Optical Coherence Tomography Angiography Parameters in Indian Patients With Central Serous Chorioretinopathy.

Background In this study, we aimed to evaluate optical coherence tomography angiography (OCTA) parameters among Indian patients affected with central serous chorioretinopathy (CSCR). Methodology A cross-sectional study on Indian patients having unilateral or bilateral affection with CSCR was conducted at the Department of Ophthalmology, Guru Nanak Eye Centre, and Maulana Azad Medical College, New Delhi. A history of ocular symptoms such as a diminution of vision, metamorphopsia, decreased contrast sensitivity (CS), and defective color vision (CV) and their duration were obtained.

Use of Deep Learning to Develop and Analyze Computational Hematoxylin and Eosin Staining of Prostate Core Biopsy Images for Tumor Diagnosis.

Importance: Histopathological diagnoses of tumors from tissue biopsy after hematoxylin and eosin (H&E) dye staining is the criterion standard for oncological care, but H&E staining requires trained operators, dyes and reagents, and precious tissue samples that cannot be reused.

Objectives: To use deep learning algorithms to develop models that perform accurate computational H&E staining of native nonstained prostate core biopsy images and to develop methods for interpretation of H&E staining deep learning models and analysis of computationally stained images by computer vision and clinical approaches.

Design, Setting, And Participants: This cross-sectional study used hundreds of thousands of native nonstained RGB (red, green, and blue channel) whole slide image (WSI) patches of prostate core tissue biopsies obtained from excess tissue material from prostate core biopsies performed in the course of routine clinical care between January 7, 2014, and January 7, 2017, at Brigham and Women's Hospital, Boston, Massachusetts.

Digital reconstruction of teeth using near-infrared light.

Cone beam computed tomography has demonstrated value by offering enhanced conceptualization of features of teeth in the 3D space. However, these systems require higher effective radiation doses to image teeth. Previous research from our group has used non-ionizing near-infrared (NIR) light for diagnosing demineralization and caries in human tooth enamel.

Automated Process Incorporating Machine Learning Segmentation and Correlation of Oral Diseases with Systemic Health.

Imaging fluorescent disease biomarkers in tissues and skin is a non-invasive method to screen for health conditions. We report an automated process that combines intraoral fluorescent porphyrin biomarker imaging, clinical examinations and machine learning for correlation of systemic health conditions with periodontal disease. 1215 intraoral fluorescent images, from 284 consenting adults aged 18-90, were analyzed using a machine learning classifier that can segment periodontal inflammation.

Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research.

Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement.

Ethyl methane sulphonate induced genetic variability and heritability in macrosperma and microsperma lentils.

Dry and healthy seeds of two lentil cultivars, LH90-54 (macrosperma) and LH89-48 (microsperma) were treated with three doses of ethyl methane sulphonate (0.1, 0.2 and 0.