We aimed to develop and validate a novel prediction model for osteoporosis based on serotonin, fat-soluble vitamins, and bone turnover markers to improve prediction accuracy of osteoporosis. Postmenopausal women aged 55 to 65 years were recruited and divided into three groups based on DXA (normal, osteopenia, and osteoporosis). A total of 109 participants were included in this study and split into healthy (39/109, 35.8%), osteopenia (35/109, 32.1%), and osteoporosis groups (35/109, 32.1%). Serum concentrations of serotonin, fat-soluble vitamins, and bone turnover markers of participants were measured. Stepwise discriminant analysis was performed to identify efficient predictors for osteoporosis. The prediction model was developed based on Bayes and Fisher’s discriminant functions, and validated via leave-one-out cross-validation. Normal and empirical volume under the receiver operating characteristic (ROC) surface (VUS) tests were used to evaluate predictive effects of variables in the prediction model.Aims
Methods
Receptor activator of nuclear factor-κB ligand (RANKL) is a key molecule that is expressed in bone stromal cells and is associated with metastasis and poor prognosis in many cancers. However, cancer cells that directly express RANKL have yet to be unveiled. The current study sought to evaluate how a single subunit of G protein, guanine nucleotide-binding protein G(q) subunit alpha (GNAQ), transforms cancer cells into RANKL-expressing cancer cells. We investigated the specific role of GNAQ using Aims
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