Plateau Iris: A Review.

Purpose: To review the concept of plateau iris and summarize the recent evidence on its diagnosis and management. Methods: This is a narrative review on the plateau iris. A literature review was conducted in PubMed, Google Scholar, and Scopus databases using keywords: angle-closure glaucoma, glaucoma, nonpupillary block glaucoma, plateau iris, and plateau iris management. Results: This review defined the current knowledge about plateau iris. First of all, the anatomy and epidemiology were discussed. Then, we outlined the available evidence on the diagnosis of plateau iris and its differential diagnosis. Conclusively, the treatment options were mentioned. Conclusions: Plateau iris is a condition in which nonpupillary block mechanisms are responsible for intraocular pressure elevation and angle closure attack when a patent peripheral iridotomy has removed the relative pupillary block. An anteriorly positioned ciliary body causes mechanical obstruction of trabecular meshwork in these patients. It is usually seen in younger patients with angle closure and is diagnosed by gonioscopic examination and imaging modalities such as Ultrasound biomicroscopy. Despite the known mechanism of plateau iris, there is no consensus over treatment. Low-dose pilocarpine and Argon laser peripheral iridoplasty are nonsurgical treatments for these patients, but their effects are short-term. Cataract extraction with/without endocyclophotocoagulation (ECP), endocycloplasty, excisional goniotomy, and transscleral cyclophotocoagulation are alternative treatments. Patients should be examined periodically for further progression or recurrence of plateau iris. In cases of glaucoma unresponsive to conventional medical treatments, surgical treatments such as trabeculectomy and drainage devices should be considered.

Machine learning for classification of cutaneous sebaceous neoplasms: implementing decision tree model using cytological and architectural features.

BACKGROUND: This observational study aims to describe and compare histopathological, architectural, and nuclear characteristics of sebaceous lesions and utilized these characteristics to develop a predictive classification approach using machine learning algorithms. METHODS: This cross-sectional study was conducted on Iranian patients with sebaceous tumors from two hospitals between March 2015 and March 2019. Pathology slides were reviewed by two pathologists and the architectural and cytological attributes were recorded. Multiple decision tree models were trained using 5-fold cross validation to determine the most important predictor variables and to develop a simple prediction model. RESULTS: This study assessed the characteristics of 123 sebaceous tumors. Histopathological findings, including pagetoid appearance, neurovascular invasion, atypical mitosis, extensive necrotic area, poor cell differentiation, and non-lobular tumor growth pattern, as well as nuclear features, including highly irregular nuclear contour, and large nuclear size were exclusively observed in carcinomatous tumors. Among non-carcinomatous lesions, some sebaceoma and sebaceous adenoma cases had features like high mitotic activity, which can be misleading and complicate diagnosis. Based on multiple decision tree models, the five most critical variables for lesion categorization were identified as: basaloid cell count, peripheral basaloid cell layers, tumor margin, nuclear size, and chromatin. CONCLUSIONS: This study implemented a machine learning modeling approach to help optimally categorize sebaceous lesions based on architectural and nuclear features. However, studies of larger sample sizes are needed to ensure the accuracy of our suggested predictive model.

One-year outcomes of combined phacoemulsification and viscogoniosynechialysis with and without endoscopic cyclophotocoagulation in primary angle-closure glaucoma.

PURPOSE: To compare the outcomes of combined phacoemulsification and viscogoniosynechialysis (VGSL) with and without endoscopic cyclophtocoagulation (ECP) in primary angle-closure glaucoma (PACG). METHODS: In this prospective interventional case series 52 eyes of 50 patients were enrolled. Twenty-seven eyes underwent combined phacoemulsification and VGSL (PV group) and 25 eyes underwent the same procedure plus circumferential ECP (PVE group). All eyes were followed 1 day, 1 week, 3 months, 6 months, and 1 year after the procedure. Intraocular pressure and antiglaucoma medications were compared between and within groups using generalized estimating equations. Kaplan-Meier survival analysis was carried out to compare intensity to failure between groups. RESULTS: The mean ± SD age was 63.23 ± 6.87 years and 50% of the cases were male in the entire group. Intraocular pressure (IOP) and antiglaucoma medications were significantly reduced at all time points in comparison with the baseline in both groups (p < 0.05). There was no significant difference between groups in either IOP or medications at specific time points (p > 0.05). One eye in each group developed a fibrinous reaction in the postoperative period. There was no statistically significant difference between groups regarding the intensity to failure (P = 0.169). CONCLUSION: There were no significant differences in IOP and medication reduction between groups. Also, the complications were comparable between groups.

Deep Learning-Based Classification of Subtypes of Primary Angle-Closure Disease With Anterior Segment Optical Coherence Tomography.

PRCIS: We developed a deep learning-based classifier that can discriminate primary angle closure suspects (PACS), primary angle closure (PAC)/primary angle closure glaucoma (PACG), and also control eyes with open angle with acceptable accuracy. PURPOSE: To develop a deep learning-based classifier for differentiating subtypes of primary angle closure disease, including PACS and PAC/PACG, and also normal control eyes. MATERIALS AND METHODS: Anterior segment optical coherence tomography images were used for analysis with 5 different networks including MnasNet, MobileNet, ResNet18, ResNet50, and EfficientNet. The data set was split with randomization performed at the patient level into a training plus validation set (85%), and a test data set (15%). Then 4-fold cross-validation was used to train the model. In each mentioned architecture, the networks were trained with original and cropped images. Also, the analyses were carried out for single images and images grouped on the patient level (case-based). Then majority voting was applied to the determination of the final prediction. RESULTS: A total of 1616 images of normal eyes (87 eyes), 1055 images of PACS (66 eyes), and 1076 images of PAC/PACG (66 eyes) eyes were included in the analysis. The mean ± SD age was 51.76 ± 15.15 years and 48.3% were males. MobileNet had the best performance in the model, in which both original and cropped images were used. The accuracy of MobileNet for detecting normal, PACS, and PAC/PACG eyes was 0.99 ± 0.00, 0.77 ± 0.02, and 0.77 ± 0.03, respectively. By running MobileNet in a case-based classification approach, the accuracy improved and reached 0.95 ± 0.03, 0.83 ± 0.06, and 0.81 ± 0.05, respectively. For detecting the open angle, PACS, and PAC/PACG, the MobileNet classifier achieved an area under the curve of 1, 0.906, and 0.872, respectively, on the test data set. CONCLUSION: The MobileNet-based classifier can detect normal, PACS, and PAC/PACG eyes with acceptable accuracy based on anterior segment optical coherence tomography images.

Superficial Keratectomy: A Review of Literature.

Superficial keratectomy (SK) is the manual dissection of the superficial corneal layers (epithelium, Bowman's layer, and sometimes superficial stroma). SK is done using a surgical blade or diamond burr. Some surgeons use intraoperative mitomycin C 0.02% or amniotic membrane transplantation to improve surgical outcomes. This literature review shows that SK remains an effective method for different indications, including tissue diagnosis, excision of corneal degenerations, dystrophies, scarring, recurrent corneal erosions, and retained corneal foreign body.

Delirium risk factors in hospitalized patient: a comprehensive evaluation of underlying diseases and medications in different wards of a large Urban Hospital Center in Iran.

BACKGROUND: Delirium is a neurobehavioral syndrome, which is characterized by a fluctuation of mental status, disorientation, confusion and inappropriate behavior, and it is prevalent among hospitalized patients. Recognizing modifiable risk factors of delirium is the key point for improving our preventive strategies and restraining its devastating consequences. This study aimed to identify and investigate various factors predisposing hospitalized patients to develop delirium, focusing mostly on underlying diseases and medications. METHOD: In a prospective, observational trial, we investigated 220 patients who had been admitted to the internal, emergency, surgery and hematology-oncology departments. We employed the Confusion Assessment Method (CAM) questionnaire, The Richmond Agitation Sedation Scale (RASS), the General Practitioner Assessment of Cognition (GPCOG), demographic questionnaire, patient interviews and medical records. Multivariate logistic regression models were used to analyze the predictive value of medications and underlying diseases for daily transition to delirium.; demographics were analyzed using univariate analysis to identify those independently associated with delirium. RESULTS: Two hundred twenty patients were enrolled; the emergency department had the most incident delirium (31.3%), and the surgery section had the least (2.4%); delirium was significantly correlated with older ages and sleep disturbance. Among multiple underlying diseases and the medications evaluated in this study, we found that a history of dementia, neurological diseases and malignancies increases the odds of transition to delirium and the use of anticoagulants decreases the incident delirium. CONCLUSION: Approximately 1 out of 10 overall patients developed delirium; It is important to evaluate underlying diseases and medications more thoroughly in hospitalized patients to assess the risk of delirium.