June 2016

PURPOSE: To analyze factors predictive of having treatment-resistant uveitis in patients with juvenile idiopathic arthritis (JIA)-associated uveitis. METHODS: The medical records of patients diagnosed with JIA-associated uveitis treated at a single tertiary referral center from October 2005 to March 2013 were reviewed retrospectively. The main outcome measures were demographic characteristics, ocular comorbidity, clinical course, treatments, and baseline risk factors associated with poor response to first-line therapies. RESULTS: A total of 96 patients (175 eyes) were included. Of these, 58 patients (108 eyes) required biologic disease-modifying antirheumatic drugs or alkylating agents for their uveitis during follow-up (recalcitrant group), and 38 patients (67 eyes) did not (nonrecalcitrant group). Eyes of the recalcitrant group tended to have a higher incidence of cataract at baseline (49%; P < 0.0001). In the nonrecalcitrant group, the most frequent complications were cataract (20.9%) and secondary glaucoma (20.9%). The mean number of flares in the recalcitrant group was significantly reduced from 3.7/eye/year prior to cataract surgery to 1.6/eye/year after (P < 0.0001). Nuclear cataract was found to be an independent predictor for a severe course of JIA-associated uveitis. Any other type of cataract, posterior synechiae, male sex, or active uveitis at baseline were not found to be independently associated with recalcitrant uveitis. CONCLUSIONS: Nuclear cataract at baseline evaluation is a risk factor for poor response to first-line therapies in JIA-associated uveitis patients.

The ability to integrate 'omics' (i.e. transcriptomics and proteomics) is becoming increasingly important to the understanding of regulatory mechanisms. There are currently no tools available to identify differentially expressed genes (DEGs) across different 'omics' data types or multi-dimensional data including time courses. We present fCI (f-divergence Cut-out Index), a model capable of simultaneously identifying DEGs from continuous and discrete transcriptomic, proteomic and integrated proteogenomic data. We show that fCI can be used across multiple diverse sets of data and can unambiguously find genes that show functional modulation, developmental changes or misregulation. Applying fCI to several proteogenomics datasets, we identified a number of important genes that showed distinctive regulation patterns. The package fCI is available at R Bioconductor and http://software.steenlab.org/fCI/.

The first aim of this study was to clarify whether cigarette smoking affects tear secretion, goblet cell density, and tear MUC5AC concentration. The second purpose was to evaluate the correlations of conjunctival goblet cell density with tear MUC5AC concentration and other ocular surface evaluation factors. This cross-sectional study included 88 office workers. All subjects were required to fill in dry eye and smoking questionnaires, in addition to ocular surface evaluation. Tear wash fluid was collected from inferior fornix, and conjunctival epithelium was obtained by impression cytology. Tear MUC5AC concentration was quantified using enzyme-linked immunoassay, and conjunctival goblet cell density was counted after Periodic-acid Schiff staining. Tear MUC5AC concentration had significant positive correlation with conjunctival goblet cell density (r = 0.181, P = 0.03). In current smokers, Schirmer I test value, goblet cell density and tear MUC5AC concentration were significantly lower than non-smokers. Pack-years of smoking have significant negative correlation with goblet cell density (r = -0.174, P = 0.036) and tear MUC5AC concentration (r = -0.183, P = 0.028). We concluded that smoking might decrease tear secretion, goblet cell density and tear MUC5AC concentration. In addition, MUC5AC concentration in tears depends on goblet cell density in the conjunctiva among office workers.

Radiologists perform many 'visual search tasks' in which they look for one or more instances of one or more types of target item in a medical image (e.g. cancer screening). To understand and improve how radiologists do such tasks, it must be understood how the human 'search engine' works. This article briefly reviews some of the relevant work into this aspect of medical image perception. Questions include how attention and the eyes are guided in radiologic search? How is global (image-wide) information used in search? How might properties of human vision and human cognition lead to errors in radiologic search?