In this retrospective study, we investigated vascular abnormalities in sarcoidosis using fluorescein gonioangiography (FGA) to detect angle neovascularization (ANV), fundus fluorescein angiography (FFA), and pathological specimens from the aspects of microangiopathy. In 57 sarcoidosis patients, clinical data was reviewed by dividing the cases into three groups (Group I: histologically diagnosed; Group II: positive bilateral hilar lymphadenopathy (BHL); Group III: negative BHL). The FFA, FGA, and pathological examination data in the autopsy eyes and trabeculectomy specimens were investigated. FGA and FFA detected ANV (91%) and nodule-associated abnormalities (87%), respectively. No intraocular pressure (IOP) elevation was observed after continuous topical betamethasone, except in the steroid responder group. Maximum IOP had significant correlation with nodules in the angle (p = 0.02696) and visual field defect (p = 0.0151). Granulomas adjacent to blood vessels, including the Schlemm’s canal, and thickening of the retinal blood vessel wall caused occlusion of those vessels. Photocoagulation was required for retinal tears (14%) and the retinal blood vessel occlusion (7%). Suppression of IOP elevation via continuous topical betamethasone may be important to avoid irreversible outflow-route changes and optic-nerve damage, and the concept of microangiopathy in ocular sarcoidosis may be important for understanding the proper treatment of serious complications.
In this paper, we consider the high-dimensional consistencies of KOO methods for selecting response variables in multivariate linear regression with covariance structures. Here, the covariance structures are considered as (1) independent covariance structure with the same variance, (2) independent covariance structure with different variances, and (3) uniform covariance structure. A sufficient condition for model selection consistency is obtained using a KOO method under a high-dimensional asymptotic framework, such that sample size n, the number p of response variables, and the number k of explanatory variables are large, as in and , where .
1 October 2012 in Journal of Multivariate Analysis