Neuromyelitis spectrum disease (NMOSD) is an immune-mediated inflammatory demyelinating disease of the central nervous system. The breakdown of the blood-brain barrier (BBB), as an important link in the pathogenesis of NMOSD, has an important impact on the occurrence, development and prognosis of the disease. It is generally believed that the aquaporin 4 antibody produced in the peripheral circulation crosses the BBB cause damage to the central nervous system, and there are components involved in the destruction of BBB in the occurrence and development of NMOSD disease. At present, little is known about the molecular mechanism of BBB destruction in NMOSD lesions and there is still a lack of systematic theory. Further research and exploration of the regulatory mechanism of BBB permeability and the manifestation of barrier destruction in NMOSD diseases are of great significance for understanding the pathogenesis of NMOSD, so as to achieve early diagnosis and discover new therapeutic and preventive targets.
Objective To construct and evaluate a screening and diagnostic system based on color fundus images and artificial intelligence (AI)-assisted screening for optic neuritis (ON) and non-arteritic anterior ischemic optic neuropathy (NAION). MethodsA diagnostic test study. From 2016 to 2020, 178 cases 267 eyes of NAION patients (NAION group) and 204 cases 346 eyes of ON patients (ON group) were examined and diagnosed in Zhongshan Ophthalmic Center of Sun Yat-sen University; 513 healthy individuals of 1 160 eyes (the normal control group) with normal fundus by visual acuity, intraocular pressure and optical coherence tomography examination were collected from 2018 to 2020. All 2 909 color fundus images were as the data set of the screening and diagnosis system, including 730, 805, and 1 374 images for the NAION group, ON group, and normal control group, respectively. The correctly labeled color fundus images were used as input data, and the EfficientNet-B0 algorithm was selected for model training and validation. Finally, three systems for screening abnormal optic discs, ON, and NAION were constructed. The subject operating characteristic (ROC) curve, area under the ROC (AUC), accuracy, sensitivity, specificity, and heat map were used as indicators of diagnostic efficacy. ResultsIn the test data set, the AUC for diagnosing the presence of an abnormal optic disc, the presence of ON, and the presence of NAION were 0.967 [95% confidence interval (CI) 0.947-0.980], 0.964 (95%CI 0.938-0.979), and 0.979 (95%CI 0.958-0.989), respectively. The activation area of the systems were mainly located in the optic disc area in the decision-making process. ConclusionAbnormal optic disc, ON and NAION, and screening diagnostic systems based on color fundus images have shown accurate and efficient diagnostic performance.
ObjectiveTo analyze the causal relationship between SARS-CoV-2 infection and retinal vascular obstruction by mendelian randomization (MR). MethodsA two-sample MR analysis utilizing summary statistics from genome-wide association studies (GWAS) in European populations was conducted. The GWAS data for SARS-CoV-2 infection comprised cases of common infection (2 597 856), hospitalized infection (2 095 324), and severe infection (1 086 211). Data on retinal vascular obstruction were obtained from the FinnGen database, which included 203 269 cases of retinal artery obstruction and 182 945 cases of retinal vein obstruction (RVO). Inverse variance weighting (IVW), random effects models, weighted median (WM), MR-Egger regression, simple models, and weighted models were used to analyze the bidirectional causal relationship between different SARS-CoV-2 infection phenotypes and retinal obstruction. The Q statistic was used to assess heterogeneity among single nucleotide polymorphisms (SNP), while MR-Presso was utilized to detect SNP outliers, and MR-Egger intercept tests were performed to evaluate horizontal pleiotropy. ResultsThe MR analysis, using IVW, random effects models, MR-Egger, WM, and weighted models, indicated no significant association between common SARS-CoV-2 infection, hospitalized infection, severe infection, and retinal vascular obstruction (P＞0.05). Additionally, retinal vascular obstruction did not show a significant association with the various SARS-CoV-2 infection phenotypes (P＞0.05). In the simple model, a significant association was found between severe SARS-CoV-2 infection and RVO (P＜0.05), as well as between RVO and common SARS-CoV-2 infection (P＜0.05). No heterogeneity was observed in the IVW and MR-Egger analyses (P＞0.05). The MR-Egger test provided no evidence of horizontal pleiotropy (P＞0.05), and MR-Presso detected no outlier SNP. ConclusionThe findings of this study do not support a causal relationship between SARS-CoV-2 infection and the occurrence of retinal vascular obstruction.