您也可能阅读
通过共同作者、期刊和引用图与本文相关的文章。
Qingling Yang1, Huilin Cheng1, Jing Qin1
1School of Nursing, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China.
一种新的机器学习工具,即临床前骨质疏松症查工具 (POST),可以准确地识别患有骨质疏松症高风险的个体. 这种可访问的查方法有助于预防骨折,并指导临床决策.
06:00Development of a Human Preclinical Model of Osteoclastogenesis from Peripheral Blood Monocytes Co-cultured with Breast Cancer Cell Lines
Published on: September 13, 2017
06:59Author Spotlight: An Economic and Efficient Method for Quantitative Evaluation of Bone Microarchitecture in a Murine Osteoporosis Model
Published on: September 8, 2023
科学领域:
背景情况:
研究的目的:
主要方法:
主要成果:
结论: