methods for identifying diabetic retinopathy (DR) and related conditions using natural language processing (NLP) techniques. The study included 91,097 patients and 692,486 office visits, with the Text-and-ICD NLP System showing the highest performance in identifying clinical conditions. The prevalence of DR and related conditions varied depending on the identification methodology, with the Text-and-ICD NLP System being the most effective. The study was supported by Research To Prevent Blindness and the National Eye Institute. [Extracted from the article]

Researchers at the University of Malaya in Kuala Lumpur, Malaysia, have developed a privacy-preserving approach using deep learning models for diagnosing diabetic retinopathy (DR). Diabetic retinopathy is a common complication of diabetes that can lead to blindness if not detected early. The study explores the use of artificial intelligence (AI) to predict DR risks while addressing privacy concerns related to healthcare data. The findings suggest that privacy-preserving methods, such as encryption, do not significantly impact the performance of the AI models in predicting DR risks. [Extracted from the article]

Researchers at the Second Xiangya Hospital of Central South University in China have detailed the progress and prospects of using artificial intelligence (AI) in diagnosing and treating diabetic retinopathy (DR). The integration of AI, particularly deep learning models, has significantly improved early DR detection and may soon be used in telemedicine to address medical resource shortages and health inequities. The study highlights key AI models for DR diagnosis and treatment, addressing challenges such as bias, transparency, and ethics, and offers a prospective outlook on future developments in this field. [Extracted from the article]

Researchers from the University of Medicine and Pharmacy Timisoara in Romania conducted a study on 377 patients with type 2 diabetes mellitus to assess cardiovascular risk factors as independent predictors of diabetic retinopathy (DR) progression. The study found that systolic blood pressure, LDL cholesterol, and body mass index (BMI) were significant predictors of DR progression. Machine learning models, particularly XGBoost, showed moderate accuracy in identifying DR stages, suggesting that integrating these risk factors into clinical practice may improve early detection and intervention strategies for DR. The study was published in Medicina and can be accessed for free online. [Extracted from the article]

University of California researchers conducted a study comparing the use of a rule-based natural language processing (NLP) algorithm with International Classification of Diseases (ICD) codes for classifying diabetic retinopathy severity. The study found that the NLP algorithm performed better in classifying proliferative diabetic retinopathy (PDR) compared to ICD codes, with higher sensitivity and similar positive predictive value. The research highlights the potential of NLP algorithms in improving the efficiency and accuracy of cohort selection for research on diabetic retinopathy. [Extracted from the article]

A study conducted at Anhui University of Chinese Medicine focused on developing puerarin-loaded poly(lactic acid) microspheres for sustained ocular delivery to treat diabetic retinopathy, a common complication of diabetes leading to blindness. The research optimized the preparation of these microspheres and found that intravitreal injection of the microspheres significantly prolonged the half-life of puerarin in eye tissues, achieving sustained drug release. The study suggests that this method has positive implications for the treatment of diabetic retinopathy. [Extracted from the article]

A study conducted at Nitte (Deemed to be University) introduced a new Modified Regularisation Long Short-term Memory (MR-LSTM) framework for the diagnosis of Diabetic Retinopathy (DR). This framework utilizes deep learning to provide an accurate and efficient solution for early detection of DR, achieving an accuracy of 97.12 percent and an F1 Score of 98.49. The research aims to revolutionize DR diagnosis by offering timely and accessible solutions to combat vision-threatening conditions. [Extracted from the article]

University of Michigan researchers conducted a study on diabetic retinopathy, focusing on proliferative diabetic retinopathy (PDR) and its inflammatory pathways. The research involved single-cell transcriptomics of vitreous cells from patients with PDR, revealing a predominance of T cells and distinct immune cell proportions compared to peripheral blood. This study highlights the potential of liquid vitreous biopsies to provide insights into vitreoretinal diseases. [Extracted from the article]

The research conducted at the Indian Institute of Science in Karnataka, India, focuses on automated grading of Diabetic Retinopathy using Ultra-wide Optical Coherence Tomography Angiography scans. The study aims to develop a method for accurately grading the severity of Diabetic Retinopathy by analyzing vascular abnormalities on retinal images. The research findings suggest that radiomics analysis could be used for multimodal grading and further analysis of Diabetic Retinopathy, indicating its potential scope in this area. The study was funded by various organizations including the Wellcome Trust and the Science and Engineering Research Board in India. [Extracted from the article]

A study conducted at Hebrew University of Jerusalem examined the effects of glucagon-like-peptide-1 receptor agonists (GLP1-RAs) on diabetic retinopathy (DR) progression, visual acuity, central subfield thickness, and response to intravitreal injections (IVIs) in a cohort of patients. The study found that patients treated with GLP1-RA did not show significant differences in DR progression, visual acuity, or central subfield thickness compared to the control group. However, GLP1-RA therapy was not significantly associated with improved IVI response, with ocular parameters acting as confounding factors. [Extracted from the article]

A recent study conducted in Varna, Bulgaria, aimed to update data on the prevalence of Diabetic Retinopathy (DR) among diabetic patients in Northeastern Bulgaria. The study found that the overall prevalence of any DR was 39.9%, with higher rates in Type 1 diabetes patients compared to Type 2. The research highlights the importance of comprehensive national DR screening programs and proactive measures to prevent visual impairment and blindness. The findings provide valuable epidemiological insights for future research and emphasize the need for widespread screening, even in rural areas. [Extracted from the article]

Researchers from Symbiosis International (Deemed University) in Pune, India, have developed a new approach called Dunnock-Scheduler optimization-based Light GBM (DkSO-Light GBM) for the detection of Diabetic Retinopathy (DR) using multimodal image fusion. This method combines Optical Coherence Tomography Angiography (OCTA) images with fundus images to classify normal and abnormal retinas with high accuracy. The experimental outcomes show that the DkSO-Light GBM model outperforms conventional techniques in terms of accuracy, sensitivity, specificity, and other metrics, making it a promising tool for automated DR diagnosis. [Extracted from the article]

Researchers at the University of Johannesburg have developed a transparent diagnosis model for diabetic retinopathy using Explainable AI (XAI) to address the challenges of diverse symptoms and diagnostic difficulties associated with the disease. The XAI approach enhances interpretability of models, allowing clinicians to understand the reasoning behind decisions and improve diagnostic accuracy. The proposed model achieved a 94% better diagnostic accuracy than traditional methods, indicating potential for improved healthcare settings. The study was supported by the Research Fund from the University of Johannesburg and involved researchers from diverse backgrounds. [Extracted from the article]

A recent study conducted at Government Medical College focused on assessing the quality of life in patients with diabetic retinopathy after treatment with Anti-VEGF and laser photocoagulation. The study found that patients treated with Anti-VEGF therapy experienced better outcomes in certain quality of life domains compared to those treated with photocoagulation therapy. The research suggests that Anti-VEGF therapy may be a preferred treatment option for patients with diabetic retinopathy, especially those with diabetic macular edema requiring intervention. For more information, readers can refer to the International Journal of Scientific Research. [Extracted from the article]

Researchers from Kuwait University conducted a study on the effects of garlic extract on diabetic retinopathy in rats with streptozotocin-induced diabetes. The study found that rats treated with garlic extract showed improvements in retinal thickness, vascular density, artery diameter, and blood vessel numbers compared to untreated diabetic rats. The research suggests that garlic extract may be a beneficial dietary supplement for diabetic patients to manage and delay the progression of diabetic retinopathy. The study was published in the Journal of Functional Foods in 2024. [Extracted from the article]

The article discusses the impact of diabetes on eye health, specifically diabetic retinopathy, which can lead to blindness. The research highlights the increasing prevalence of diabetic retinopathy globally and the burden it places on medical services due to the long-term care required for affected patients. The study emphasizes the importance of early detection and management of diabetic retinopathy to prevent vision loss. [Extracted from the article]

A recent study conducted at the Second Hospital of Shandong University in China focused on identifying biomarkers related to neutrophil extracellular traps (NETs) in diabetic retinopathy (DR). The research found that five key genes, including CLIC3, GBP2, and P2RY12, were associated with DR, with HOXA1 and PSAP acting as protective factors. These findings offer new insights for clinical diagnosis and potential treatment strategies for DR. For more information, the full article can be accessed in Frontiers in Immunology. [Extracted from the article]

A recent report from the First Affiliated Hospital of Henan University of Science and Technology in China explores the predictive value of combining the serum TyG index, nesfatin-1, and retinol-binding protein 4 for diabetic retinopathy (DR) in patients with type 2 diabetes mellitus. The study found that the TyG index, nesfatin-1, and RBP4 levels were early predictive markers for DR and were associated with the risk and severity of the disease. The combination of these markers showed better predictive performance for DR, indicating their potential utility in early detection and management of DR in diabetic patients. [Extracted from the article]

Researchers at Hainan Medical University conducted a retrospective cohort study to investigate the impact of absolute changes in glycated hemoglobin A1c (HbA1c) on the risk of diabetic retinopathy (DR) in individuals with type 2 diabetes (T2D). The study involved 3,767 people with T2D without DR, and participants were grouped into four categories based on HbA1c absolute changes. The findings suggested that while HbA1c variability is a risk factor for DR, a significant reduction in HbA1c may be protective for individuals with very high baseline HbA1c. The research was funded by the National Natural Science Foundation of China and has been peer-reviewed. [Extracted from the article]

Researchers at Southwest State University conducted a study on the relationship between biological age and diabetic retinopathy in patients with type 2 diabetes mellitus. The study involved 580 patients aged 45-59 years, revealing that an accelerated biological age is a significant risk factor for diabetic retinopathy in this demographic. The findings suggest that biological age should be considered alongside other risk factors for diabetic retinopathy in patients with type 2 diabetes mellitus. [Extracted from the article]

A recent study conducted by researchers at the First Affiliated Hospital in Ningbo, China, explored the relationship between controlling nutritional status (CONUT) score and the risk of developing diabetic retinopathy (DR) in patients with diabetes. The study, which included 5,256 participants, found that higher CONUT scores were associated with an increased risk of DR development and all-cause mortality in patients with DR. While the study suggests that CONUT score may be a useful tool for assessing DR risk and predicting patient outcomes, it is important to note that the study was cross-sectional, and causality cannot be inferred. [Extracted from the article]

Researchers from the Harbin Institute of Technology in Shenzhen, China, have developed a deep convolutional neural network for detecting diabetic retinopathy, a common complication of diabetes that can lead to irreversible blindness. The network incorporates long-range dependence between lesion features in retinal fundus photographs to improve detection accuracy. Experimental results show that this approach outperforms existing models on datasets like Messidor and EyePACS. This study highlights the potential of machine learning and emerging technologies in the field of ophthalmology to enhance healthcare outcomes for individuals with diabetic retinopathy. [Extracted from the article]

Researchers at the University of Electronic Science and Technology of China conducted a study on the anti-ferroptotic effect of resveratrol on retinal Müller cells in the early stages of diabetic retinopathy. The research involved experiments on rats and mice treated with resveratrol, measuring changes in retinal neurofunction and cell viability. The study concluded that resveratrol could be a potential therapeutic option for diabetic retinopathy by regulating the Nrf2/GPx4/PTGS2 pathway. The findings were published in Molecular Neurobiology and have been peer-reviewed. [Extracted from the article]

A study conducted by University College London (UCL) evaluated the performance of a deep learning system (DLS) for detecting diabetic retinopathy (DR) in Indigenous Australians, a group that experiences a high burden of DR-related blindness. The DLS showed improved sensitivity and similar specificity compared to a retina specialist for DR detection, indicating its potential to support DR screening in this underserved population. Funders for this research included Google Incorporated, General Sir John Monash Scholarship, Moorfields Eye Charity Career Development Award, UK Research & Innovation (UKRI). [Extracted from the article]

A patent application titled "Detecting Ocular Comorbidities When Screening For Diabetic Retinopathy (Dr) Using 7-Field Color Fundus Photos" was published online by inventors BENMANSOUR, Fethallah; CAVALCANTI, Daniela Ferrara; DAI, Jian; NOVOSEL, Jelena. The application aims to provide methods and systems for detecting comorbid ocular conditions in subjects with diabetic retinopathy using deep learning models and imaging data. The application highlights the importance of accurate and efficient screening for ocular comorbidities such as glaucoma, age-related macular degeneration, and macular edema in diabetic patients to prevent severe vision loss and blindness. The patent application was filed on June 14, 2024, and made available online on October 10, 2024. [Extracted from the article]

A study conducted by Shanghai Jiao Tong University School of Medicine explored the impact of axial length (AL) on vision-threatening diabetic retinopathy (VTDR) in patients of different ages. The research found that younger patients with VTDR had longer AL but exhibited more aggressive clinical signs and compromised prognosis compared to elderly patients. In the elderly group, a longer AL was independently associated with a reduced risk of tractional retinal detachment (TRD), while this protective effect was not observed in younger patients. The study included 736 patients and was published in the International Journal of Retina and Vitreous. [Extracted from the article]

A recent study from Emory University examined the factors influencing the receipt of standard care and progression to vision-threatening diabetic retinopathy among patients with diabetes in the U.S. The research found that urban residence, race and ethnicity, and glycemic control were associated with both receiving guideline-recommended care and progression to vision-threatening diabetic retinopathy. Racial and ethnic minority individuals were more likely to progress to vision-threatening diabetic retinopathy, while those with poor or unknown glycemic control were less likely to receive standard care. The study highlights the importance of understanding the impact of geography, race, and glycemia on the management and progression of diabetic retinopathy. [Extracted from the article]

A recent study conducted at King Khalid University in Saudi Arabia focused on the barriers to adherence for diabetic retinopathy screening among Saudi adults. The research highlighted the challenges faced by individuals in regularly undergoing screenings for this condition, which can lead to blindness. Findings indicated that knowledge about diabetic retinopathy significantly influenced the likelihood of individuals getting screened, with those informed about the condition being more likely to undergo screening compared to those who were not aware. The study emphasized the importance of addressing these barriers within the healthcare system to improve screening programs and overall health outcomes for individuals in Saudi Arabia. [Extracted from the article]

Researchers at the Second Hospital of Jilin University conducted a study on the relationship between weight-adjusted-waist index (WWI), body mass index (BMI), and diabetic retinopathy (DR) among American adults aged 40 and above. The study, based on data from the National Health and Nutrition Examination Survey (2005-2008), found a significant increase in DR prevalence with higher WWI and BMI values. The findings suggest that both WWI and BMI are independently associated with DR risk in older US adults, emphasizing the importance of central obesity measures in assessing diabetic complications. [Extracted from the article]

A study conducted at Hallym University Sacred Heart Hospital explored microvascular changes in diabetic patients undergoing cataract surgery. The research found that phacoemulsification reduced the foveal avascular zone area in the superficial capillary plexus and increased vessel density in the radial peripapillary capillary plexus in diabetic patients without retinopathy six months post-surgery. This study provides new insights into diabetic retinopathy and its impact on optical coherence tomography angiography parameters after cataract surgery. [Extracted from the article]

A study conducted by Kyushu University in Fukuoka, Japan, explored the early pathogenesis of diabetic retinopathy (DR) and the potential protective effects of sodium-glucose cotransporter 2 (SGLT2) inhibitors. The research found that SGLT2 inhibitors could prevent early pathogenic mechanisms of DR, including retinal vascular leakage and morphological changes in microglia. This study sheds light on potential treatments for DR and was funded by various organizations, including the Japan Society for the Promotion of Science. [Extracted from the article]

A recent report from Istanbul Aydin University discusses the use of deep learning methods for detecting diabetic retinopathy, a serious complication of diabetes that can lead to vision loss. The researchers introduced an innovative technique that utilizes algorithms to automatically identify diabetic retinopathy, allowing for early diagnosis and intervention. By implementing various pre-processing techniques and evaluating their model on a dataset, the researchers demonstrated improved accuracy in detecting diabetic retinopathy, making a significant contribution to the field of medical image analysis. For more information, the full article can be accessed for free online. [Extracted from the article]

A recent study conducted at the University of California San Diego (UCSD) reviewed the use of subthreshold micropulse laser therapy in treating diabetic retinopathy (DR). The research found that this laser treatment approach is safe and effective, showing additional benefits compared to current pharmacotherapy options. Ongoing investigations aim to further optimize the therapeutic advantages of this technology. For more information, the full article can be accessed for free at https://doi.org/10.1055/a-2418-5173. [Extracted from the article]

A study conducted at the Catholic University of Korea focused on using deep learning models to automatically segment important features of ultra-widefield fluorescein angiography images in patients with diabetic retinopathy. The research aimed to detect retinal capillary non-perfusion and neovascularization with good performance, aiding physicians in diabetic retinopathy clinics. The study concluded that automated segmentation of these features can help overcome grader subjectivity in diagnosing and treating diabetic retinopathy. The research was peer-reviewed and published in the British Journal of Ophthalmology. [Extracted from the article]

Researchers at the Cleveland Clinic conducted a study to investigate the relationship between lipid-lowering agents (LLA) and the progression of diabetic retinopathy (DR) in adults with type 2 diabetes mellitus. The study found that patients who received statins had a reduced rate of progression to proliferative DR, while those who received PCSK9 inhibitors had a reduced risk of progressing to other secondary complications of DR. The findings suggest that the use of statins and PCSK9 inhibitors may be associated with a reduced risk of adverse progression of DR. [Extracted from the article]