ABSTRACT
PURPOSE: To report a case of multiple evanescent white dot syndrome (MEWDS) following severe acute respiratory syndrome coronavirus 2 vaccination. STUDY DESIGN: Case report. RESULTS: A 36-year-old healthy Taiwanese female was presented with flashing lights in the right eye two days after the first dose of Medigen Vaccine Biologics Corporation (MVC) coronavirus disease 19 (COVID-19) vaccine. Examination of the retina revealed multiple white dots in the posterior pole extending to the mid-periphery. Disruption of ellipsoid zone on optical coherence tomography, early hyperfluorescence on fluorescein angiography, late hypo-cyanescence on indocyanine green angiography, and paracentral scotoma on visual field test were consistent with MEWDS. At four-week follow-up, the patient's fundus lesions resolved, and symptoms subsided without treatment. CONCLUSION: Resembling previous post-vaccine MEWDs cases, the symptoms are self-limited, and the visual prognosis is excellent. The presented case demonstrates MEWDS following MVC COVID-19 vaccine and suggests the immune-mediated basis for MEWDS in predisposed patients.
Subject(s)
Biological Products , COVID-19 Vaccines , COVID-19 , Retinal Diseases , White Dot Syndromes , Adult , Female , Humans , Biological Products/administration & dosage , COVID-19/prevention & control , COVID-19 Vaccines/adverse effects , Fluorescein Angiography/methods , Indocyanine Green , Retinal Diseases/chemically induced , Retinal Diseases/diagnosis , Tomography, Optical Coherence/methods , Vaccination/adverse effects , White Dot Syndromes/chemically inducedABSTRACT
An automatic method is presented for detecting myocardial ischemia, which can be considered as the early symptom of acute coronary events. Myocardial ischemia commonly manifests as ST- and T-wave changes on ECG signals. The methods in this study are proposed to detect abnormal ECG beats using knowledge-based features and classification methods. A novel classification method, sparse representation-based classification (SRC), is involved to improve the performance of the existing algorithms. A comparison was made between two classification methods, SRC and support-vector machine (SVM), using rule-based vectors as input feature space. The two methods are proposed with quantitative evaluation to validate their performances. The results of SRC method encompassed with rule-based features demonstrate higher sensitivity than that of SVM. However, the specificity and precision are a trade-off. Moreover, SRC method is less dependent on the selection of rule-based features and can achieve high performance using fewer features. The overall performances of the two methods proposed in this study are better than the previous methods.
