Diabetic Retinopathy Disease Research Articles

GDF11 protects against glucotoxicity-induced mice retinal microvascular endothelial cell dysfunction and diabetic retinopathy disease

Growth differentiation factor 11 (GDF11) has been implicated in the regulation of embryonic development and age-related dysfunction, including the regulation of retinal progenitor cells. However, little is known about the functions of GDF11 in diabetic retinopathy. In this study, we demonstrated that GDF11 treatment improved diabetes-induced retinal cell death, capillary degeneration, pericyte loss, inflammation, and blood-retinal barrier breakdown in mice. Treatment of isolated mouse retinal microvascular endothelial cells with recombinant GDF11 in vitro attenuated glucotoxicity-induced retinal endothelial apoptosis and the inflammatory response. The protective mechanisms exerted are associated with TGF-β/Smad2, PI3k-Akt-FoxO1 activation，and NF-κB pathway inhibition. This study indicated that GDF11 is a novel therapeutic target for diabetic retinopathy.

The Association of Diabetic Retinopathy and Cardiovascular Disease: A 13-Year Nationwide Population-Based Cohort Study.

Objectives: Previous studies have demonstrated that patients with diabetic retinopathy (DR) have a higher prevalence of risk factors known to be associated with cardiovascular disease (CVD). We hypothesized that patients with more severe DR could have a higher relative risk of CVD. Methods: To test this hypothesis, we used the National Health Insurance Research Database (NHIRD) to evaluate whether associations exist between DR and CVD. The data for this nationwide population-based retrospective cohort study were obtained from the NHIRD in Taiwan from 2001 to 2013. The assessed study outcome used was the incidence and other statistical analyses of CVD in patients with DR during a 13-year follow-up period. Results: Our findings obtained from 2001 to 2013 suggest that the incidence rates of CVD are 2.026 times that of diabetes mellitus (DM) without DR (95% C.I. = 1.876–2.187) and 2.75 times that of DM with DR (95% C.I. = 2.487–3.04) compared with the Non-DM group. Conclusion: The relative risk of CVD in DR was greater than that in the Non-DM group for both men and women. Targeted monitoring of DM, especially the co-existence of diabetic retinopathy, is of utmost importance in the clinical care of the DM population.

Complications and its association with sleep quality in the elderly diabetic patients.

Background: Sleep disorders are commonly seen in type 2 diabetic patients. However, there is not much research about the association of diabetic complications and poor sleep quality. Objectives: To evaluate diabetic complications and to find out which diabetic complications are associated with poor sleep quality in older diabetic patients treated at National Geriatric Hospital. Methods: A cross-sectional study on 178 older diabetic patients treated at National Geriatric Hospital from March to October, 2020. Quality of sleep was assessed using the Pittsburgh Sleep Quality Index. Results: 93 patients (52.2%) had peripheral nerve complications. 50% patients had diabetic retinopathy. There were 86 patients (48.3%) had experienced hypoglycemia in the last 3 months. The prevalence of diabetic neuropathy, cerebral vascular disease, cardiovascular disease, peripheral artery disease and foot complication were 12.9%; 12.9%; 9%; 8.4% and 7.3%, respectively. 19.1% patients didn’t have any complications of diabetes. Patients with diabetic retinopathy and cerebral vascular disease had a significantly higher rate of poor sleep quality compared with the group without these complications. There were no significant association between diabetic nephropathy, cardiovascular disease, peripheral nerve complication, foot complication, hypoglycemia and sleep quality. Conclusion: This study showed that older diabetic patients had many complications. Diabetic retinopathy and cerebral vascular disease were higher in the poor sleep quality group.

Diabetic Retinopathy and Chronic Kidney Disease: Associations and Comorbidities in a Large Diabetic Population: The Tongren Health Care Study

Background: Diabetic retinopathy (DR) and chronic kidney disease (CKD) in type 2 diabetes tended to have different pathogenesis, while their distinct risk factors have not been fully investigated. Methods: In the cross-sectional, community-based Tongren Health Care Study, type 2 diabetes was diagnosed based on fasting plasma glucose concentration of ≥7·0 mmol/L or a medical history. DR was assessed using color fundus photograph. CKD was defined by a reduced estimated glomerular filtration rate (eGFR) of <60ml/min/1·73mm 2 and/or albuminuria. Findings: Among the 5103 diabetes patients (8·2%) out of 62,217 participants, the prevalence of DR, reduced eGFR, albuminuria and CKD was 12·8% (95%CI:11·8%,13·7%), 4·6% (95%CI:4·2%,5·1%), 10·1% (95%CI:9·3%,10·9%) and 13·3% (95%CI:12·4%,14·3%), respectively. DR was present in 21·0% of the CKD patients while CKD was detectable in 20·9% of the DR patients. The presence of DR was significantly associated with the CKD and albuminuria (P <0·05), but not with reduced eGFR. Factors independently associated with the presence of CKD instead of DR were older age (P <0·001,OR=1·05), a higher body mass index (P <0·001,OR=1·14), a higher serum concentration of triglycerides (P <0·001,OR=1·26), and a lower blood glucose (P <0·001,OR=0·93). Having hypertension was additionally associated with the presence of reduced eGFR as compared with DR (P=0·005,OR=4·47). Interpretation: Type 2 diabetes patients with older age, higher body mass index, with hypertension and dyslipidemia had a higher chance of being affected by CKD rather than DR, while those with a higher blood glucose level were more prone to DR than CKD. Funding: Beijing Municipal of Health Reform and Development Project #2019-4. Declaration of Interest: None to declare. Ethical Approval: The study protocol was approved by the Medical Ethics Committee of Beijing Tongren Hospital and was in adherence with the Declaration of Helsinki.

Urinary peptidome and diabetic retinopathy in the DIRECT-Protect 1 and 2 trials.

Given the association of diabetic retinopathy (DR) and kidney disease, we investigated the urinary peptidome to presence and deterioration of DR in a post hoc analysis of trials investigating the effect of candesartan on progression of DR in type 1 and type 2 diabetes, respectively. Baseline urinary peptidomic analysis was performed on a random selection of 783 and 792subjects in two randomized controlled trials, DIRECT-Protect 1 and 2, respectively. End points were two-step (RET2) and three-step (RET3) change in Early Treatment of Diabetic Retinopathy Study protocol (ETDRS) defined level. Peptide levels were correlated to baseline EDTRS level in a discovery set of 2/3 of the participants from DIRECT-Protect 1. The identified peptides were then validated cross-sectionally in the remaining 1/3 from DIRECT-Protect 1. Thereafter, peptides identified in the discovery set were assessed in the entire DIRECT-Protect 1 and 2 cohorts and significant peptides were tested longitudinally. Follow-up ranged 4.0-4.7years. 24 peptides were associated with baseline DR in the discovery set. COL3A1 (seq: NTG~) and COL4A1 (seq: DGA~) were associated with baseline DR in the validation set (Rho: -.223, p<0.001 and Rho: -.141, p=0.024). Neither was significantly associated with end points. Assessing the 24 identified peptides in the entire cohorts, several collagen peptides were associated with baseline DR and end points; however, there was no overlap across diabetes types. We identified several urinary peptides (mainly collagen) associated with the presence of DR, however they could not be conclusively associated with worsening of DR.

Relationship between Diabetic Retinopathy and Systemic Neurodegenerative Diseases: A Systematic Review and Meta-analysis

To examine the potential role of systemic neurodegeneration, this study aimed to provide an overview of the available evidence on the relationship between diabetic retinopathy (DR) and systemic neurodegeneration. The association between DR and systemic neurodegeneration is inconsistent in the literature. A summary estimate on the measures of association is important to establish whether DR may be used as a risk marker of systemic neurodegeneration. We searched the literature databases PubMed/MEDLINE, EMBASE, and Cochrane Library on October 3, 2020, for all observational studies on humans evaluating the association between DR and systemic neurodegenerative diseases. Two authors conducted the literature search, study selection, and data extraction in an independent fashion. Studies were reviewed qualitatively in text and quantitatively in meta-analyses. Heterogeneity was evaluated with Cochran's Q and I2, and Funnel plot was used to investigate for skewed results and possible publication bias. We identified 27 eligible studies with a total of 1 398 041 patients with diabetes. Diagnosis of DR was made using fundus photography or examination (n = 20), health registries (n = 4), was self-reported (n = 1), or was not disclosed in the remaining studies. Neurodegenerative conditions studied were cognitive impairment (n = 23), Alzheimer's disease (n = 3), and Parkinson's disease (n = 1). In cross-sectional and longitudinal studies, respectively, presence of any DR was associated with present (odds ratio [OR], 1.57; 95% confidence interval [CI], 1.02-2.43, P=0.043) and incident (OR, 2.36; 95% CI, 1.50-3.71, P= 0.00021) systemic neurodegeneration, but severity of DR was not associated with differences in systemic neurodegeneration (OR, 0.98; 95% CI, 0.45-2.15, P= 0.96). In this systematic review, DR appears to be a marker of systemic neurodegeneration. Further studies are warranted to better elucidate the clinical practical implications of this relationship.

Clinical Characteristics and Risk of Diabetic Complications in Data-Driven Clusters Among Type 2 Diabetes.

BackgroundThis study aimed to cluster newly diagnosed patients and patients with long-term diabetes and to explore the clinical characteristics, risk of diabetes complications, and medication treatment related to each cluster.Research Design and MethodsK-means clustering analysis was performed on 1,060 Chinese patients with type 2 diabetes based on five variables (HbA1c, age at diagnosis, BMI, HOMA2-IR, and HOMA2-B). The clinical features, risk of diabetic complications, and the utilization of elven types of medications agents related to each cluster were evaluated with the chi-square test and the Tukey–Kramer method.ResultsFour replicable clusters were identified, severe insulin-resistant diabetes (SIRD), severe insulin-deficient diabetes (SIDD), mild obesity-related diabetes (MOD), and mild age-related diabetes (MARD). In terms of clinical characteristics, there were significant differences in blood pressure, renal function, and lipids among clusters. Furthermore, individuals in SIRD had the highest prevalence of stages 2 and 3 chronic kidney disease (CKD) (57%) and diabetic peripheral neuropathy (DPN) (67%), while individuals in SIDD had the highest risk of diabetic retinopathy (32%), albuminuria (31%) and lower extremity arterial disease (LEAD) (13%). Additionally, the difference in medication treatment of clusters were observed in metformin (p = 0.012), α-glucosidase inhibitor (AGI) (p = 0.006), dipeptidyl peptidase 4 inhibitor (DPP-4) (p = 0.017), glucagon-like peptide-1 (GLP-1) (p <0.001), insulin (p <0.001), and statins (p = 0.006).ConclusionsThe newly diagnosed patients and patients with long-term diabetes can be consistently clustered into featured clusters. Each cluster had significantly different patient characteristics, risk of diabetic complications, and medication treatment.

Metabolomics: A Scoping Review of Its Role as a Tool for Disease Biomarker Discovery in Selected Non-Communicable Diseases.

Metabolomics is a branch of ‘omics’ sciences that utilises a couple of analytical tools for the identification of small molecules (metabolites) in a given sample. The overarching goal of metabolomics is to assess these metabolites quantitatively and qualitatively for their diagnostic, therapeutic, and prognostic potentials. Its use in various aspects of life has been documented. We have also published, howbeit in animal models, a few papers where metabolomic approaches were used in the study of metabolic disorders, such as metabolic syndrome, diabetes, and obesity. As the goal of every research is to benefit humankind, the purpose of this review is to provide insights into the applicability of metabolomics in medicine vis-à-vis its role in biomarker discovery for disease diagnosis and management. Here, important biomarkers with proven diagnostic and therapeutic relevance in the management of disease conditions, such as Alzheimer’s disease, dementia, Parkinson’s disease, inborn errors of metabolism (IEM), diabetic retinopathy, and cardiovascular disease, are noted. The paper also discusses a few reasons why most metabolomics-based laboratory discoveries are not readily translated to the clinic and how these could be addressed going forward.

Methodology and Rationale for Ophthalmic Examinations in the Seventh and Eighth Korea National Health and Nutrition Examination Surveys (2017-2021).

This report provides a detailed description of the methodology for ophthalmic examinations according to the Korea National Health and Nutrition Examination Survey (KNHANES) VII and VIII (from 2017 to 2021). The KNHANES is a nationwide survey which has been performed since 1998 in representatives of whole Korean population. During the KNHANES VII and VIII, in addition to the ophthalmic questionnaire, intraocular pressure measurement, visual field test, auto refractometry, axial length and optical coherence tomography measurements were included. This new survey will provide not only provide normative and pathologic ophthalmic data including intraocular pressure, refractive error, axial length, visual field and precise measurement of anterior segment, macula and optic nerve with optical coherence tomography, but also a more accurate diagnosis for major adult blindness diseases, including age-related macular degeneration, diabetic retinopathy, glaucoma, and other ocular diseases, for the national Korean population.

Early Onset Age Increased the Risk of Diabetic Retinopathy in Type 2 Diabetes Patients with Duration of 10-20 Years and HbA1C ≥7%: A Hospital-Based Case-Control Study.

Background The relationship between onset age of diabetes and diabetic retinopathy (DR) is controversy and not concluded. Therefore, this hospital-based case-control study aimed to investigate the influence of diabetes onset age on the development of DR in patients with type 2 diabetes (T2D), independent of diabetic duration and HbA1c levels. Methods A sample of 780 T2D patients with diabetic duration of 10–20 years and glycated hemoglobin (HbA1c) ≥7% were enrolled in the study. 338 T2D patients with onset age ≤45 years were further selected as cases (early onset) and 79 with onset age ≥ 55 years were chosen as controls (elderly onset). International Clinical Diabetic Retinopathy Disease Severity Scale was applied to estimate the severity of DR. Results The prevalence of DR and proliferative diabetic retinopathy (PDR) was notably increased in the early onset group. When stratified by duration of diabetes, the impact of younger age on the risk of DR turned to be greatest in patients with diabetic duration ≥15 years (OR = 5.202, 95% CI 2.625–10.310). In groups stratified by HbA1c, the risk of DR was highest in patients with younger onset age and HbA1c ≥ 9% (OR = 3.889, 95% CI 1.852–8.167). Compared with the elderly onset group, the risk of DR (OR = 1.776, 95% CI = 1.326–2.380, p < 0.001) and PDR (OR = 1.605, 95% CI = 1.106–2.329, p = 0.013) in younger diagnosed patients was increased after multivariable adjustment. Conclusions Age of onset was an independent risk factor for developing DR and PDR. This suggests that it is urgent to closely monitor and treat the metabolic disorders in younger T2D patients to delay the occurrence and progression of DR.

A Compendium of Perspectives on Diabetes: A Challenge for Sustainable Health in the Modern Era.

Diabetes is a chronic illness. Hyperglycemia is the characteristic of this disorder. Diabetes is a global crisis which affects the economy and health of all nations. Over the last decades, the number of individuals living with diabetes has significantly increased worldwide. Asia is a key epicenter of the emerging diabetes epidemic, with China and India the two nations having the highest number of diabetic people. Economic development, modernization, unhealthy diet, population aging, and sedentary lifestyles are the major factors responsible for the increasing diabetes epidemic. Diabetes is associated with several complications, and cardiovascular disease is the most important cause of morbidity and mortality among people with diabetes. These life-threatening problems can be prevented or delayed by proper management of diabetes. Lifestyle modification is an important factor to decrease the diabetes risk. The frequency of diabetic complications will rise if there is a lack of cost-effective and sustainable interventions. Hence, prevention of diabetes and its complications such as diabetic retinopathy and cardiovascular disease should be a crucial part of all future health-related public policies among all nations. This review summarizes current epidemiological aspects of diabetes in the world along with its complications, preventive measures, and treatment.

1017-P: Association between Death at Day 28 and Components of Microvascular Disease of with Diabetes Hospitalized for Coronavirus Disease-2019 (COVID-19): The CORONADO Study

The prognostic role of microvascular complications (MVC) for all-cause death in patients with diabetes hospitalized for COVID-19 has been suggested. However, no detailed analysis of the microvascular burden is available so far. The CORONADO initiative is a French nationwide multicentre study of patients with diabetes hospitalized for COVID-19. The patients were enrolled after hospital admission from 03/10/2020 to 04/10/2020. MVC were defined as severe diabetic retinopathy (DR) and/or diabetic kidney disease (DKD, eGFR 300mg/g) and/or diabetic foot ulcer (DFU). We analysed the association of MVC with death within 28 days after admission. The status for all 3 MVC was ascertained for 1755 participants including 1048 (60%) free of MVC. Patients with MVC were older, had lower BMI, HbA1c, AST/ALT concentration, lymphocyte and platelet counts, while sex, type of diabetes and CRP were not significantly different. Patients with any MVC had a sex- and age-adjusted OR for death of 2.54 (95%CI: 1.94-3.31, P Disclosure P. Saulnier: None. S. Hadjadj: Advisory Panel; Self; Valbiotis, Consultant; Self; AstraZeneca, Eli Lilly and Company, Novo Nordisk, Sanofi. L. Kessler: None. M. Marre: Board Member; Self; Merck Sharp & Dohme Corp., Consultant; Self; Novo Nordisk. J. Halimi: None. M. Wargny: None. P. Gourdy: Advisory Panel; Self; Abbott, Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, Merck Sharp & Dohme Corp., Mundipharma International, Novo Nordisk, Sanofi, Research Support; Self; Novo Nordisk, Speaker’s Bureau; Self; AstraZeneca, Eli Lilly and Company, Sanofi. B. Cariou: Board Member; Self; Bristol-Myers Squibb Company, Gilead Sciences, Inc., Lexicon Pharmaceuticals, Inc., Lilly Diabetes, Novo Nordisk, Sanofi, Research Support; Self; Amgen Inc., AstraZeneca, Regeneron Pharmaceuticals Inc., Speaker’s Bureau; Self; Abbott Diabetes, Akcea Therapeutics. For the coronado study group: n/a.

Profiles of Intraday Glucose in Type 2 Diabetes and Their Association with Complications: An Analysis of Continuous Glucose Monitoring Data.

Aims: To identify profiles of type 2 diabetes from continuous glucose monitoring (CGM) data using ambulatory glucose profile (AGP) indicators and examine the association with prevalent complications. Methods: Two weeks of CGM data, collected between 2015 and 2019, from 5901 adult type 2 diabetes patients were retrieved from a clinical database in Chennai, India. Non-negative matrix factorization was used to identify profiles as per AGP indicators. The association of profiles with existing complications was examined using multinomial and logistic regressions adjusted for glycated hemoglobin (HbA1c; %), sex, age at onset, and duration of diabetes. Results: Three profiles of glycemic variability (GV) were identified based on CGM data-Profile 1 ["TIR Profile"] (n = 2271), Profile 2 ["Hypo"] (n = 1471), and Profile 3 ["Hyper"] (n = 2159). Compared with time in range (TIR) profile, those belonging to Hyper had higher mean fasting plasma glucose (202.9 vs. 167.1, mg/dL), 2-h postprandial plasma glucose (302.1 vs. 255.6, mg/dL), and HbA1c (9.7 vs. 8.6; %). Both "Hypo profile" and "Hyper profile" had higher odds of nonproliferative diabetic retinopathy ("Hypo": 1.44, 1.20-1.73; "Hyper": 1.33, 1.11-1.58), macroalbuminuria ("Hypo": 1.58, 1.25-1.98; "Hyper": 1.37, 1.10-1.71), and diabetic kidney disease (DKD; "Hypo": 1.65, 1.18-2.31; "Hyper": 1.88, 1.37-2.58), compared with "TIR profile." Those in "Hypo profile" (vs. "TIR profile") had higher odds of proliferative diabetic retinopathy (PDR; 2.84, 1.65-2.88). Conclusions: We have identified three profiles of GV from CGM data. While both "Hypo profile" and "Hyper profile" had higher odds of prevalent DKD compared with "TIR profile," "Hypo profile" had higher odds of PDR. Our study emphasizes the clinical importance of recognizing and treating hypoglycemia (which is often unrecognized without CGM) in patients with type 2 Diabetes Mellitus.

Recognising and treating psychological issues in people with diabetes mellitus.

Diabetes mellitus is a long-term condition that can lead to complications such as diabetic ketoacidosis, retinopathy and cardiovascular disease as a result of uncontrolled high blood glucose levels. In addition to these physical health complications, people with diabetes are more likely to experience psychological issues such as guilt, distress and depression compared with the general population. These issues can negatively affect an individual's ability to effectively monitor and self-manage their condition; however, they are often an overlooked aspect of diabetes care. This article explains how nurses can prevent, recognise and treat some of the psychological issues that people with diabetes commonly experience.

To Study the Role of Serum Homocysteine Levels and Ocular Involvement in Retinal Vasculitis.

Idiopathic retinal vasculitis is a chronic disease of unknown aetiology which results in ocular morbidity in patients of productive age group. Homocysteine has been implicated in various ocular conditions like age-related macular degeneration, retinal vein occlusions, diabetic retinopathy, and optic nerve diseases. We conducted a study to investigate the relation between serum homocysteine levels and retinal vasculitis at a tertiary care centre in North India.In this case-control study, 32 cases and 64 controls were included and the duration was from June 2017 to March 2019. Serum homocysteine of cases and controls was detected by reflectance photometry using VITROS Chemistry Products HCY 2 (Homocysteine) and the normal range of serum homocysteine as per this method was 6.60 to 14.80 micro mol per litre. Our study found that of the total 32 retinal vasculitis patients, serum homocysteine was raised in 65.62% (21 cases out of 32) and in 70.31% of control group (45 out of 64). Chi square test results showed that there was no significant association found between S. homocysteine levels and Reticular vasculitis (P: 0.64). The two groups were comparable in terms of the age with mean ± SD in cases being 33.47 ± 8.336years and controls being 35.16 ± 8.568years with a P value of 0.37 being statistically insignificant. The data collected was analysed using SSPS-16 (Statistical Package for Social Sciences Version 16) software and test of association was Odd's ratio. Power of study was 80% and P < 0.05 is considered statistically significant. We found that there is no significant association between raised serum homocysteine and retinal vasculitis (with P < 0.64). Odds ratio was 0.80(0.33-1.99) implying that the cases and controls were statistically significantly different with respect to homocysteine levels.

Fractional-Order Edge Detection Masks for Diabetic Retinopathy Diagnosis as a Case Study

Edge detection is one of the main steps in the image processing field, especially in biomedical imaging, to diagnose a disease or trace its progress. The transfer of medical images makes them more susceptible to quality degradation due to any imposed noise. Hence, the protection of this data against noise is a persistent need. The efficiency of fractional-order filters to detect fine details and their high noise robustness, unlike the integer-order filters, it renders them an attractive solution for biomedical edge detection. In this work, two novel central fractional-order masks are proposed with their detailed mathematical proofs. The fractional-order parameter gives an extra degree of freedom in designing different masks. The noise performance of the proposed masks is evaluated upon applying Salt and Pepper noise and Gaussian noise. Numerical results proved that the proposed masks outperform the integer-order masks regarding both types of noise, achieving higher Peak Signal to Noise Ratio. As a practical application, the proposed fractional-order edge detection masks are employed to enhance the Diabetic Retinopathy disease diagnosis.

Greedy segmentation based diabetic retinopathy identification using curvelet transform and scale invariant features

Diabetic retinopathy (DR) is the major reason of vision loss in the active population. It can usually be prevented by regulating the blood glucose and providing a timely treatment. DR has clinical features recognized by the experts including the blood vessel area, exudates, neovascularization, hemorrhages, and microaneurysm. Because DR has some varieties and complexities due to its geometrical and haemodynamic features, it is hard and time-consuming to detect DR in manual diagnosis. In Computer Aided Diagnosis (CAD) systems, the features of DR fundus images are detected using computer vision techniques. In this paper, a CAD system is proposed, which distinguishes automatically whether the fundus is normal or it suffers from diabetic retinopathy disease. As preprocess morphological operations like filtering, opening, and dilation are applied to the images firstly, then, Optic Disk (OD) segmentation is implemented using Greedy algorithm. Because of the intensity of an OD is similar to some DR intensities, OD regions are removed from the fundus images for an accurate feature extraction. The features extracted with Curvelet Transform (CT) and Scale Invariant Feature Transform (SIFT), respectively, are concatenated to provide a feature set that defines the fundus data optimally. Finally, the feature set is given to the Support Vector Machines (SVM), K-Nearest Neighborhood (KNN), and Naïve–Bayes (NB) classifiers for the DR identification purpose. The proposed method achieves the highest accuracy and sensitivity as 92.8% and 97.6%, respectively, with SVM and specificity as 92.5% with KNN classifier.

Detection of Diabetic Retinopathy from Ultra-Widefield Scanning Laser Ophthalmoscope Images: A Multicenter Deep Learning Analysis

To develop a deep learning (DL) system that can detect referable diabetic retinopathy (RDR) and vision-threatening diabetic retinopathy (VTDR) from images obtained on ultra-widefield scanning laser ophthalmoscope (UWF-SLO). Observational, cross-sectional study. A total of 9392 UWF-SLO images of 1903 eyes from 1022 subjects with diabetes from Hong Kong, the United Kingdom, India, and Argentina. All images were labeled according to the presence or absence of RDR and the presence or absence of VTDR. Labeling was performed by retina specialists from fundus examination, according to the International Clinical Diabetic Retinopathy Disease Severity Scale. Three convolutional neural networks (ResNet50) were trained with a transfer-learning procedure for assessing gradability and identifying VTDR and RDR. External validation was performed on 4 datasets spanning different geographical regions. Area under the receiver operating characteristic curve (AUROC); area under the precision-recall curve (AUPRC); sensitivity, specificity, and accuracy of the DL system in gradability assessment; and detection of RDR and VTDR. For gradability assessment, the system achieved an AUROC of 0.923 (95% confidence interval [CI], 0.892-0.947), sensitivity of 86.5% (95% CI, 77.6-92.8), and specificity of 82.1% (95% CI, 77.3-86.2) for the primary validation dataset, and >0.82 AUROCs, >79.6% sensitivity, and >70.4% specificity for the geographical external validation datasets. For detecting RDR and VTDR, the AUROCs were 0.981 (95% CI, 0.977-0.984) and 0.966 (95% CI, 0.961-0.971), with sensitivities of 94.9% (95% CI, 92.3-97.9) and 87.2% (95% CI, 81.5-91.6), specificities of 95.1% (95% CI, 90.6-97.9) and 95.8% (95% CI, 93.3-97.6), and positive predictive values (PPVs) of 98.0% (95% CI, 96.1-99.0) and 91.1% (95% CI, 86.3-94.3) for the primary validation dataset, respectively. The AUROCs and accuracies for detecting both RDR and VTDR were >0.9% and >80%, respectively, for the geographical external validation datasets. The AUPRCs were >0.9, and sensitivities, specificities, and PPVs were >80% for the geographical external validation datasets for RDR and VTDR detection. The excellent performance achieved with this DL system for image quality assessment and detection of RDR and VTDR in UWF-SLO images highlights its potential as an efficient and effective diabetic retinopathy screening tool.

MES-Net: a new network for retinal image segmentation

Glaucoma, diabetic retinopathy, and other eye diseases have seriously threatened people’s visual health. Whether it is clinical diagnosis or computer-aided diagnosis, the accurate segmentation of the retinal tissues and lesion areas (optic disc (OD), optic cup (OC), retinal blood vessels) are indispensable. In this paper, we propose a novel network MES-Net for the retinal image segmentation tasks. MES-Net adopts U-Net as the overall architecture, including three proposed modules: multi-scale feature pre-extraction (MFP) block, encoder spatial cascading encoding (ESCE) path, and decoder input SE block. We use MFP block to extract multi-scale semantic features from input images with different resolutions, and take ESCE path to extract deep features and improve the feature reuse rate. SE block can weaken the semantic gaps between the encoder paths and the decoder paths. For retinal vessel segmentation, the accuracy values of MES-Net tested on the DRIVE, STARE and CHASE datasets are 0.9667, 0.9724, and 0.9697, and AUC is 0.9853, 0.9897, and 0.9869, respectively. For OC and OD simultaneous segmentation, the overlap coefficients of OC and OD tested on the ORIGA dataset are 0.818 and 0.948, and the accuracy are 0.935 and 0.977, respectively. The experimental results show that the proposed method significantly improves the performance of the original U-Net and is superior to other state-of-the-art methods. Thus, it can be applied to retinal image segmentation tasks, and many other medical image segmentation problems can also benefit from the proposed method.

A polymer membrane electrolysis micropump powered by a compact wireless power transmission system

In this study, a polydimethylsiloxane (PDMS) membrane electrolysis micropump activated by a miniaturized wireless power transfer (WPT) system is introduced. The micropump system has total dimensions of 20 × 14 × 8.2 mm3. Combined with a compact WPT system, the micropump enables the delivery of drugs to the treatment area on demand. The WPT system consists of an impedance matching network and a two-stage voltage multiplier. Voltages ranging from 0.8 V to 1.3 V can be generated at different operating distance of 8 cm to 20 cm away from the RF source. The micropump is capable of delivering liquid continuously over a flow ranging from 0.11 µL/min to 4.84 µL/min. This micropump demonstrates a maximum cumulative released drug volume of up to 100.8 µL and was able to produce a pressure that is higher than the intraocular pressure. The results can be used for the study of age-related macular degeneration, diabetic retinopathy, and other ophthalmic diseases.