Deep-Learning Based Automated Segmentation and Quantitative Volumetric Analysis of Orbital Muscle and Fat for Diagnosis of Thyroid Eye Disease.

Purpose: Thyroid eye disease (TED) is characterized by proliferation of orbital tissues and complicated by compressive optic neuropathy (CON). This study aims to utilize a deep-learning (DL)-based automated segmentation model to segment orbital muscle and fat volumes on computed tomography (CT) images and provide quantitative volumetric data and a machine learning (ML)-based classifier to distinguish between TED and TED with CON. Methods: Subjects with TED who underwent clinical evaluation and orbital CT imaging were included. Patients with clinical features of CON were classified as having severe TED, and those without were classified as having mild TED. Normal subjects were used for controls. A U-Net DL-model was used for automatic segmentation of orbital muscle and fat volumes from orbital CTs, and ensemble of Random Forest Classifiers were used for volumetric analysis of muscle and fat. Results: Two hundred eighty-one subjects were included in this study. Automatic segmentation of orbital tissues was performed. Dice coefficient was recorded to be 0.902 and 0.921 for muscle and fat volumes, respectively. Muscle volumes among normal, mild, and severe TED were found to be statistically different. A classification model utilizing volume data and limited patient data had an accuracy of 0.838 and an area under the curve (AUC) of 0.929 in predicting normal, mild TED, and severe TED. Conclusions: DL-based automated segmentation of orbital images for patients with TED was found to be accurate and efficient. An ML-based classification model using volumetrics and metadata led to high diagnostic accuracy in distinguishing TED and TED with CON. By enabling rapid and precise volumetric assessment, this may be a useful tool in future clinical studies.

Molecular study on the role of vacuolar transporters in glycyrrhetinic acid production in engineered Saccharomyces cerevisiae.

Glycyrrhetinic acid (GA) is one of the major bioactive components of the leguminous plant, Glycyrrhiza spp. (Chinese licorice). Owing to GA's complicated chemical structure, its production by chemical synthesis is challenging and requires other efficient strategies such as microbial synthesis. Earlier investigations employed numerous approaches to improve GA yield by refining the synthetic pathway and improving the metabolic flux. Nevertheless, the metabolic role of transporters in GA biosynthesis in microbial cell factories has not been studied so far. In this study, we investigated the role of yeast ATP binding cassette (ABC) vacuolar transporters in GA production. Molecular docking of GA and its precursors, ß-Amyrin and 11-oxo-ß-amyrin, was performed with five vacuolar ABC transporters (Bpt1p, Vmr1p, Ybt1p, Ycf1p and Nft1p). Based on docking scores, two top scoring transporters were selected (Bpt1p and Vmr1p) to investigate transporters' functions on GA production via overexpression and knockout experiments in one GA-producing yeast strain (GA166). Results revealed that GA and its precursors exhibited the highest predicted binding affinity towards BPT1 (ΔG = -10.9, -10.6, -10.9 kcal/mol for GA, ß-amyrin and 11-oxo-ß-amyrin, respectively). Experimental results showed that the overexpression of BPT1 and VMR1 restored the intracellular as well as extracellular GA production level under limited nutritional conditions, whereas knockout of BPT1 resulted in a total loss of GA production. These results suggest that the activity of BPT1 is required for GA production in engineered Saccharomyces cerevisiae.