Using histogram analysis of T2 values to detect early involvement of extraocular muscles (EOMs) in patients with thyroid-associated ophthalmopathy (TAO). Five EOMs of each orbit were analyzed for 45 TAO patients and 22 healthy controls (HCs). Patients' EOMs were grouped into involved or normal-appearing EOMs (NAEOMs). Histogram parameters and signal intensity ratios (SIRs) of EOMs were compared; receiver operating characteristic (ROC) curve analysis was performed to differentiate NAEOMs from EOMs of HCs. 24 patients were reassessed following immunosuppressive treatment. For SIRs, involved muscles showed higher values than those of NAEOMs and HCs (p < 0.05); there were no differences between NAEOMs and HCs (p = 0.26). Parameters of involved muscles showed no different from those of NAEOMs excluding 25th, 50th percentiles, and standard deviation (SD) (p < 0.05). NAEOMs displayed higher values of 90th, 95th percentiles, SD, skewness, inhomogeneity, and entropy than HCs (p < 0.05). ROC curve analysis of entropy yielded the best area under the ROC curve (AUC; 0.816) for differentiating NAEOMs and HCs. After treatment, histogram parameters including 5th, 75th, 90th, and 95th percentiles, SD, kurtosis, inhomogeneity, and entropy were reduced in NAEOMs (p < 0.05). T2 histogram analysis could detect early involvement of EOMs in TAO prior to detection on conventional orbital MRI.
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http://dx.doi.org/10.1038/s41598-020-76341-6 | DOI Listing |
Sci Rep
January 2025
Department of Electronics and Communication Engineering, Sri Ramakrishna Institute of Technology, Coimbatore, Tamilnadu, India, 641010.
The global spread of COVID-19, particularly through cough symptoms, necessitates efficient diagnostic tools. COVID-19 patients exhibit unique cough sound patterns distinguishable from other respiratory conditions. This study proposes an advanced framework to detect and predict COVID-19 using deep learning from cough audio signals.
View Article and Find Full Text PDFAdv Appl Bioinform Chem
January 2025
Department of Information Technology, Mutah University, Al-Karak, Jordan.
Purpose: The incidence of cancer, which is a serious public health concern, is increasing. A predictive analysis driven by machine learning was integrated with haematology parameters to create a method for the simultaneous diagnosis of several malignancies at different stages.
Patients And Methods: We analysed a newly collected dataset from various hospitals in Jordan comprising 19,537 laboratory reports (6,280 cancer and 13,257 noncancer cases).
Medicine (Baltimore)
November 2024
College of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Utilizing network pharmacology and molecular docking, we evaluated the possible pharmacological mechanism of Danggui Sini Decoction (DGSND) for treating erectile dysfunction (ED). DGSND's chemical components and targets were found utilizing the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Disease-related genes associated with ED were identified through GeneCards, OMIM, TTD, DrugBank, and DisGeNET databases.
View Article and Find Full Text PDFPLoS One
January 2025
Division of Neurology, Department of Medicine, The Ottawa Hospital, Ottawa, Ontario, Canada.
Background: Aeromedical transfer of patients with ischemic stroke to access hyperacute stroke treatment is becoming increasingly common. Little is known about how rapid changes of altitude and atmospheric pressure can impact cerebral perfusion and ischemic burden. In patients with ischemic stroke, there is a theoretical possibility that this physiologic response of hypoxia-driven hyperventilation at higher altitude can lead to a relative drop in PaCO2.
View Article and Find Full Text PDFTech Innov Patient Support Radiat Oncol
March 2025
Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Tokushima 770-8503, Japan.
Purpose: This study aims to compare treatment plans created using RapidPlan and PlanIQ for twelve patients with prostate cancer, focusing on dose uniformity, dose reduction to organs at risk (OARs), plan complexity, and dose verification accuracy. The goal is to identify the tool that demonstrates superior performance in achieving uniform target dose distribution and reducing OAR dose, while ensuring accurate dose verification.
Methods: Dose uniformity in the planning target volume, excluding the rectum, and dose reduction in the OARs (the rectum and bladder) were assessed.
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