Metabolic bone disease of prematurity is characterised by disordered bone mineralisation and is therefore an increased fracture risk. Preterm infants are especially at risk due to incomplete in utero bone accretion during the last trimester. Currently, diagnosing metabolic bone disease mainly relies on biochemistry and radiographs. Dual-energy x-ray absorptiometry and quantitative ultrasound (US) are used less frequently. However, biochemical measurements correlate poorly with bone mineralisation and although scoring systems exist for metabolic bone disease, radiographs are subjective and do not detect early features of osteopenia. Dual energy x-ray absorptiometry is the reference standard for determining bone density in older children and adults. However, challenges with this method include movement artefact, difficulty scanning small and sick infants and a lack of normative data for young children. Quantitative US has a relatively low cost, is radiation-free and portable, and may hence be suitable for assessing bone status in preterm infants. This review aims to provide an overview of the use of quantitative US in detecting metabolic bone disease in preterm infants.
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http://dx.doi.org/10.1007/s00247-018-4161-5 | DOI Listing |
J Vis Exp
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Barts Cancer Institute, Queen Mary University of London;
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Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.
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Department of Anatomy and Cell Biology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
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View Article and Find Full Text PDFFASEB J
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Department of Orthopedic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China.
Nonunion is a significant complication in fracture management for surgeons. Salvianolic acid A (SAA), derived from the traditional Chinese plant Salviae miltiorrhizae Bunge (Danshen), exhibits notable anti-inflammatory and antioxidant properties. Although studies have demonstrated its ability to promote osteogenic differentiation, the exact mechanism of action remains unclear.
View Article and Find Full Text PDFPhysiol Rep
February 2025
Motion and Exercise Science, University of Stuttgart, Stuttgart, Germany.
The maintenance of an appropriate ratio of body fat to muscle mass is essential for the preservation of health and performance, as excessive body fat is associated with an increased risk of various diseases. Accurate body composition assessment requires precise segmentation of structures. In this study we developed a novel automatic machine learning approach for volumetric segmentation and quantitative assessment of MRI volumes and investigated the efficacy of using a machine learning algorithm to assess muscle, subcutaneous adipose tissue (SAT), and bone volume of the thigh before and after a strength training.
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