Age-related macular degeneration is one of the major causes of blindness in the elderly. As an important pathway of cell metabolism, autophagy maintains intracellular homeostasis through the degradation and recycle of damaged organelles and macromolecules. Understanding its mechanism may promote discoveries to delay aging process, reduce the incidence of age-related diseases. In mammals, silent information regulator protein 6 (SIRT6) plays its deacetylase and ribonucleotransferase activity in multiple signaling pathways, including inhibition of cellular senescence, tumorigenesis, metabolic diseases, regulating cellular lifespan. It has a significant impact on the structure and function of tissues and organs. SIRT6 regulates intracellular autophagy mainly through the insulin-like growth factor-protein kinase B-mammalian target of rapamycin, reducing the accumulation of toxic metabolites and cellular senescence. The function of SIRT6 in age-related macular degeneration need to be combined with the genetic background, pathogenesis, clinical manifestations and other aspects of the disease, and it is expected to be further studied in subsequent studies.
Diabetic retinopathy is a vascular complication of diabetes, and homocysteine is an intermediate product of methionine metabolism. Hyperhomocysteinemia can directly or indirectly damage vascular endothelial cells, causing vascular endothelial cells dysfunction and participating in the occurrence and development of diabetic retinopathy. Uric acid is the final product of purine metabolism. Hyperuricemia can cause vascular endothelial dysfunction, oxidative metabolism, platelet adhesion and aggregation dysfunction, thus participating in the occurrence and development of diabetic retinopathy. In recent years, there have been many studies on the correlation between diabetic retinopathy and levels of homocysteine and uric acid. This article reviews the relevant literature at home and abroad in order to provide new information for the prevention and treatment of diabetic retinopathy.
Diabetic macular edema (DME) is the main cause of visual impairment in diabetic retinopathy patients. It mainly includes focal DME and diffuse DME, while DME of clinical significance needs timely intervention treatment. Optical coherence tomography is currently recognized as the most sensitive method to accurately diagnose DME. Currently, the common treatments of DME include intravitreal injection of anti-vascular endothelial growth factor (VEGF) or glucocorticoid and laser photocoagulation. Among them, anti-VEGF injection is becoming the first-line therapeutic, and corresponding individual treatment or combined treatment strategy should be selected according to the characteristics of DME and the specific conditions of patients. During the diagnosis and treatment of DME, attention should be paid to the systemic treatment of diabetes and the effect of diabetes-related neuroretinopathy on the therapeutic effect of DME. With the appearance of heterogeneity in the efficacy of anti-VEGF drugs, it remains to be further studied how to choose alternative therapeutics and when to replace them.
Objective To investigate the early influences of laser photocoagulation on macular retinal thickness in diabetic retinopathy(DR). Methods Optic coherence tomography examination was performed in 30 eyes with DR(phase Ⅲ~Ⅳ) before, and on the 3rd day and the 7th day after photocoagulation respectively. The thickness of neuroretina and pigment epithelium were measured in the areas of fovea macula and 750 μm from fovea macula. Results Three days after photocoagulation, significant thickening of neuroretina was observed in the fovea macula, which is positively related with age, fasting blood sugar and duration of DR. There was no significant changes in the thickness of pigment epithelium in macula and in the thickness of neuroretina 750 μm from fovea macula. Conclusion Significant thickening of neuroretina in fovea macula in DR early after photocoagulation reveals progressed macular edema induced by photocoagulation which is positively related with age, fasting blood sugar and duration of DR. (Chin J Ocul Fundus Dis, 2002, 18: 31-33)
ObjectiveTo compare the consistency of artificial analysis and artificial intelligence analysis in the identification of fundus lesions in diabetic patients.MethodsA retrospective study. From May 2018 to May 2019, 1053 consecutive diabetic patients (2106 eyes) of the endocrinology department of the First Affiliated Hospital of Zhengzhou University were included in the study. Among them, 888 patients were males and 165 were females. They were 20-70 years old, with an average age of 53 years old. All patients were performed fundus imaging on diabetic Inspection by useing Japanese Kowa non-mydriatic fundus cameras. The artificial intelligence analysis of Shanggong's ophthalmology cloud network screening platform automatically detected diabetic retinopathy (DR) such as exudation, bleeding, and microaneurysms, and automatically classifies the image detection results according to the DR international staging standard. Manual analysis was performed by two attending physicians and reviewed by the chief physician to ensure the accuracy of manual analysis. When differences appeared between the analysis results of the two analysis methods, the manual analysis results shall be used as the standard. Consistency rate were calculated and compared. Consistency rate = (number of eyes with the same diagnosis result/total number of effective eyes collected) × 100%. Kappa consistency test was performed on the results of manual analysis and artificial intelligence analysis, 0.0≤κ＜0.2 was a very poor degree of consistency, 0.2≤κ＜0.4 meant poor consistency, 0.4≤κ＜0.6 meant medium consistency, and 0.6≤κ＜1.0 meant good consistency.ResultsAmong the 2106 eyes, 64 eyes were excluded that cannot be identified by artificial intelligence due to serious illness, 2042 eyes were finally included in the analysis. The results of artificial analysis and artificial intelligence analysis were completely consistent with 1835 eyes, accounting for 89.86%. There were differences in analysis of 207 eyes, accounting for 10.14%. The main differences between the two are as follows: (1) Artificial intelligence analysis points Bleeding, oozing, and manual analysis of 96 eyes (96/2042, 4.70%); (2) Artificial intelligence analysis of drusen, and manual analysis of 71 eyes (71/2042, 3.48%); (3) Artificial intelligence analyzes normal or vitreous degeneration, while manual analysis of punctate exudation or hemorrhage or microaneurysms in 40 eyes (40/2042, 1.95%). The diagnostic rates for non-DR were 23.2% and 20.2%, respectively. The diagnostic rates for non-DR were 76.8% and 79.8%, respectively. The accuracy of artificial intelligence interpretation is 87.8%. The results of the Kappa consistency test showed that the diagnostic results of manual analysis and artificial intelligence analysis were moderately consistent (κ=0.576, P＜0.01).ConclusionsManual analysis and artificial intelligence analysis showed moderate consistency in the diagnosis of fundus lesions in diabetic patients. The accuracy of artificial intelligence interpretation is 87.8%.
Objective To study the relationship between insulinase activity of erythrocytes(EIA)and diabetic retinopathy(DR)in non insulin dependent diabetes mellitus (NIDDM) patients． Methods EIA,fasting plasma glucose (FPG),fasting plasma insulin (FINS) and glycosylated hemoglobin (HbA1c) were determined in 55 healthy controls,42 NIDDM patients with DR and 44 NIDDM patients without DR． Results EIA was lower,disease duration was longer,and FPG and HbA1c were higher in NIDDA patients with DR.EIA was decreased,duration of NIDDM was lengthened,FPG and HbA1c were increased in NIDDM patients with proliferative DR as compared with NIDDM patients with background DR．The correlation analysis showed,in NIDDM patients with DR,EIA was inversely correlated with FPG,HbA1c and duration of NIDDM． Conclusion Insulinase may play certain role in the onset and development of DR． (Chin J Ocul Fundus Dis,1998,14:132-134)