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皮质骨对机械加载的适应性的可反转数学模型。

An Invertible Mathematical Model of Cortical Bone's Adaptation to Mechanical Loading.

机构信息

Department of Mechanical Engineering, Indian Institute of Technology Ropar, Nangal Road Rupnagar, Punjab, 140001, India.

出版信息

Sci Rep. 2019 Apr 10;9(1):5890. doi: 10.1038/s41598-019-42378-5.

DOI:10.1038/s41598-019-42378-5
PMID:30971812
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6458131/
Abstract

Determination of mechanical loading regimen that would induce a prescribed new bone formation rate and its site-specific distribution, may be desirable to treat some orthopaedic conditions such as bone loss due to muscle disuse, e.g. because of space flight, bed-rest, osteopenia etc. Site-specific new bone formation has been determined earlier experimentally and numerically for a given loading regimen; however these models are mostly non-invertible, which means that they cannot be easily inverted to predict loading parameters for a desired new bone formation. The present work proposes an invertible model of bone remodeling, which can predict loading parameters such as peak strain, or magnitude and direction of periodic forces for a desired or prescribed site-specific mineral apposition rate (MAR), and vice versa. This fast, mathematical model has a potential to be developed into an important aid for orthopaedic surgeons for prescribing exercise or exogenous loading of bone to treat bone-loss due to muscle disuse.

摘要

确定机械加载方案,以诱导规定的新骨形成率及其特定部位的分布,可能是治疗某些骨科疾病的理想方法,例如因肌肉废用导致的骨质流失,例如太空飞行、卧床休息、骨质疏松症等。已经通过实验和数值方法确定了特定部位的新骨形成率,对于给定的加载方案;然而,这些模型大多是不可逆的,这意味着它们不能轻易地反转来预测所需新骨形成的加载参数。本工作提出了一种可反转的骨重建模型,该模型可以预测加载参数,如峰值应变,或周期性力的幅度和方向,以达到所需或规定的特定部位的矿化沉积率(MAR),反之亦然。这种快速的数学模型有可能成为骨科医生的一个重要辅助工具,用于规定骨骼的运动或外源性加载,以治疗因肌肉废用导致的骨质流失。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/baa8/6458131/68f99c0f2173/41598_2019_42378_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/baa8/6458131/cc7192a155d1/41598_2019_42378_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/baa8/6458131/ddbb3b57470d/41598_2019_42378_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/baa8/6458131/aa8974bf0e9b/41598_2019_42378_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/baa8/6458131/bca46177e845/41598_2019_42378_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/baa8/6458131/57259b684a86/41598_2019_42378_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/baa8/6458131/12014178a981/41598_2019_42378_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/baa8/6458131/82bd49e19e4a/41598_2019_42378_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/baa8/6458131/68f99c0f2173/41598_2019_42378_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/baa8/6458131/cc7192a155d1/41598_2019_42378_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/baa8/6458131/ddbb3b57470d/41598_2019_42378_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/baa8/6458131/aa8974bf0e9b/41598_2019_42378_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/baa8/6458131/bca46177e845/41598_2019_42378_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/baa8/6458131/57259b684a86/41598_2019_42378_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/baa8/6458131/12014178a981/41598_2019_42378_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/baa8/6458131/82bd49e19e4a/41598_2019_42378_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/baa8/6458131/68f99c0f2173/41598_2019_42378_Fig8_HTML.jpg

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