Journal: Biomechanics and Modeling in Mechanobiology

Article Title: Effects of geometric individualisation of a human spine model on load sharing: neuro-musculoskeletal simulation reveals significant differences in ligament and muscle contribution

doi: 10.1007/s10237-022-01673-3

Figure Lengend Snippet: Thoracolumbar ligament parameters. Pairs of force–strain values at the characteristic points A \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\varepsilon _A,F_A)$$\end{document} ( ε A , F A ) and B \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\varepsilon _B,F_B)$$\end{document} ( ε B , F B ) were determined for every ligament by taking the thoracolumbar average (av.) value or through optimisation (opt.) from Nolte et al. ( 1990 ). Determined forces \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_{A}$$\end{document} F A and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_{B}$$\end{document} F B were divided by three to comply with Mörl et al. ( 2020 ) and divided by the number of threads l . Here, the effective values for every ligament are shown (sum of all threads l representing a ligament)

Article Snippet: For the LF and CAP ligament, the force–strain value pairs A \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\varepsilon _A,F_A)$$\end{document} ( ε A , F A ) and B \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\varepsilon _B,F_B)$$\end{document} ( ε B , F B ) were determined by an optimisation algorithm (MATLAB R2018b, The Mathworks, Natick, MA, USA) in which the optimal regression curve \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {reg}(A,B)$$\end{document} reg ( A , B ) was found to initial literature data from Nolte et al. ( ).

Techniques:

Journal: Biomechanics and Modeling in Mechanobiology

Article Title: Effects of geometric individualisation of a human spine model on load sharing: neuro-musculoskeletal simulation reveals significant differences in ligament and muscle contribution

doi: 10.1007/s10237-022-01673-3

Figure Lengend Snippet: Force–strain characteristics for the LF ligament with the optimisation-derived value pairs A \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\varepsilon _A,F_A)$$\end{document} ( ε A , F A ) and B \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\varepsilon _B,F_B)$$\end{document} ( ε B , F B ) indicated

Article Snippet: For the LF and CAP ligament, the force–strain value pairs A \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\varepsilon _A,F_A)$$\end{document} ( ε A , F A ) and B \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\varepsilon _B,F_B)$$\end{document} ( ε B , F B ) were determined by an optimisation algorithm (MATLAB R2018b, The Mathworks, Natick, MA, USA) in which the optimal regression curve \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {reg}(A,B)$$\end{document} reg ( A , B ) was found to initial literature data from Nolte et al. ( ).

Techniques: Derivative Assay