Post-mortem evaluation of the pathological degree of myocardial infarction by Fourier transform infrared microspectroscopy

A novel approach to the cause of death identification-multi-strategy integration of multi-organ FTIR spectroscopy information using machine learning

Identifying the cause of death has always been a major focus and challenge in forensic practice and research. Traditional techniques for determining the causes of death are time-consuming, labor-intensive, have high professional barriers, and are vulnerable to significant subjective bias. Additionally, most current studies on causes of death are limited to specific organs and single causes. To overcome these challenges, this study utilized simple and rapid fourier transform infrared spectroscopy (FTIR) detection technology, integrating data from six organs-heart, liver, spleen, lung, kidney, and brain. The optimum model for identifying seven different causes of death was determined by evaluating the performance of models developed using the model efficiencies of single-organ (SO), single-organ model fusion (SOMF), multi-organ data fusion (MODF), and multi-organ data model fusion (MODMF) modeling methods. Considering factors such as operational costs, model performance, and model complexity, the MODF artificial neural network (ANN) model was found to be the most suitable choice for constructing a cause of death identification model, with a cross-validation mean accuracy of 0.960 and a test set accuracy of 0.952. The heart and kidney contributed more spectral features to the construction of the cause of death identification model compared to other organs. This study not only demonstrated that data fusion and model fusion are effective strategies for improving model performance but also provided a comprehensive data analysis framework and process for modeling with small sample multi-modal data (multiple organ data). In conclusion, by exploring various approaches, this study offers new solutions for identifying the cause of death.

The application of vibrational spectroscopy in forensic analysis of biological evidence

Vibrational spectroscopy is a powerful analytical domain, within which Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy stand as exemplars, offering high chemical specificity and sensitivity. These methodologies have been instrumental in the characterization of chemical compounds for an extensive period. They are particularly adept at the identification and analysis of minute sample quantities. Both FTIR and Raman spectroscopy are proficient in elucidating small liquid samples and detecting nuanced molecular alterations. The application of chemometrics further augments their analytical prowess. Currently, these techniques are in the research phase within forensic medicine and have yet to be broadly implemented in examination and identification processes. Nonetheless, studies have indicated that a combined classification model utilizing FTIR and Raman spectroscopy yields exceptional results for the identification of biological fluid-related information and the determination of causes of death. The objective of this review is to delineate the current research trajectory and potential applications of these two vibrational spectroscopic techniques in the detection of body fluids and the ascertainment of causes of death within the context of forensic medicine.

Use of Raman spectroscopy to study rat lung tissues for distinguishing asphyxia from sudden cardiac death

Determining asphyxia as the cause of death is crucial but is based on an exclusive strategy because it lacks sensitive and specific morphological characteristics in forensic practice. In some cases where the deceased has underlying heart disease, differentiation between asphyxia and sudden cardiac death (SCD) as the primary cause of death can be challenging. Herein, Raman spectroscopy was employed to detect pulmonary biochemical differences to discriminate asphyxia from SCD in rat models. Thirty-two rats were used to build asphyxia and SCD models, with lung samples collected immediately or 24 h after death. Twenty Raman spectra were collected for each lung sample, and 640 spectra were obtained for further data preprocessing and analysis. The results showed that different biochemical alterations existed in the lung tissues of the rats that died from asphyxia and SCD and could be used to distinguish between the two causes of death. Moreover, we screened and used 8 of the 11 main differential spectral features that maintained their significant differences at 24 h after death to successfully determine the cause of death, even with decomposition and autolysis. Eventually, seven prevalent machine learning classification algorithms were employed to establish classification models, among which the support vector machine exhibited the best performance, with an area under the curve value of 0.9851 in external validation. This study shows the promise of Raman spectroscopy combined with machine learning algorithms to investigate differential biochemical alterations originating from different deaths to aid determining the cause of death in forensic practice.

Distinguishing Asphyxia from Sudden Cardiac Death as the Cause of Death from the Lung Tissues of Rats and Humans Using Fourier Transform Infrared Spectroscopy

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.

Evaluating Subtle Pathological Changes in Early Myocardial Ischemia Using Spectral Histopathology

Early myocardial ischemia (EMI) is morphologically challenging, and the results from conventional histological staining may be subjective, imprecise, or even silent. The size of myocardial necrosis determines the acute and long-term mortality of EMI. The precise diagnosis of myocardial ischemia is critical for both clinical management and forensic investigation. Fourier transform infrared (FTIR) spectroscopic imaging is a highly sensitive tool for detecting protein conformations and imaging protein profiles. The aim of this study was to evaluate the application of FTIR imaging with multivariate analysis to detect biochemical changes in the protein conformation in the early phase of myocardial ischemia and to visually classify different disease states. The spectra and curve fitting results revealed that the total protein content decreased significantly in the EMI group and that the α-helix content of the secondary protein structure continuously decreased as ischemia progressed, while the β-sheet content increased. Differences in the control and EMI groups and perfused and ischemic myocardium were confirmed using principal component analysis and partial least squares discriminant analysis. Next, two support vector machine classifiers were effectively created. The accuracy, recall, and precision were 99.98, 99.96, and 100.00%, respectively, to differentiate the EMI group from the control group and 99.25, 98.95, and 99.54%, respectively, to differentiate perfused and ischemic myocardium. Ultimately, high EMI diagnostic accuracy was achieved with 100.00% recall and 100.00% precision, and ischemic myocardium diagnostic accuracy was achieved with 99.30% recall and 99.53% precision for the test set. This pilot study demonstrated that FTIR imaging is a powerful automated quantitative analysis tool to detect EMI without morphological changes and will improve diagnostic accuracy and patient prognosis.