Purpose: To determine the sensitivity and specificity of cardiac gated electron-beam computed tomography (CT) and ungated helical CT in detecting and quantifying coronary arterial calcification (CAC) by using a working heart phantom and artificial coronary arteries.
Materials And Methods: A working heart phantom simulating normal cardiac motion and providing attenuation equal to that of an adult thorax was used. Thirty tubes with a 3-mm inner diameter were internally coated with pulverized human cortical bone mixed with epoxy glue to simulate minimal (n = 10), mild (n = 10), or severe (n = 10) calcified plaques. Ten additional tubes were not coated and served as normal controls. The tubes were attached to the same location on the phantom heart and scanned with electron-beam CT and helical CT in horizontal and vertical planes. Actual plaque calcium content was subsequently quantified with atopic spectroscopy. Two blinded experienced radiologic imaging teams, one for each CT system, separately measured calcium content in the model vessels by using a Hounsfield unit threshold of 130 or greater.
Results: The sensitivity and specificity of electron-beam CT in detecting CAC were 66.1% and 80.0%, respectively. The sensitivity and specificity of helical CT were 96.4% and 95.0%, respectively. Electron-beam CT was less reliable when vessels were oriented vertically (sensitivity and specificity, 71.4% and 70%; 95% CI: 39.0%, 75.0%) versus horizontally (sensitivity and specificity, 60.7% and 90.0%; 95% CI: 48.0%, 82.0%). When a correction factor was applied, the volume of calcified plaque was statistically better quantified with helical CT than with electron-beam CT (P =.004).
Conclusion: Ungated helical CT depicts coronary arterial calcium better than does gated electron-beam CT. When appropriate correction factors are applied, helical CT is superior to electron-beam CT in quantifying coronary arterial calcium. Although further work must be done to optimize helical CT grading systems and scanning protocols, the data of this study demonstrated helical CT's inherent advantage over currently commercially available electron-beam CT systems in CAC detection and quantification.
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JMIR Ment Health
December 2024
Otsuka Pharmaceutical Development & Commercialization, Inc, 508 Carnegie Center Drive, Princeton, NJ, 08540, United States, 1 609 535 9035.
Background: Sleep-wake patterns are important behavioral biomarkers for patients with serious mental illness (SMI), providing insight into their well-being. The gold standard for monitoring sleep is polysomnography (PSG), which requires a sleep lab facility; however, advances in wearable sensor technology allow for real-world sleep-wake monitoring.
Objective: The goal of this study was to develop a PSG-validated sleep algorithm using accelerometer (ACC) and electrocardiogram (ECG) data from a wearable patch to accurately quantify sleep in a real-world setting.
Mycoses
January 2025
Department of Dermatology and Venereology, Peking University First Hospital, Beijing, China.
Objectives: Tinea capitis remains a common fungal infection in children worldwide. Species identification is critical for determining the source of infection and reducing transmission. In conventional methods, macro- and microscopic analysis is time-consuming and results in slow fungal growth or low specificity.
View Article and Find Full Text PDFJMIR Form Res
December 2024
thymia, International House, 64 Nile Street, London, N1 7SR, United Kingdom, 44 7477285252.
Background: Anxiety and depression represent prevalent yet frequently undetected mental health concerns within the older population. The challenge of identifying these conditions presents an opportunity for artificial intelligence (AI)-driven, remotely available, tools capable of screening and monitoring mental health. A critical criterion for such tools is their cultural adaptability to ensure effectiveness across diverse populations.
View Article and Find Full Text PDFWorld J Clin Cases
December 2024
Faculty of Medicine, Alatoo International University, Bishkek 720048, Kyrgyzstan.
Background: Obesity and type 2 diabetes mellitus (T2DM) are frequent co-occurring disorders that affect regular metabolic functions. Obesity has also been linked to an increased risk of developing diabetes. Obesity and diabetes are on the rise, increasing healthcare costs and raising mortality rates.
View Article and Find Full Text PDFFront Nutr
December 2024
Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock, Germany.
Introduction: Disease-related malnutrition is common but often underdiagnosed in patients with chronic gastrointestinal diseases, such as liver cirrhosis, short bowel and intestinal insufficiency, and chronic pancreatitis. To improve malnutrition diagnosis in these patients, an evaluation of the current Global Leadership Initiative on Malnutrition (GLIM) diagnostic criteria, and possibly the implementation of additional criteria, is needed.
Aim: This study aimed to identify previously unknown and potentially specific features of malnutrition in patients with different chronic gastrointestinal diseases and to validate the relevance of the GLIM criteria for clinical practice using machine learning (ML).
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