Department of Forensic Medicine, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China.
Department of Forensic Pathology, Xi'an Jiaotong University, Xi'an 710061, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2022 Mar 5;268:120630. doi: 10.1016/j.saa.2021.120630. Epub 2021 Nov 15.
In clinical and forensic investigations, accurate post-mortem diagnosis of the pathological degree of myocardial infarction (MI) is critical. However, because of the observer variability, the diagnosis cannot be made objectively. Many studies have shown that Fourier transform infrared (FTIR) microspectroscopy is non-invasive, observer-independent, and label-free when analyzing biological tissues. In this study, we used FTIR microspectroscopy in combination with intelligent algorithms to identify the pathological phases in human infarcted cardiac tissues, including ischemia, necrotic, granulation, and fibrotic stages. First, a comparison of infrared spectra corresponding to infarcted tissue pathological categories revealed various spectral properties. The results of unsupervised principal component analysis (PCA) revealed a clear distinction between these four pathological stages and the normal stage. Then, to identify these five stages, an automatic artificial neural network (ANN) classifier was effectively created. Finally, two-dimensional pseudo-color images of two infarcted cardiac tissue sections visualized via the ANN classifier showed great agreement with their histological images. These findings demonstrate that FTIR microspectroscopy has the potential for the post-mortem evaluation of the pathological degree of MI.
在临床和法医学研究中，准确判断心肌梗死（MI）的病理程度至关重要。然而，由于观察者的变异性，诊断无法做到客观。许多研究表明，傅里叶变换红外（FTIR）显微光谱技术在分析生物组织时具有非侵入性、观察者独立性和无需标记的特点。在这项研究中，我们使用 FTIR 显微光谱技术结合智能算法来识别人类梗死心脏组织的病理阶段，包括缺血、坏死、肉芽和纤维化阶段。首先，对与梗死组织病理分类相对应的红外光谱进行比较，揭示了各种光谱特性。无监督主成分分析（PCA）的结果表明，这四个病理阶段与正常阶段之间有明显的区别。然后，为了识别这五个阶段，有效地创建了一个自动人工神经网络（ANN）分类器。最后，通过 ANN 分类器可视化的两个梗死心脏组织切片的二维伪彩色图像与它们的组织学图像非常吻合。这些发现表明，FTIR 显微光谱技术有可能用于 MI 病理程度的死后评估。
