Accurate identification of traumatic lung injury (TLI) by ATR-FTIR spectroscopy combined with chemometrics.
Accurate identification of traumatic lung injury (TLI) by ATR-FTIR spectroscopy combined with chemometrics.
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DOI：
10.1016/j.saa.2022.122186
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Xinggong Liang;Gongji Wang;Zefeng Li;Run Chen;Hao Wu;Huiyu Li;Chen Shen;M. Deng;Zeyi Hao
中科院分区：
文献类型：
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作者：
Xinggong Liang;Gongji Wang;Zefeng Li;Run Chen;Hao Wu;Huiyu Li;Chen Shen;M. Deng;Zeyi Hao
创伤性肺损伤（TLI）是一种常见的机械性损伤，因其严重的危害性而日益受到重视。在司法实践中，准确识别TLI对侦查和案件审理具有重要意义。本研究的主要目的是利用衰减全反射-傅里叶变换红外光谱（ATR-FTIR）结合化学计量学来识别TLI。肺组织的宏观外观显示，在分解阶段识别肺组织中的TLI仅通过可视化是不可行的，并且无论肺组织是否受损，在肺中观察到显著的肺实质。平均光谱和主成分分析（PCA）表明，TLI组损伤肺组织样品与阴性对照组非损伤肺组织样品之间的生化差异主要归因于蛋白质结构和含量的不同。偏最小二乘判别分析（PLS-DA），然后利用识别TLI的准确率分别为96.4%和98.6%的训练集和测试集的基础上。接下来，我们专注于在模型中被错误分类的样本，并提出错误分类可能是由肺本质效应引起的。因此，创建了两个额外的PCA和PLS-DA模型，以识别TLI组和阴性对照组之间的肺气肿区域以及TLI组和阴性对照组之间的非肺气肿区域。主成分分析结果表明，两组之间的生化差异仍然与蛋白质有关，两个PLS-DA模型在训练集和测试集上都达到了100%的准确率。这一结果表明，当考虑到肺实质，并将肺组织分为肺实质区和非肺实质区进行单独比较时，TLI识别的准确性比两个区域结合时更高。本研究证实，ATR-FTIR光谱和化学计量学的联合应用可以用来准确地识别TLI。
Traumatic lung injury (TLI), which is a common mechanical injury, is receiving increasing attention because of its serious hazards. In forensic practices, accurately identifying TLI is of great importance for investigations and case trials. The main goal of this research was to identify TLI utilizing attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy in combination with chemometrics. The macroscopic appearance of lung tissue showed that identifying TLI in lung tissue at the decomposition stage is not feasible by only visualization, and significant pulmonary hypostasis was observed in the lungs regardless of whether the lung tissue was injured. Average spectra and principal component analysis (PCA) suggested that the biochemical difference between injured lung tissue samples from the TLI group and noninjured lung tissue samples from the negative control group was mainly attributed to the different structures and contents of proteins. Partial least squares discriminant analysis (PLS-DA) was then utilized to identify TLI with an accuracy of 96.4% and 98.6% based on the training set and the test set, respectively. Next, we focused on samples that were misclassified in the model and proposed that the misclassification could be caused by the pulmonary hypostasis effect. Therefore, two additional PCA and PLS-DA models were created to identify the pulmonary hypostatic areas between the TLI group and the negative control group and the nonpulmonary hypostatic areas between the TLI group and the negative control group. The PCA results indicated that the biochemical difference between the two groups was still associated with proteins, and the two PLS-DA models achieved 100% accuracy based on both the training and test sets. This result indicated that when pulmonary hypostasis was considered and the lung tissue was divided into pulmonary hypostatic areas and nonpulmonary hypostatic areas for separate comparisons, TLI identification was achieved with a greater accuracy than that obtained when the two areas were combined. This research confirms that the combined application of ATR-FTIR spectroscopy and chemometrics can be utilized to accurately identify TLI.