Identification of Ocular Autoantigens Associated With Juvenile Idiopathic Arthritis-Associated Uveitis

The purpose of the current study was to analyze the binding patterns of serum autoantibodies from juvenile idiopathic arthritis (JIA) and JIA-associated uveitis (JIAU) patients to proteomes from different ocular tissues and to identify potential ocular autoantigens in JIAU. Proteomes from porcine iris, ciliary body, or retina tissue were isolated, separated using 2D-gel electrophoresis, and transferred to a blotting membrane. The binding pattern of serum antibodies from JIA or JIAU patients or healthy controls to ocular proteins was visualized by using anti-human IgG secondary antibodies and chemiluminescence reaction. Selected protein spots were excised from silver-stained 2D gels and subjected to mass spectrometry. Serum antibodies binding to ocular proteins were detected in all patient groups and healthy controls. Irrespective of the patient groups, serum antibodies bound to 49 different protein spots of the retina proteome, to 53 of the ciliary body proteome, and to 44 of the iris proteome. The relative binding frequency of sera to these iris protein spots was significantly higher in JIAU than in JIA patients or healthy controls. Particularly in JIAU patients, cluster analyses indicated a broad range of serum antibodies directed against ocular antigens, mostly in the iris proteome. Iris proteins frequently bound by serum antibodies in all groups were identified as tubulin beta chain, vimentin, ATP synthase subunit beta, actin, and L-lactate dehydrogenase B chain. Iris proteins exclusively bound by JIAU serum antibodies were heat shock cognate 71 kDa protein and keratin. Although serum autoantibody binding to ocular antigens was not disease-specific, a significant diversity of autoantibodies against a broad range of antigens, particularly from the iris tissue, was detected in JIAU patients. As the iris is a major site of inflammation in JIAU, the present data give further evidence that autoantibodies may be involved in JIAU immunopathology

Localization of Pedestrian Lights on Mobile Devices

In this work a framework is presented to localize and classify pedestrian lights using mobile devices. Our method can be applied as interactive assistance for visually handicapped people to help them passing a pedestrian crossing. Since the computation power and the storage resources of mobile devices are limited the main objective on the localization task is the efficiency of the used computer vision algorithms. The requirement on the traffic light classification is not to miss the crucial red light (reliability). We have designed a prototype for German traffic lights and realized it on a Nokia N95. The presented results demonstrate the efficiency as well as reliability of our method.APSIPA ASC 2009: Asia-Pacific Signal and Information Processing Association, 2009 Annual Summit and Conference. 4-7 October 2009. Sapporo, Japan. Oral session: Vision-based Information Processing and Applications (6 October 2009)

A vision transformer architecture for the automated segmentation of retinal lesions in spectral domain optical coherence tomography images

Abstract Neovascular age-related macular degeneration (nAMD) is one of the major causes of irreversible blindness and is characterized by accumulations of different lesions inside the retina. AMD biomarkers enable experts to grade the AMD and could be used for therapy prognosis and individualized treatment decisions. In particular, intra-retinal fluid (IRF), sub-retinal fluid (SRF), and pigment epithelium detachment (PED) are prominent biomarkers for grading neovascular AMD. Spectral-domain optical coherence tomography (SD-OCT) revolutionized nAMD early diagnosis by providing cross-sectional images of the retina. Automatic segmentation and quantification of IRF, SRF, and PED in SD-OCT images can be extremely useful for clinical decision-making. Despite the excellent performance of convolutional neural network (CNN)-based methods, the task still presents some challenges due to relevant variations in the location, size, shape, and texture of the lesions. This work adopts a transformer-based method to automatically segment retinal lesion from SD-OCT images and qualitatively and quantitatively evaluate its performance against CNN-based methods. The method combines the efficient long-range feature extraction and aggregation capabilities of Vision Transformers with data-efficient training of CNNs. The proposed method was tested on a private dataset containing 3842 2-dimensional SD-OCT retina images, manually labeled by experts of the Franziskus Eye-Center, Muenster. While one of the competitors presents a better performance in terms of Dice score, the proposed method is significantly less computationally expensive. Thus, future research will focus on the proposed network’s architecture to increase its segmentation performance while maintaining its computational efficiency

Enhancing presence in head-mounted display environments by visual body feedback using head-mounted cameras

Abstract—A fully-articulated visual representation of a user in an immersive virtual environment (IVE) can enhance the user’s subjective sense of feeling present in the virtual world. Usually this requires the user to wear a full-body motion capture suit to track real-world body movements and to map them to a virtual body model. In this paper we present an augmented virtuality approach that allows to incorporate a realistic view of oneself in virtual environments using cameras attached to head mounted displays. The described system can easily be integrated into typical virtual reality setups. Egocentric camera images captured by a video-see-through system are segmented in real-time into foreground, showing parts of the user’s body, e. g., her hands or feet, and background. The segmented foreground is then displayed as inset in the user’s current view of the virtual world. Thus the user is able to see her physical body in an arbitrary virtual world, including individual characteristics such as skin pigmentation and hairiness. Keywords-Head-mounted displays; augmented virtuality; presence I

Occurrence and Risk Factors for Macular Edema in Patients with Juvenile Idiopathic Arthritis-Associated Uveitis.

PURPOSE To analyze occurrence and risk factors for macular edema (ME) in juvenile idiopathic arthritis-associated uveitis (JIA-U). METHODS Retrospective analysis of patients with JIA-U at a tertiary referral uveitis center between 2000 and 2019. Epidemiological data and clinical findings before ME onset were evaluated. RESULTS Out of 245 patients, ME developed in 41 (18%) of the 228 JIA-U patients for whom data documentation was complete during the follow-up (mean 4.0 ± 3.8 years). Risk factors (univariable logistic regression analysis) at baseline for subsequent ME onset included older age at initial documentation at institution (hazard ratio, HR 1.19, p < 0.0001), longer duration of uveitis at initial documentation (HR 1.17, p < 0.0001), worse best-corrected visual acuity (BCVA; HR 2.49, p < 0.0001), lower intraocular pressure (IOP; HR 0.88, p < 0.01), band keratopathy (HR 2.29, p < 0.01), posterior synechiae (HR 2.55, p < 0.01), epiretinal membrane formation (HR 6.19, p < 0.0001), optic disc swelling (HR 2.81, p < 0.01), and cataract (HR 4.24, p < 0.0001). Older age at initial documentation at institution (HR 1.55, p < 0.001), worse BCVA (HR 28.56, p < 0.001), and higher laser-flare photometry (LFM) values (HR 1.003, p = 0.01) were independent risk factors for ME manifestation. Patients with ME revealed significant changes in BCVA, LFM, and IOP and new optic disc swelling at 6 and 3 months before ME onset compared to timepoint of ME occurrence (p < 0.05, each). CONCLUSION ME is a common complication of JIA-U. Demographic risk factors and courses of IOP, BCVA, and LFM may indicate patients at risk for ME onset

Vessel density in OCT angiography permits differentiation between normal and glaucomatous optic nerve heads

AIM: To evaluate whether optical coherence tomography angiography (OCTA) can detect altered vessel density (VD) at the optic nerve head (ONH) in glaucoma patients. Special attention is paid to the accuracy of the OCTA technique for distinguishing healthy from glaucomatous eyes. METHODS: A total of 171 eyes were examined by the OCTA system AngioVue™ (Optovue): 97 eyes diagnosed with glaucoma and 74 healthy control eyes. The papillary and peripapillary VD was measured. Furthermore, the VD was correlated with different structural and functional measurements. In order to test the accuracy of differentiation between eyes with and without glaucoma, we calculated the receiver operating characteristic curve (ROC) and the area under the curve (AUC). RESULTS: The papillary and peripapillary VD in glaucomatous eyes was significantly lower than in healthy eyes (P<0.05). The VD of the nasal peripapillary sector was significantly lower than in the other sectors. The further the disease had progressed [measured by determining the thickness of the ganglion cell complex (GCC) and the retinal nerve fiber layer (RNFL)] the greater the VD reduction. The AUC discriminated well between glaucomatous and normal eyes (consensus classifier 94.2%). CONCLUSION: OCTA allows non-invasive quantification of the peripapillary and papillary VD, which is significantly reduced in glaucomatous eyes and accurately distinguishes between healthy and diseased eyes. OCTA expands the spectrum of procedures for detecting and monitoring glaucoma