ABSTRACT Purpose: To compare the outcomes of intravitreal dexamethasone implant used as either an adjuvant or a switching therapy for diabetic macular edema in patients with poor anatomic response after three consecutive monthly injections of ranibizumab. Methods: This retrospective study included patients with diabetic macular edema who received three consecutive doses of ranibizumab as initial therapy and demonstrated poor response. A single dose of intravitreal de xamethasone implant was administered to these patients. The patients were divided into two groups according to the treatment modalities: the adjuvant therapy group, consisting of patients who continued treatment with ranibizumab injection after receiving intravitreal dexamethasone implant, and the switch therapy group, consisting of patients who were switched from ranibizumab treatment to intravitreal dexamethasone implant as needed. The main outcome measurements were best corrected visual acuity and central retinal thickness at baseline and at 3, 6, 9, and 12 months of follow-up. Results: In this study that included 64 eyes of 64 patients, the best corrected visual acuity and central retinal thickness values did not significantly differ between the groups at baseline and at 6 months of follow-up (p>0.05). However, at 12 months, the best corrected visual acuity values in the adjuvant and switch therapy groups were 0.46 and 0.35 LogMAR, respectively (p=0.012), and the central retinal thickness values were 344.8 and 270.9, respectively (p=0.007). Conclusions: In a real-world setting, it seems more reasonable to use intravitreal dexamethasone implant as a switch therapy rather than an adjuvant therapy for diabetic macula edema refractory to ranibizumab despite three consecutive monthly injections of ranibizumab. Patients switched to intravitreal dexamethasone implant were found to have better anatomic and visual outcomes at 12 months than those who continued ranibizumab therapy despite their less-than-optimal responses. Purpose Methods modalities group needed 3 9 1 followup. followup follow up. up Results p>0.05. p005 p p>0.05 . 0 05 (p>0.05) However 046 46 0.4 035 35 0.3 LogMAR p=0.012, p0012 p=0.012 , 012 (p=0.012) 3448 344 8 344. 2709 270 270.9 p=0.007. p0007 p=0.007 007 (p=0.007) Conclusions realworld real world setting lessthanoptimal less optimal responses p00 p>0.0 (p>0.05 04 4 0. 03 p001 p=0.01 01 (p=0.012 34 27 270. p000 p=0.00 00 (p=0.007 p0 p>0. (p>0.0 p=0.0 (p=0.01 2 (p=0.00 p>0 (p>0. p=0. (p=0.0 p> (p>0 p=0 (p=0. (p> p= (p=0 (p (p=

ABSTRACT Introduction: Presenilin 1 (PSEN1), catalase (CAT), glutathione-S-transferase (GST) and paraoxonase 1 (PON1) play a vital role in prediction, diagnosis and therapy of metabolic disorders. Methods: Metabolic enzyme activities and lipid peroxidation in serum of cerebrovascular diseases (CVD) and coronary artery diseases were measured by spectrophotometric methods. mRNA was isolated from leukocytes of the patient group and healthy adult patients. Quantitative gene expression of PSEN1, CAT and GST mRNA was identified by quantitative real-time polymerase chain reaction (qPCR). Results: The PSEN1, CAT and GST expression in patients showed significant differences compared to the control group. PSEN1 expression in leukocytes was significantly about twice as high as that of the control group in patients with CVD. The GST, CAT and PON1 activity showed significant differences in patient groups compared to the control group. Conclusion: The mRNA expression levels can be used as a potential biomarker in the diagnosis of atherosclerosis that occurs as a result of the metabolic disorder. In atherosclerotic patients, antioxidant status is independently related to an increased risk of cardiovascular events. Antioxidant activities and mRNA expressions may have predictive value, as well as available risk factors. Introduction PSEN , (PSEN1) CAT, (CAT) glutathioneStransferase glutathione S transferase (GST PON (PON1 prediction disorders Methods CVD (CVD methods realtime real time qPCR. qPCR . (qPCR) Results Conclusion disorder events value factors (PSEN1 (CAT (PON (qPCR (PSEN