Hvasswhitaker4096

BACKGROUND Having Juvenile idiopathic Arthritis (JIA) has widespread implications for a person's life. Patients have to deal with recurring arthritis, characterized by pain often accompanied by a loss of energy. Since JIA often persists into adulthood, patients with JIA are likely to encounter difficulties in their working life. We expect that the experiences in school life may be comparable to the barriers and opportunities which patients affected by JIA encounter in adult working life. Therefore, the aim of this study was to elicit the experiences during school life and the perspectives and expectations regarding future work participation of adolescents with JIA. METHODS This study used individual, semi-structured interviews and followed a predefined interview guide. Participants between 14 and 18 years of age (n = 22) were purposively selected to achieve a broad range of participant characteristics. Open coding was performed, followed by axial coding and selective coding. RESULTS Great differences were seen in the support and understanding that adolescents received in dealing with JIA at school, leisure activities and work. 4SC-202 research buy Varying approaches were mentioned on how to pursue a desired vocation. Perspectives regarding disclosure varied. Participants wished to be approached like any other healthy adolescent. Expectations regarding work participation were positively expressed. CONCLUSION This study showed that participants often disregarded having JIA when making plans for their future career. Facilitating an open discussion about the possible limitations accompanying JIA with educators and employers might prevent overburden and increase the chance of starting a career which would accommodate the patient with JIA in the near and distant future.INTRODUCTION This is a systematic review on the main algorithms using machine learning (ML) in retinal image processing for glaucoma diagnosis and detection. ML has proven to be a significant tool for the development of computer aided technology. Furthermore, secondary research has been widely conducted over the years for ophthalmologists. Such aspects indicate the importance of ML in the context of retinal image processing. METHODS The publications that were chosen to compose this review were gathered from Scopus, PubMed, IEEEXplore and Science Direct databases. Then, the papers published between 2014 and 2019 were selected . Researches that used the segmented optic disc method were excluded. Moreover, only the methods which applied the classification process were considered. The systematic analysis was performed in such studies and, thereupon, the results were summarized. DISCUSSION Based on architectures used for ML in retinal image processing, some studies applied feature extraction and dimensionality reduction to detect and isolate important parts of the analyzed image. Differently, other works utilized a deep convolutional network. Based on the evaluated researches, the main difference between the architectures is the number of images demanded for processing and the high computational cost required to use deep learning techniques. CONCLUSIONS All the analyzed publications indicated it was possible to develop an automated system for glaucoma diagnosis. The disease severity and its high occurrence rates justify the researches which have been carried out. Recent computational techniques, such as deep learning, have shown to be promising technologies in fundus imaging. Although such a technique requires an extensive database and high computational costs, the studies show that the data augmentation and transfer learning techniques have been applied as an alternative way to optimize and reduce networks training.BACKGROUND Juvenile idiopathic arthritis-associated uveitis (JIA-U) is a serious condition associated with the risk of blindness. However, pediatric rheumatologists rarely encounter cases of blindness, because most patients reach adulthood during the course of follow-up before blindness occurs. Here, we report the progress of 9 patients with JIA-U, including 2 patients who became blind after the transition period. We aimed to highlight the importance of the role of pediatric rheumatologists and transitional care in preventing blindness associated with JIA-U. CASE PRESENTATION We conducted a retrospective analysis of the case records of 9 JIA-U patients (1 male, 8 female; median age 16.8 years, range 5.5-19.8 years). All patients presented with oligo-juvenile idiopathic arthritis (oligo-JIA) (one presented with extended oligo-JIA); the median age of uveitis onset was 5.0 years (range 3.0-13.0 years), and the onset of uveitis preceded the onset of arthritis in 2 patients. The median disease duration was 12.5 yeosis, the specialized treatment with the involvement of pediatric rheumatologists is necessary early on, and consideration for transitional medicine is essential. Therefore, this report reaffirms the importance of planned transitional care that has been advocated for globally.BACKGROUND Autoimmune diseases have been associated with changes in the gut microbiome. In this study, the gut microbiome was evaluated in individuals with dry eye and bacterial compositions were correlated to dry eye (DE) measures. We prospectively included 13 individuals with who met full criteria for Sjögren's (SDE) and 8 individuals with features of Sjögren's but who did not meet full criteria (NDE) for a total of 21 cases as compared to 21 healthy controls. Stool was analyzed by 16S pyrosequencing, and associations between bacterial classes and DE symptoms and signs were examined. RESULTS Results showed that Firmicutes was the dominant phylum in the gut, comprising 40-60% of all phyla. On a phyla level, subjects with DE (SDE and NDE) had depletion of Firmicutes (1.1-fold) and an expansion of Proteobacteria (3.0-fold), Actinobacteria (1.7-fold), and Bacteroidetes (1.3-fold) compared to controls. Shannon's diversity index showed no differences between groups with respect to the numbers of different operational taxonomic units (OTUs) encountered (diversity) and the instances these unique OTUs were sampled (evenness). On the other hand, Faith's phylogenetic diversity showed increased diversity in cases vs controls, which reached significance when comparing SDE and controls (13.57 ± 0.89 and 10.96 ± 0.76, p = 0.02). Using Principle Co-ordinate Analysis, qualitative differences in microbial composition were noted with differential clustering of cases and controls. Dimensionality reduction and clustering of complex microbial data further showed differences between the three groups, with regard to microbial composition, association and clustering. Finally, differences in certain classes of bacteria were associated with DE symptoms and signs. CONCLUSIONS In conclusion, individuals with DE had gut microbiome alterations as compared to healthy controls. Certain classes of bacteria were associated with DE measures.