Application of Deep Learning Algorithms Based on the Multilayer Y0L0v8 Neural Network to Identify Fungal Keratitis.

Unlabelled: is to develop a method for diagnosing fungal keratitis based on the analysis of photographs of the anterior segment of the eye using deep learning algorithms with subsequent evaluation of sensitivity and specificity of the method on a test data set in comparison with the results of practicing ophthalmologists.

Materials And Methods: The study has included the stages of data acquisition, image pre-training and markup, selection of training approach and neural network architecture, training with input data augmentation, validation with hyperparameter correction, evaluation of algorithm performance on a test sample, and determination of sensitivity and specificity of fungal keratitis detection by practicing doctors. A total of 274 anterior segment images were used, including 130 photographs of the eyes affected by fungal keratitis and 144 photographs illustrating normal eyes, keratitis of other etiologies, and various anterior segment pathologies.

The opportunities and challenges of telemedicine during COVID-19 pandemic.

At the end of 2019, patients with pneumonia of unknown etiology appeared in the city of Wuhan (China). After a short time, this infection affected not only the people of China but also the whole world. On March 11, 2020, the World Health Organization declared the disease a pandemic.

Differential non-coding RNAs expression profiles of invasive and non-invasive pituitary adenomas.

Background: Pituitary adenoma (PA) accounts for 10-15% of all intracranial neoplasms. Despite their benign nature, PA often shows invasive growth. MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are a class of non-coding RNAs that play important roles in PA initiation and progression.