The effect of elapsed time on cardiac troponin-T (cTnT) degradation and its relation to postmortem interval in cases of electrocution

Systematic Review on Post-Mortem Protein Alterations: Analysis of Experimental Models and Evaluation of Potential Biomarkers of Time of Death

Estimating the post-mortem interval (PMI) is a very complex issue due to numerous variables that may affect the calculation. Several authors have investigated the quantitative and qualitative variations of protein expression on post-mortem biological samples in certain time intervals, both in animals and in humans. However, the literature data are very numerous and often inhomogeneous, with different models, tissues and proteins evaluated, such that the practical application of these methods is limited to date. The aim of this paper was to offer an organic view of the state of the art about post-mortem protein alterations for the calculation of PMI through the analysis of the various experimental models proposed. The purpose was to investigate the validity of some proteins as “molecular clocks” candidates, focusing on the evidence obtained in the early, intermediate and late post-mortem interval. This study demonstrates how the study of post-mortem protein alterations may be useful for estimating the PMI, although there are still technical limits, especially in the experimental models performed on humans. We suggest a protocol to homogenize the study of future experimental models, with a view to the next concrete application of these methods also at the crime scene.

Postmortem Protein Degradation as a Tool to Estimate the PMI: A Systematic Review

Objectives: We provide a systematic review of the literature to evaluate the current research status of protein degradation-based postmortem interval (PMI) estimation. Special attention is paid to the applicability of the proposed approaches/methods in forensic routine practice. Method: A systematic review of the literature on protein degradation in tissues and organs of animals and humans was conducted. Therefore, we searched the scientific databases Pubmed and Ovid for publications until December 2019. Additional searches were performed in Google Scholar and the reference lists of eligible articles. Results: A total of 36 studies were included. This enabled us to consider the degradation pattern of over 130 proteins from 11 different tissues, studied with different methods including well-established and modern approaches. Although comparison between studies is complicated by the heterogeneity of study designs, tissue types, methods, proteins and outcome measurement, there is clear evidence for a high explanatory power of protein degradation analysis in forensic PMI analysis. Conclusions: Although only few approaches have yet exceeded a basic research level, the current research status provides strong evidence in favor of the applicability of a protein degradation-based PMI estimation method in routine forensic practice. Further targeted research effort towards specific aims (also addressing influencing factors and exclusion criteria), especially in human tissue will be required to obtain a robust, reliable laboratory protocol, and collect sufficient data to develop accurate multifactorial mathematical decomposition models.

Evaluating the effects of causes of death on postmortem interval estimation by ATR-FTIR spectroscopy

Estimating postmortem interval (PMI) is one of the most challenging tasks in forensic practice due to the effects of many factors. Here, attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy combined with chemometrics was utilized to evaluate the effects of causes of death when estimating PMI and to establish a partial least square (PLS) regression model, which can precisely predict PMI under different causes of death. First, the sensitivities to causes of death (brainstem injury, mechanical asphyxia, and hemorrhage shock) of seven kinds of organs were evaluated based on their degrees of cohesion and separation. Then, the liver was selected as the most sensitive organ to establish a PMI estimation model to compare the predicted deviations from different causes of death. It turns out that the cause of death has no significant effect on estimating PMI. Next, a PLS regression model was built with kidney tissues, which have the lowest sensitivity, and this model showed a satisfactory predictive ability and wide applicability. This study demonstrates the feasibility of using ATR-FTIR spectroscopy in conjunction with chemometrics as a powerful alternative for detecting changes in biochemistry and estimating PMI. A new perspective was also provided for evaluating the effect of causes of death when predicting PMI.

Characterization of postmortem biochemical changes in rabbit plasma using ATR-FTIR combined with chemometrics: A preliminary study

Postmortem interval (PMI) determination is one of the most challenging tasks in forensic medicine due to a lack of accurate and reliable methods. It is especially difficult for late PMI determination. Although many attempts with various types of body fluids based on chemical methods have been made to solve this problem, few investigations are focused on blood samples. In this study, we employed an attenuated total reflection (ATR)-Fourier transform infrared (FTIR) technique coupled with principle component analysis (PCA) to monitor biochemical changes in rabbit plasma with increasing PMI. Partial least square (PLS) model was used based on the spectral data for PMI prediction in an independent sample set. Our results revealed that postmortem chemical changes in compositions of the plasma were time-dependent, and various components including proteins, lipids and nucleic acids contributed to the discrimination of the samples at different time points. A satisfactory prediction within 48h postmortem was performed by the combined PLS model with a good fitting between actual and predicted PMI of 0.984 and with an error of ±1.92h. In consideration of the simplicity and portability of ATR-FTIR, our preliminary study provides an experimental and theoretical basis for application of this technique in forensic practice.

Use of Cardiac Injury Markers in the Postmortem Diagnosis of Sudden Cardiac Death

In the daily practice of forensic pathology, sudden cardiac death (SCD) is a diagnostic challenge. Our aim was to determine the usefulness of blood biomarkers [creatine kinase CK-MB, myoglobin, troponins I and T (cTn-I and T), and lactate dehydrogenase] measured by immunoassay technique, in the postmortem diagnosis of SCD. Two groups were compared, 20 corpses with SCD and 8 controls. Statistical significance was determined by variance analysis procedures, with a post hoc Tukey multiple range test for comparison of means (p < 0.05). SCD cases showed significantly higher levels (p < 0.05) of cTn-T and cTn-I compared to the control group. Although only cases within the first 8 h of postmortem interval were included, and the control group consisted mainly of violent death cases, our results suggest that blood troponin levels may be useful to support a diagnosis of SCD.

Estimation of postmortem interval using the data of insulin level in the cadaver׳s blood

An assessment of levels of Insulin in cadaveric fluids, to estimate the postmortem interval (PMI) was carried out.

To profile postmortem changes of Insulin, it was extracted at different intervals i.e. (0, 3, 6, 12, 24 h), from the heart of 22 human cadavers. The cases included were the subjects of accidental deaths without any prior history of disease and their exact time of death was known. Immunoanalyzer Cobas e-411 instrument was used to detect the relationship between the amount of Insulin and PMI.

Level of Insulin was measured in cardiac blood. Statically, significant correlations between levels of Insulin and PMI were studied and correlation coefficients were calculated. SPSS (version 12.0) was used for statistical analysis.

Insulin levels in cadaver blood are correlated significantly with PMI with a p value of <0.001. When insulin level increases by 1 unit the duration decreases by 0.93 units. The least square regression line is: [Duration(Y)=22.71−0.93 Insulin level (X)]