Background:The aim of this study was to identify the incidence of severe retinopathy of prematurity (ROP) requiring laser treatment and the overall incidence of ROP in extremely low birth weight (ELBW) infants, and to evaluate the associated risk fac...

The central retinal artery occlusion (CRAO) is a rare ophthalmological emergency that can occur in the eye. CRAO can affect persons of any age, however it is most common in people over the age of 60. CRAO is associated with a number of risk factors, including giant cell arteritis, carotid artery atherosclerosis, cardiogenic emboli, hypertension, smoking, diabetes, and thromboembolic disease. The chance of each of these etiologies being present is assessed during the course of the investigation. Hyperbaric oxygen treatment (HBOT) is classified by the American Heart Association for CRAO at level IIb. In accordance with that, HBOT might be considered for the treatment of such a severe condition. HBOT can maintain retinal oxygenation during ischemic events by allowing oxygen to diffuse through choroidal capillaries that have been exposed to elevated partial pressures of oxygen. As a result, ischemia-related damage is reversed if applied within proper time frame. The amount of time that has passed prior to initiation of HBOT is considered to be the most critical factor in determining the best visual prognosis. According to the Undersea and Hyperbaric Medical Society, patients who are identified with CRAO after the onset of symptoms should be evaluated for HBOT within 24 hours. HBOT has the advantage of having a low risk profile, and it can be utilized to improve visual outcomes in proper patients.

The purpose of the present study was to assess the role of hedgehog signaling pathway in the carcinogenesis of eyelid skin and conjunctival epithelial malignant tumors. The study was conducted on specimens from 41 patients with cutaneous eyelid basal cell carcinoma, 22 with bulbar conjunctival squamous cell carcinoma, 12 with bulbar conjunctival intraepithelial neoplasia. Major molecules of Hedgehog signaling pathway (Sonic Hedgehog [Shh] and Patched-1 [Ptch-1] and Glioma-associated oncogene [Gli-1]) were evaluated in paraffin-embedded tissue specimens using immunohistochemical staining. For each specimen, the percentage (50%) and the intensity of the immunohistochemical staining (graded from 0 to 3) were calculated and the scores obtained by multiplication of two values were analyzed using the Kruskall-Wallis test. Shh and Ptch-1 expression levels were statistically significantly lower in the basal cell carcinoma group compared with the squamous cell carcinoma group (P=0.043 for Shh; P=0.030 for Ptch-1). In the conjunctival squamous cell carcinoma group, the Ptch-1 score was 0 in ~25% of specimens and the Gli-1 score was ≤2 in ~45% of cases. In the conjunctival intraepithelial neoplasia group, the Ptch-1 score was ≥2 in 66% of specimens, the Gli-1 score was ≤2 in ~92% of cases. Ptch-1 mutations contribute to the development of cutaneous eyelid basal cell carcinoma. The present study provides evidence that alterations in hedgehog signaling pathways may lead to transformation of the conjunctival intraepithelial neoplasia into invasive squamous cell carcinoma.

Purpose: The aim of the study is to improve the accuracy of age related macular degeneration (AMD) disease in its earlier phases with proposed Capsule Network (CapsNet) architecture trained on speckle noise reduced spectral domain optical coherence tomography (SD-OCT) images based on an optimized Bayesian non-local mean (OBNLM) filter augmentation techniques. Methods: A total of 726 local SD-OCT images were collected and labelled as 159 drusen, 145 dry AMD, 156 wet AMD and 266 normal. Region of interest (ROI) was identified. Speckle noise in SD-OCT images were reduced based on OBNLM filter. The processed images were fed to proposed CapsNet architecture to clasify SD-OCT images. Accuracy rates were calculated in both public and local dataset. Results: Accuracy rate of local SD-OCT image dataset classification was achieved to a value of 96.39% after performing data augmentation and speckle noise reduction with OBNLM. The performance of proposed CapsNet was also evaluated on the public Kaggle dataset under the same processing procedures and the accuracy rate was calculated as 98.07%. The sensitivity and specificity rates were 96.72% and 99.98%, respectively. Conclusions: The classification success of proposed CapsNet may be improved with robust pre-processing steps like; determination of ROI and denoised SD-OCT images based on OBNLM. These impactful image preprocessing steps yielded higher accuracy rates for determining different types of AMD including its precursor lesion on the both local and public dataset with proposed CapsNet architecture.