Outer retinal and renal glomerular functions rely on specialized vasculature maintained by VEGF that is produced by neighboring epithelial cells, the retinal pigment epithelium (RPE) and podocytes, respectively. Dysregulation of RPE- and podocyte-derived VEGF is associated with neovascularization in wet age-related macular degeneration (ARMD), choriocapillaris degeneration, and glomerular thrombotic microangiopathy (TMA). Since complement activation and genetic variants in inhibitory complement factor H (CFH) are also features of both ARMD and TMA, we hypothesized that VEGF and CFH interact. Here, we demonstrated that VEGF inhibition decreases local CFH and other complement regulators in the eye and kidney through reduced VEGFR2/PKC-α/CREB signaling. Patient podocytes and RPE cells carrying disease-associated CFH genetic variants had more alternative complement pathway deposits than controls. These deposits were increased by VEGF antagonism, a common wet ARMD treatment, suggesting that VEGF inhibition could reduce cellular complement regulatory capacity. VEGF antagonism also increased markers of endothelial cell activation, which was partially reduced by genetic complement inhibition. Together, these results suggest that VEGF protects the retinal and glomerular microvasculature, not only through VEGFR2-mediated vasculotrophism, but also through modulation of local complement proteins that could protect against complement-mediated damage. Though further study is warranted, these findings could be relevant for patients receiving VEGF antagonists.

Various immune mediators have crucial roles in the pathogenesis of intraocular diseases. Machine learning can be used to automatically select and weigh various predictors to develop models maximizing predictive power. However, these techniques have not yet been extensively applied studies focused on intraocular diseases. We evaluated whether five machine learning algorithms applied to the data of immune mediator levels in aqueous humor can accurately predict the actual diagnoses of 17 selected intraocular diseases, and identified which immune mediators drive the predictive power of a machine learning model. Cross-sectional study. 512 eyes with diagnoses of 17 intraocular diseases. Aqueous humor samples were collected, and the concentrations of 28 immune mediators were determined using a cytometric bead array. Each immune mediator was ranked according to its importance using five machine learning algorithms: random forest (RF), linear support vector machine (SVM), radial basis function SVM, decision tree and naïve Bayes classifier. Stratified k-fold cross-validation was used in evaluation of algorithms with dataset divided into training and test datasets. The algorithms were evaluated in terms of precision, recall, accuracy, F-score, area under the receiver operating characteristics curve, area under the precision-recall curve and mean decrease in Gini index. Among the five machine learning models, RF yielded the highest classification accuracy in multi-class differentiation of 17 intraocular diseases. The RF prediction models for vitreoretinal lymphoma, acute retinal necrosis, endophthalmitis, rhegmatogenous retinal detachment, and primary open angle glaucoma achieved the highest classification accuracy, precision, and recall. RF recognized vitreoretinal lymphoma, acute retinal necrosis, endophthalmitis, rhegmatogenous retinal detachment and primary open angle glaucoma with the top five F-scores. The three highest-ranking relevant immune mediators were IL-10, IP-10 and angiogenin for prediction of vitreoretinal lymphoma; Mig, IFN-γ and IP-10 for acute retinal necrosis; and IL-6, G-CSF and IL-8 for endophthalmitis. RF algorithms based on 28 immune mediators in aqueous humor successfully predicted the diagnosis of vitreoretinal lymphoma, acute retinal necrosis, and endophthalmitis. Overall, the findings of the present study contribute to increased knowledge on new biomarkers that can potentially facilitate diagnosis of intraocular diseases in the future.

Abnormal subretinal neovascularization is a characteristic of vision-threatening retinal diseases, including macular telangiectasia (MacTel) and retinal angiomatous proliferation (RAP). Subretinal neovascular tufts and photoreceptor dysfunction are observed in very-low-density lipoprotein receptor ( Vldlr ^–/– ) mutant mice. These changes mirror those observed in patients with MacTel and RAP, but the pathogenesis is largely unknown. In this study, we show that retinal microglia were closely associated with retinal neovascular tufts in Vldlr ^–/– mice and retinal tissue from patients with MacTel; ablation of microglia/macrophages dramatically prevented formation of retinal neovascular tufts and improved neuronal function, as assessed by electroretinography. Vldlr ^–/– mice with retinal pigmented epithelium–specific (RPE-specific) Vegfa had greatly reduced subretinal infiltration of microglia/macrophages, subsequently reducing neovascular tufts. These findings highlight the contribution of microglia/macrophages to the pathogenesis of neovascularization, provide valuable clues regarding potential causative cellular mechanisms for subretinal neovascularization in patients with MacTel and RAP and suggest that targeting microglia activation may be a therapeutic option in these diseases.

To compare the clinical findings in patients with anterior uveitis (AU) caused by herpes simplex virus (HSV), varicella zoster virus (VZV), and cytomegalovirus (CMV).

The efficacy of infliximab (IFX) and adalimumab (ADA) for treating Behçet’s syndrome (BS) and sarcoidosis has not been compared adequately. We reviewed the medical records of patients with uveitis ...