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  • Biochemical Markers of Osteoporosis - PubMed
    Given these constraints, researchers have investigated alternative tools to enhance osteoporosis management, with bone turnover markers emerging as a key area of interest Bone Turnover Biomarkers Bone turnover markers (BTMs) are byproducts of the bone remodeling process and can be measured in urine or serum
  • Enhancing osteoporosis risk prediction using machine learning . . .
    Osteoporosis (OP), characterized by decreased bone mineral density (BMD) and compromised bone structure, was estimated to have a prevalence worldwide of 18 3 % between 103,334,579 individuals aged 15–105 years [1] Also, osteoporosis-associated fractures affect up to 37 million annually in individuals aged over 55 [2]
  • Discovery of potential biomarkers for osteoporosis diagnosis . . .
    The continuous development of individual- and multi-omics methods is helpful for the exploration and discovery of biomarkers for the diagnosis of osteoporosis Areas covered In this review, we first introduce the epidemiological status of osteoporosis and then describe the pathogenesis of osteoporosis
  • Using machine learning techniques to predict the risk of . . .
    Osteoporosis is a major public health concern that significantly increases the risk of fractures The aim of this study was to develop a Machine Learning based predictive model to screen
  • Laboratory-based Biomarkers for Risk Prediction, Auxiliary . . .
    Purpose of Review Osteoporosis (OP) is characterized by degraded bone microstructure, loss of bone mass and increased risk of fragility fractures Currently, T-score determined by dual-energy X-ray absorptiometry (DEXA) measurements has been regarded as the gold standard for the diagnosis of osteoporosis However, multiple factors have indicated that the T-score is insufficient to identify
  • Identification of combined biomarkers for predicting the risk . . .
    Osteoporosis is a severe chronic skeletal disorder that affects older individuals, especially postmenopausal women However, molecular biomarkers for predicting the risk of osteoporosis are not well characterized The aim of this study was to identify combined biomarkers for predicting the risk of osteoporosis using machine learning methods
  • An updated overview of the search for biomarkers of . . .
    Studies have shown that proteomics can identify new biomarkers for predicting osteoporosis risk and assessing disease progression in the early stages when bone loss has not yet occurred significantly At the same time, the underlying mechanisms of osteoporosis disease can be further understood and refined through proteomics [10] In conclusion





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