Editorial — Glaucomatous Optic Neuropathy: The Dark Side of the Moon

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Intraorbital Steroid Injection for Active Thyroid Ophthalmopathy

Purpose: To evaluate the effect of orbital steroid injections in patients with active thyroid ophthalmopathy resistant to or dependent on systemic steroids, or with complications related to systemic steroid use. Methods: This prospective non-comparative case series includes 31 eyes of 17 patients with active thyroid ophthalmopathy and clinical activity score (CAS) of 3 or more, without compressive optic neuropathy or overt exposure keratopathy. All subjects had a history of previous systemic steroid use (with steroid resistance or dependence) or had developed complications related to steroids. A combination of steroids including triamcinolone acetonide 20 mg and dexamethasone 4 mg was injected in the upper and lower retroseptal orbital spaces three or four times at one-month intervals. The patients were examined periodically after each injection and at least three months after the last injection. Results: Mean pre-injection CAS was 5.2 ± 1.3 which was improved to 1.6 ± 1 after the fourth injection (P < 0.001). Upper and lower lid retraction improved in 100% and 68.2% of the affected eyes, respectively. Strabismus completely resolved in one of five affected patients and the most significant improvement was observed in supraduction. Mean improvement in exophthalmos was 1.2 ± 1.1 mm. Visual acuity did not significantly change after the injections. Eyelid ecchymosis and/or subconjunctival hemorrhage was observed in 7.1% and intraocular pressure rise occurred in 8.8% of eyes. Conclusion: Orbital steroid injections can be used for the treatment of active thyroid ophthalmopathy when the patient is resistant to or dependent on systemic steroids or has developed complications of systemic steroids

Bilateral Terrien’s Marginal Degeneration and Posterior Polymorphous Dystrophy in a Patient with Rheumatoid Arthritis

Purpose: To report an interesting case of concomitant bilateral Terrien′s marginal degeneration-like corneal ectasia and posterior polymorphous corneal dystrophy in a young man with quiescent rheumatoid arthritis. Case Report: A 24-year-old man with history of rheumatoid arthritis presented with bilateral decreased vision since four years ago. Slit lamp examination revealed bilateral circumferential peripheral corneal thinning and bulging with vascularization and lipid deposition in addition to band-like lesions in descemet′s membrane. Previous records revealed no gross corneal abnormalities up to 4 years ago. Corneal lesions were compatible with bilateral circumferential Terrien′s marginal degeneration concomitant with posterior polymorphous dystrophy. Conclusion: To our knowledge, this is the first report of concomitant bilateral Terrien′s marginal degeneration with peripheral corneal ectasia and posterior polymorphous dystrophy in a patient with rheumatoid arthritis. Bilateral circumferential involvement, younger age at presentation and total peripheral corneal ectasia as observed in this case are not typical for classic Terrien′s marginal degeneration

Editorial – Eye Disorders in the Post-COVID Era

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A new intelligent estimation method based on the Cascade Forward Neural Network for the Electric and Magnetic fields in the vicinity of the High Voltage Overhead Transmission Lines

The evaluation and estimation of the electric and magnetic field (EMF) intensity in the vicinity of overhead transmission lines (OHTL) is of paramount importance for residents’ healthcare and industrial monitoring purposes. Using artificial intelligence (AI) techniques makes researchers able to estimate EMF with extremely high accuracy in a significantly short time. In this paper, two models based on the Artificial Neural Network (ANN) have been developed for estimating electric and magnetic fields, i.e., feed-forward neural network (FFNN) and cascade-forward neural network (CFNN). By performing the sensitivity analysis on controlling/hyper-parameters of these two ANN models, the best setup resulting in the highest possible accuracy considering their response time has been chosen. Overall, the CFNN achieved a significant 56% reduction in Root Mean Squared Error (RMSE) for the electric field and a 5% reduction for the magnetic field, compared to the FFNN. This indicates that the CFNN model provided more accurate predictions, particularly for the electric field than the proposed methods in other recent works, making it a promising choice for this application. When the model is trained, it will be tested by a different dataset. Then, the accuracy and response time of the model for new data points of that layout will be evaluated through this process. The model can predict the fields with an accuracy near 99.999% of the actual values in times under 10 ms. Also, the results of sensitivity analysis indicated that the CFNN models with triple and double hidden layers are the best options for the electric and magnetic field estimation, respectively