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相关概念视频

Bone Remodeling01:40

Bone Remodeling

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Bone remodeling is a continuous and balanced process of bone resorption by osteoclasts and bone formation by osteoblasts. In adults, it helps maintain bone mass and calcium homeostasis. While mechanical stress can stimulate turnover as part of the normal maintenance and reparative process, several hormones also regulate bone remodeling.
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Bone Disorders01:29

Bone Disorders

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Aging and its effect on bone remodeling is the most common cause of bone disorders. In young and healthy people, bone deposition and resorption happen at an equal rate to maintain optimal bone health.
Bone deposition is also affected by the levels of sex hormones like estrogen and testosterone that promote osteoblast activity and bone matrix synthesis. When the level of these hormones decreases due to aging, it causes a reduction in bone deposition. As a result, bone resorption by osteoclasts...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

48
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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相关实验视频

Updated: Jun 21, 2025

Author Spotlight: Advanced Techniques for Characterizing Tissue Mineralization in Bone Regeneration Research
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Author Spotlight: Advanced Techniques for Characterizing Tissue Mineralization in Bone Regeneration Research

Published on: September 27, 2024

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模拟未来的骨矿物质密度:简单还是复杂?

E Erjiang1, John J Carey2, Tingyan Wang3

  • 1School of Management, Guangxi Minzu Univeristy, Nanning, China.

Bone
|July 7, 2024
PubMed
概括
此摘要是机器生成的。

预测骨质疏松性骨折是一个挑战. 新的深度学习模型在预测骨矿物质密度 (BMD) 变化方面表现有希望,可能改善骨质疏松症管理和患者的治疗结果.

关键词:
骨矿物质密度 骨矿物质密度决策 决策是做出决定的.深度学习是一种深度学习.纵向监测 纵向监测 纵向监测 纵向监测骨质疏松症是一种骨质疏松症.这就是Z-score.

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Scanning Skeletal Remains for Bone Mineral Density in Forensic Contexts
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Scanning Skeletal Remains for Bone Mineral Density in Forensic Contexts

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Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
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Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

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相关实验视频

Last Updated: Jun 21, 2025

Author Spotlight: Advanced Techniques for Characterizing Tissue Mineralization in Bone Regeneration Research
07:29

Author Spotlight: Advanced Techniques for Characterizing Tissue Mineralization in Bone Regeneration Research

Published on: September 27, 2024

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Scanning Skeletal Remains for Bone Mineral Density in Forensic Contexts
07:56

Scanning Skeletal Remains for Bone Mineral Density in Forensic Contexts

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Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
09:32

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

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科学领域:

  • 老年学是一门学科.
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 骨质疏松性骨折对全球健康造成重大负担,影响患者的福祉和医疗保健成本.
  • 骨矿物质密度 (BMD) 评估对于识别骨质疏松症和骨折风险至关重要.
  • 目前的临床算法缺乏有效的方法来将随时间的BMD变化纳入骨折风险预测中.

研究的目的:

  • 将统计方法 (ZBM) 与基于深度学习 (DL) 的方法进行比较,以预测未来的骨矿物质密度 (BMD).
  • 通过使用纵向DXA数据,评估这些模型在预测BMD方面的性能.

主要方法:

  • 分析了来自2948名成年人 (40-90岁) 的纵向DXA数据,其中至少有两次部扫描.
  • 一个ZBM模型使用参考组数据和最新的扫描预测了未来的BMD.
  • 一种基于DL的方法结合了历史DXA数据,ZBM特征和多维纵向变量.

主要成果:

  • 深度学习模型,特别是混合DL,在女性中显著优于ZBM模型.
  • 在男性中,基于ZBM的模型的表现与基于DL的模型相比或更好.
  • 这项研究包括2652名女性 (90%) 和296名男性 (10%).

结论:

  • 基于DL和统计模型都可以使用纵向临床数据预测未来的BMD.
  • 这些预测模型可以提高骨质疏松症评估的临床决策,包括重复BMD测试的时间.