A Case of Vogt-Koyanagi-Harada Disease Associated With Polycystic Ovary Syndrome

We report a female patient diagnosed as Vogt-Koyanagi-Harada (VKH) and polycystic ovary syndrome (PCOS). She has diagnosed as VKH with diminished vision, bilateral serous retinal detachment, the signs of fundus fluorescein angiography and the findings of optical coherence tomography. The patient was referred to the gynecology clinic for her complaints as weight gain, hirsutismus and amenorrhea. She has also been diagnosed with PCOS. With oral steroid treatment, visual acuity has improved and the detachments have resolved within a month. VKH disease may be associated with polycystic ovary syndrome. The two conditions may have a common autoimmune pathogenesis

The effect on wound healing of pazopanib and bevacizumab compared with corticosteroid in experimental glaucoma filtration surgery

AIM: To compare the effects of bevacizumab and pazopanib with corticosteroids on wound healing after trabeculectomy. METHODS: In the study, 35 New Zealand white rabbits were randomly divided into five groups. Apart from the first group, limbus-based trabeculectomy was performed for the eyes of rabbits. No postoperative treatment was administered for group I. Topically administered saline, prednisolone acetate (1%), bevacizumab 5 mg/mL, pazopanib 5 mg/mL for group II, III, IV and V respectively were applied for groups 6h daily for 28d. On day 28 of the experiment, eyes were enucleated and histologically and immunohistochemically analyzed. RESULTS: The fibroblast counts of groups IV and V were determined to be lower than those of groups II and III (P0.05). The immunohistochemical staining intensity of fibroblast growth factor β (FGF-β) and vascular endothelial growth factor (VEGF) was determined to be lower in groups IV and V than in groups II and III (P0.05). The platelet derived growth factor β (PDGF-β) intensity was lower in group V than in groups II, III and IV (P0.05). CONCLUSION: Bevacizumab and pazopanib might be good alternatives of corticosteroid treatment on delaying wound healing in glaucoma surgery

The impact of octreotide in experimental proliferative vitreoretinopathy

Aims: This study aims to investigate the effects of intravitreal octreotide on the growth factors, which have significant roles in the pathogenesis of proliferative vitreoretinopathy (PVR). Settings and Design: An experimental trial. Materials and Methods: 21 guinea pigs were randomly assigned to form 3 groups each including 7 animals. In group 1 (the control group), 0.2 ml saline solution was applied intravitreally in a location of 1.5 mm behind the limbus. In group 2 (the sham group), 0.07 IU dispase in 0.1 ml and 0.1 ml saline solution were applied via the same route. The guinea pigs in group 3 (the treatment group) were applied 0.07 IU dispase in 0.1 ml and 1 mg octreotide in 0.1 ml via the same route. Octreotide injection was applied twice during the period of 10 weeks of the experiment. At the end of the 10 weeks, eyes were enucleated and retinal homogenates were prepared. The platelet derivated growth factor (PDGF), insulin-like growth factor (IGF 1) and transforming growth factor (TGF ß) levels in homogenized retina tissue were measured by Enzyme Linked-Immuno-Sorbent Assay (ELISA) method. Statistical Analysis Used: Kruskal-Wallis variance analysis and Mann-Whitney U test. Results: In the treatment group, a significant decrease was observed in retinal PDGF levels (P 0.05). Conclusions: Intravitreally applied octreotide at a dose of 1 mg has a highly strong effect on PDGF. This study suggests that intravitreal octreotide may suppress PVR development and that octreotide may merit investigation for PVR prophylaxis

Comparison Of Midazolam, Propofol And Fentanyl Combinations Fair Sedation And Hemodynamic Parameters In Cataract Extraction

Objective: Midazolam, propofol and fentanyl were compared in terms of sedation during cataract extraction. Hemodynamic parameters, sedation level, postoperative satisfaction, and side effects were investigated. Methods: The study was carried out in Hacettepe University Hospitals Ophthalmology Operating Theatres in 2005. The patients received only midazolam (0.02 mg kg(-1)), or midazolam (0.02 mg kg(-1)) + propofol (0.2 mg kg(-1)), or midazolam (0.02 mg kg(-1)) + fentanyl (1 microgram kg(-1)), or midazolam (0.02 mg kg(-1)) + propofol (0.2 mg kg-1) + fentanyl (I microgram kg(-1)). The sedation level of patients was measured according to a 'physician questionnaire'. Postoperative nausea/vomiting, headache, and patient satisfaction were determined via a 'patient, s evaluation scale'. Results: In the groups receiving fentanyl, the hemodynamic response to peribulbar block insertion was minimal (P < 0.05) and the sedation level was best (p < 0.05). Respiration rate and O-2 saturation of patients receiving midazolam, propofol, and fentanyl decreased after sedation (p < 0.01) and postoperative satisfaction was high in this group (p < 0.01). Patients in the midazolarn group showed a prominent reaction to peribulbar block insertion while movement during the operation was obvious (P < 0.05). Conclusion: The combination of midazolam, propofol, and fentanyl should be preferred to other study groups as the sedation level is suitable for cataract extraction with high postoperative patient satisfaction and without any side effects.Wo

Automated Diabetic Retinopathy Detection Using Horizontal and Vertical Patch Division-Based Pre-Trained DenseNET with Digital Fundus Images

Diabetic retinopathy (DR) is a common complication of diabetes that can lead to progressive vision loss. Regular surveillance with fundal photography, early diagnosis, and prompt intervention are paramount to reducing the incidence of DR-induced vision loss. However, manual interpretation of fundal photographs is subject to human error. In this study, a new method based on horizontal and vertical patch division was proposed for the automated classification of DR images on fundal photographs. The novel sides of this study are given as follows. We proposed a new non-fixed-size patch division model to obtain high classification results and collected a new fundus image dataset. Moreover, two datasets are used to test the model: a newly collected three-class (normal, non-proliferative DR, and proliferative DR) dataset comprising 2355 DR images and the established open-access five-class Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 dataset comprising 3662 images. Two analysis scenarios, Case 1 and Case 2, with three (normal, non-proliferative DR, and proliferative DR) and five classes (normal, mild DR, moderate DR, severe DR, and proliferative DR), respectively, were derived from the APTOS 2019 dataset. These datasets and these cases have been used to demonstrate the general classification performance of our proposal. By applying transfer learning, the last fully connected and global average pooling layers of the DenseNet201 architecture were used to extract deep features from input DR images and each of the eight subdivided horizontal and vertical patches. The most discriminative features are then selected using neighborhood component analysis. These were fed as input to a standard shallow cubic support vector machine for classification. Our new DR dataset obtained 94.06% and 91.55% accuracy values for three-class classification with 80:20 hold-out validation and 10-fold cross-validation, respectively. As can be seen from steps of the proposed model, a new patch-based deep-feature engineering model has been proposed. The proposed deep-feature engineering model is a cognitive model, since it uses efficient methods in each phase. Similar excellent results were seen for three-class classification with the Case 1 dataset. In addition, the model attained 87.43% and 84.90% five-class classification accuracy rates using 80:20 hold-out validation and 10-fold cross-validation, respectively, on the Case 2 dataset, which outperformed prior DR classification studies based on the five-class APTOS 2019 dataset. Our model attained about &gt;2% classification results compared to others. These findings demonstrate the accuracy and robustness of the proposed model for classification of DR images