Zhang Kai, Liu Ruina, Tuo Ya, Ma Kaijun, Zhang Dongchuan, Wang Zhenyuan, Huang Ping
Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an 710061, People's Republic of China.
Department of Biochemistry and Physiology, Shanghai University of Medicine and Health Sciences, Shanghai 201318, People's Republic of China.
ACS Omega. 2022 Dec 7;7(50):46859-46869. doi: 10.1021/acsomega.2c05968. eCollection 2022 Dec 20.
The ability to determine asphyxia as a cause of death is important in forensic practice and helps us to judge whether a case is criminal. However, in some cases where the deceased has underlying heart disease, death by asphyxia cannot be determined by traditional autopsy and morphological observation under a microscope because there are no specific morphological features for either asphyxia or sudden cardiac death (SCD). Here, Fourier transform infrared (FTIR) spectroscopy was employed to distinguish asphyxia from SCD. A total of 40 lung tissues (collected at 0 h and 24 h postmortem) from 20 rats (10 died from asphyxia and 10 died from SCD) and 16 human lung tissues from 16 real cases were used for spectral data acquisition. After data preprocessing, 2675 spectra from rat lung tissues and 1526 spectra from human lung tissues were obtained for subsequent analysis. First, we found that there were biochemical differences in the rat lung tissues between the two causes of death by principal component analysis and partial least-squares discriminant analysis (PLS-DA), which were related to alterations in lipids, proteins, and nucleic acids. In addition, a PLS-DA classification model can be built to distinguish asphyxia from SCD. Second, based on the spectral data obtained from lung tissues allowed to decompose for 24 h, we could still distinguish asphyxia from SCD even when decomposition occurred in animal models. Nine important spectral features that contributed to the discrimination in the animal experiment were selected and further analyzed. Third, 7 of the 9 differential spectral features were also found to be significantly different in human lung tissues from 16 real cases. A support vector machine model was finally built by using the seven variables to distinguish asphyxia from SCD in the human samples. Compared with the linear PLS-DA model, its accuracy was significantly improved to 0.798, and the correct rate of determining the cause of death was 100%. This study shows the application potential of FTIR spectroscopy for exploring the subtle biochemical differences resulting from different death processes and determining the cause of death even after decomposition.
在法医学实践中，确定窒息为死因的能力很重要，有助于我们判断案件是否为刑事案件。然而，在一些死者患有潜在心脏病的案例中，由于窒息或心源性猝死（SCD）均无特定的形态学特征，因此无法通过传统尸检和显微镜下的形态学观察来确定窒息死亡。在此，采用傅里叶变换红外（FTIR）光谱法区分窒息和SCD。总共使用了来自20只大鼠（10只死于窒息，10只死于SCD）的40个肺组织（在死后0小时和24小时采集）以及来自16个真实案例的16个人类肺组织进行光谱数据采集。经过数据预处理，获得了来自大鼠肺组织的2675个光谱和来自人类肺组织的1526个光谱用于后续分析。首先，通过主成分分析和偏最小二乘判别分析（PLS - DA），我们发现两种死因的大鼠肺组织存在生化差异，这与脂质、蛋白质和核酸的变化有关。此外，可以建立PLS - DA分类模型来区分窒息和SCD。其次，基于从允许分解24小时的肺组织获得的光谱数据，即使在动物模型中发生分解，我们仍然能够区分窒息和SCD。选择并进一步分析了在动物实验中有助于区分的9个重要光谱特征。第三，在16个真实案例的人类肺组织中也发现9个差异光谱特征中的7个存在显著差异。最终使用这七个变量建立了支持向量机模型，以区分人类样本中的窒息和SCD。与线性PLS - DA模型相比，其准确率显著提高到0.798，死因判定正确率为100%。本研究展示了FTIR光谱法在探索不同死亡过程导致的细微生化差异以及确定甚至在分解后死因方面的应用潜力。
