Soft-tissue spectral subtraction improves transcutaneous Raman estimates of murine bone strength in vivo.
Soft-tissue spectral subtraction improves transcutaneous Raman estimates of murine bone strength in vivo.
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DOI：
10.1002/jbio.202000256
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发表时间：
2020-11
影响因子：
2.8
通讯作者：
Berger AJ
中科院分区：
文献类型：
--
作者：
Chen K;Massie C;Berger AJ
骨的拉曼光谱的经皮测定是具有挑战性的，因为覆盖软组织中的I型胶原在光谱上与骨中的I型胶原相同。在先前的经皮研究的小鼠tibodies，我们开发了一个基于库的模型，称为SOLD的unmix空间偏移拉曼测量到三个光谱：骨估计，软组织估计，和残差。在这里，我们展示了结合骨估计和残差以产生“顶层减去”（tls）光谱的价值。我们报告了两个标准骨指标（体积骨矿化密度和最大扭矩）的上级预测，使用偏最小二乘回归模型的基础上tls光谱，而不是SOLD骨估计，这意味着光谱残差包含有用的信息。模拟强化了体内实验结果。这种化学计量学方法，我们表示为SOLD/TLS，可能有广泛的适用性，在情况下，综合光谱库是难以获得的。[We使称为SOLD的基于库的模型适应于对体内鼠胫骨的空间偏移拉曼测量进行解混，以产生“顶层扣除”（tls）光谱。报告了使用偏最小二乘回归对tls光谱而不是SOLD光谱进行体积骨矿化密度和最大扭矩的上级预测。模拟表明，当难以获得全面的光谱库时，这种化学计量学方法可能具有广泛的适用性。
Transcutaneous determination of a bone’s Raman spectrum is challenging because the type I collagen in the overlying soft tissue is spectroscopically identical to that in bone. In a previous transcutaneous study of murine tibiae, we developed a library-based model called SOLD to unmix spatially-offset Raman measurements into three spectra: a bone estimate, a soft tissue estimate, and a residual. Here, we demonstrate the value of combining the bone estimate and the residual to produce a “top layer subtracted” (tls) spectrum. We report superior prediction of two standard bone metrics (volumetric bone mineralization density and maximum torque) using partial least squares regression models based upon tls spectra rather than SOLD bone estimates, implying that the spectral residuals contain useful information. Simulations reinforce experimental in vivo findings. This chemometric approach, which we denote as SOLD/TLS, could have broad applicability in situations where comprehensive spectral libraries are difficult to acquire. [We adapt a library-based model called SOLD to unmix spatially-offset Raman measurements of murine tibiae in vivo to produce a "top layer subtracted" (tls) spectrum. Superior prediction of volumetric bone mineralization density and maximum torque using partial least squares regression on tls spectra rather than the SOLD spectra is reported. Simulations indicate this chemometric approach could have broad applicability when comprehensive spectral libraries are difficult to acquire.]
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影响因子：
46.2
作者：
Pence I;Mahadevan-Jansen A
通讯作者：
Mahadevan-Jansen A
影响因子：
3.5
作者：
Maher, Jason R.;Takahata, Masahiko;Berger, Andrew J.
通讯作者：
Berger, Andrew J.
影响因子：
4
作者：
Dall'Ara, E.;Pahr, D.;Zysset, P.
通讯作者：
Zysset, P.
影响因子：
6.2
作者：
Burge, Russel;Dawson-Hughes, Bess;Tosteson, Anna
通讯作者：
Tosteson, Anna
影响因子：
2.8
作者：
MARKBREITER, LA;PELKER, RR;PANJABI, MM
通讯作者：
PANJABI, MM