Lymphatic vessels play a crucial role in systemic immune response and regulation of tissue fluid homeostasis. Corneal lymphangiogenesis in bacterial keratitis has not been studied. In this study, we investigated the mechanism and the role of corneal lymphangiogenesis in a murine bacterial keratitis model using Pseudomonas aeruginosa . We first demonstrated that corneal lymphangiogenesis was enhanced mainly in the late stage of bacterial keratitis, contrary to corneal angiogenesis that started earlier. Corresponding to the delayed lymphangiogenesis, expression of the pro-lymphangiogenic factors VEGF-C and VEGFR-3 increased in the late stage of bacterial keratitis. We further found that F4/80 and CD11b positive macrophages played an essential role in corneal lymphangiogenesis. Notably, macrophages were specifically involved in corneal lymphangiogenesis in the late stage of bacterial keratitis. Finally, we demonstrated the beneficial role of corneal lymphangiogenesis in ameliorating the clinical course of bacterial keratitis. Our study showed that bacterial activity was not directly involved in the late stage of keratitis, while corneal lymphangiogenesis reduced corneal edema and clinical manifestation in the late stage of bacterial keratitis. These findings suggest that the process of lymphangiogenesis in bacterial keratitis ameliorates corneal inflammation and edema in the late stage of bacterial keratitis.
Purpose: Vitreoretinal lymphoma (VRL) is a non-Hodgkin lymphoma of the diffuse large B cell type (DLBCL), which is an aggressive cancer causing central nervous system related mortality. The pathogenesis of VRL is largely unknown. The role of microRNAs (miRNAs) has recently acquired remarkable importance in the pathogenesis of many diseases including cancers. Furthermore, miRNAs have shown promise as diagnostic and prognostic markers of cancers. In this study, we aimed to identify differentially expressed miRNAs and pathways in the vitreous and serum of patients with VRL and to investigate the pathogenesis of the disease. Materials and Methods: Vitreous and serum samples were obtained from 14 patients with VRL and from controls comprising 40 patients with uveitis, 12 with macular hole, 14 with epiretinal membrane, 12 healthy individuals. The expression levels of 2565 miRNAs in serum and vitreous samples were analyzed. Results: Expression of the miRNAs correlated significantly with the extracellular matrix (ECM) 鈥抮eceptor interaction pathway in VRL. Analyses showed that miR-326 was a key driver of B-cell proliferation, and miR-6513-3p could discriminate VRL from uveitis. MiR-1236-3p correlated with vitreous interleukin (IL)-10 concentrations. Machine learning analysis identified miR-361-3p expression as a discriminator between VRL and uveitis. Conclusions: Our findings demonstrate that aberrant microRNA expression in VRL may affect the expression of genes in a variety of cancer-related pathways. The altered serum miRNAs may discriminate VRL from uveitis, and serum miR-6513-3p has the potential to serve as an auxiliary tool for the diagnosis of VRL.
Purpose: 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 applied extensively in studies focused on intraocular diseases. We evaluated whether 5 machine learning algorithms applied to the data of immune-mediator levels in aqueous humor can predict the actual diagnoses of 17 selected intraocular diseases and identified which immune mediators drive the predictive power of a machine learning model.Design: Cross-sectional study.Participants: Five hundred twelve eyes with diagnoses from among 17 intraocular diseases. Methods: 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 5 machine learning algorithms. Stratified k-fold cross-validation was used in evaluation of algorithms with the dataset divided into training and test datasets.Main Outcome Measures: The algorithms were evaluated in terms of precision, recall, accuracy, F-score, area under the receiver operating characteristic curve, area under the precision-recall curve, and mean decrease in Gini index.Results: Among the 5 machine learning models, random forest (RF) yielded the highest classification accuracy in multiclass 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. Random forest recognized vitreoretinal lymphoma, acute retinal necrosis, endophthalmitis, rhegmatogenous retinal detachment, and primary open-angle glaucoma with the top 5 F-scores. The 3 highest-ranking relevant immune mediators were interleukin (IL)-10, interferon-g-inducible protein (IP)-10, and angiogenin for prediction of vitreoretinal lymphoma; monokine induced by interferon g, interferon g, and IP-10 for acute retinal necrosis; and IL-6, granulocyte colony-stimulating factor, and IL-8 for endophthalmitis.Conclusions: Random forest 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 potentially can facilitate diagnosis of intraocular diseases in the future. Ophthalmology 2021;-:1e12
These results suggest that the presence of bacteria is essential and a critical number of bacteria is required for the development of AK. The time of coexistence with bacteria may be an important determinant of the severity of AK.
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