Stromal Cell-Derived Factor 1 Polymorphism in Retinal Vein Occlusion

BACKGROUND: Stromal cell-derived factor 1 (SDF1) has crucial role in the regulation of angiogenesis and ocular neovascularisation (NV). The purpose of this study was to evaluate the association between SDF1-3'G(801)A polymorphism and NV complications of retinal vein occlusion (RVO). METHODS: 130 patients with RVO (median age: 69.0, range 35-93 years; male/female- 58/72; 55 patients had central RVO, 75 patients had branch RVO) were enrolled in this study. In the RVO group, 40 (30.8%) patients were diagnosed with NV complications of RVO and 90 (69.2%) patients without NVs. The median follow up period was 40.3 months (range: 18-57 months). The SDF1-3'G(801)A polymorphism was detected by PCR-RFLP. Allelic prevalence was related to reference values obtained in the control group consisted of 125 randomly selected, age and gender matched, unrelated volunteers (median age: 68.0, range 36-95 years; male/female- 53/72). Statistical analysis of the allele and genotype differences between groups (RVO patients vs controls; RVO patients with NV vs RVO patients without NV) was determined by chi-squared test. P value of <0.05 was considered statistically significant. RESULTS: Hardy-Weinberg criteria was fulfilled in all groups. The SDF1-3'G(801)A allele and genotype frequencies of RVO patients were similar to controls (SDF1-3'A allele: 22.3% vs 20.8%; SDF1-3'(801)AA: 5.4% vs 4.8%, SDF1-3'(801)GG: 60.8% vs 63.2%). The frequency of SDF1-3'(801)AA and SDF1-3'(801)GA genotypes, as well as the SDF1-3'(801)A allele frequency were higher in RVO patients with NV versus in patients without NV complication (SDF1-3'(801)AA+AG genotypes: 57.5% vs 31.1%, p = 0.008; SDF1-3'(801)A allele: 35.0% vs 16.7%, p = 0.002) or versus controls (SDF1-3'(801)AA+AG genotypes 57.5% vs 36.8%, p = 0.021; SDF1-3'(801)A allele: 35.0% vs 20.8% p = 0.01). Carrying of SDF1-3'(801)A allele increased the risk of neovascularisation complications of RVO by 2.69 (OR, 95% CI = 1.47-4.93). CONCLUSION: These findings suggest that carrying SDF1-3'(801)A allele plays a role in the development of neovascular complications in retinal vein occlusion

Ocular Application of the Kinin B1 Receptor Antagonist LF22-0542 Inhibits Retinal Inflammation and Oxidative Stress in Streptozotocin-Diabetic Rats

Purpose: Kinin B1 receptor (B1R) is upregulated in retina of Streptozotocin (STZ)-diabetic rats and contributes to vasodilation of retinal microvessels and breakdown of the blood-retinal barrier. Systemic treatment with B 1R antagonists reversed the increased retinal plasma extravasation in STZ rats. The present study aims at determining whether ocular application of a water soluble B1R antagonist could reverse diabetes-induced retinal inflammation and oxidative stress. Methods: Wistar rats were made diabetic with STZ (65 mg/kg, i.p.) and 7 days later, they received one eye drop application of LF22-0542 (1 % in saline) twice a day for a 7 day-period. The impact was determined on retinal vascular permeability (Evans blue exudation), leukostasis (leukocyte infiltration using Fluorescein-isothiocyanate (FITC)-coupled Concanavalin A lectin), retinal mRNA levels (by qRT-PCR) of inflammatory (B1R, iNOS, COX-2, ICAM-1, VEGF-A, VEGF receptor type 2, IL-1b and HIF-1a) and anti-inflammatory (B2R, eNOS) markers and retinal level of superoxide anion (dihydroethidium staining). Results: Retinal plasma extravasation, leukostasis and mRNA levels of B 1R, iNOS, COX-2, VEGF receptor type 2, IL-1b and HIF-1a were significantly increased in diabetic retinae compared to control rats. All these abnormalities were reversed to control values in diabetic rats treated with LF22-0542. B1R antagonist also significantly inhibited the increased production of superoxide anion in diabetic retinae. Conclusion: B1R displays a pathological role in the early stage of diabetes by increasing oxidative stress and proinflammator

The Development of an Age-Structured Model for Trachoma Transmission Dynamics, Pathogenesis and Control

Trachoma is the worldwide leading infectious cause of blindness and is due to repeated conjunctival infection with Chlamydia trachomatis bacteria. The effects of control interventions on population levels of infection and active disease can be promptly measured, but the effects on severe ocular disease outcomes require long-term monitoring. We present a mathematical model of trachoma transmission and disease to predict the impact of interventions on blinding trachoma. The model is based on the concept of multiple re-infections leading to progressive scarring of the eye and the potentially blinding disease sequelae. It includes aspects of trachoma natural history such as an increasing rate of recovery from infection, and a decreasing chlamydial load with subsequent infections. The model reproduces key features of trachoma epidemiology such as the age-profile of infection prevalence; a shift in the prevalence peak toward younger ages in higher-transmission environments; and a rising profile of the prevalence of the severe sequelae (scarring, trichiasis), as well as estimates of the number of infections experienced before these sequelae appear. The model can be used to examine the outcomes of various control strategies on infection and disease and can help to plan treatment interventions for different endemic settings

Identification of Novel Single Nucleotide Polymorphisms in Inflammatory Genes as Risk Factors Associated with Trachomatous Trichiasis

infection, the primary cause of trachoma. Despite control programs that include mass antibiotic treatment, reinfection and recurrence of trachoma are common after treatment cessation. Furthermore, a subset of infected individuals develop inflammation and are at greater risk for developing the severe sequela of trachoma known as trachomatous trichiasis (TT). While there are a number of environmental and behavioral risk factors for trachoma, genetic factors that influence inflammation and TT risk remain ill defined. = 0.001] with the combination of TNFA (-308A), LTA (252A), VCAM1 (-1594C), SCYA 11 (23T) minor allele, and the combination of TNFA (-308A), IL9 (113M), IL1B (5′UTR-T), and VCAM1 (-1594C). However, TT risk increased 13.5 times [odds ratio = 13.5 (95% confidence interval 3.3–22), p = 0.001] with the combination of TNFA (-308G), VDR (intron G), IL4R (50V), and ICAM1 (56M) minor allele.Evaluating genetic risk factors for trachoma will advance our understanding of disease pathogenesis, and should be considered in the context of designing global control programs

Diagnostic techniques for inflammatory eye disease: past, present and future: a review

Investigations used to aid diagnosis and prognosticate outcomes in ocular inflammatory disorders are based on techniques that have evolved over the last two centuries have dramatically evolved with the advances in molecular biological and imaging technology. Our improved understanding of basic biological processes of infective drives of innate immunity bridging the engagement of adaptive immunity have formed techniques to tailor and develop assays, and deliver targeted treatment options. Diagnostic techniques are paramount to distinguish infective from non-infective intraocular inflammatory disease, particularly in atypical cases. The advances have enabled our ability to multiplex assay small amount of specimen quantities of intraocular samples including aqueous, vitreous or small tissue samples. Nevertheless to achieve diagnosis, techniques often require a range of assays from traditional hypersensitivity reactions and microbe specific immunoglobulin analysis to modern molecular techniques and cytokine analysis. Such approaches capitalise on the advantages of each technique, thereby improving the sensitivity and specificity of diagnoses. This review article highlights the development of laboratory diagnostic techniques for intraocular inflammatory disorders now readily available to assist in accurate identification of infective agents and appropriation of appropriate therapies as well as formulating patient stratification alongside clinical diagnoses into disease groups for clinical trials