A preliminary study on postmortem interval estimation of suffocated rats by GC-MS/MS-based plasma metabolic profiling

Multi-omics integration strategy in the post-mortem interval of forensic science

Estimates of post-mortem interval (PMI), which often serve as pivotal evidence in forensic contexts, are fundamentally based on assessments of variability among diverse molecular markers (including proteins and metabolites), their correlations, and their temporal changes in post-mortem organisms. Nevertheless, the present approach to estimating the PMI is not comprehensive and exhibits poor performance. We developed an innovative approach that integrates multi-omics and artificial intelligence, using multimolecular, multimarker, and multidimensional information to accurately describe the intricate biological processes that occur after death, ultimately enabling inference of the PMI. Called the multi-omics stacking model (MOSM), it combines metabolomics, protein microarray electrophoresis, and fourier transform-infrared spectroscopy data. It shows improved prediction accuracy of the PMI, which is urgently needed in the forensic field. It achieved an accuracy of 0.93, generalized area under the receiver operating characteristic curve of 0.98, and minimum mean absolute error of 0.07. The MOSM integration framework not only considers multiple markers but also incorporates machine-learning models with distinct algorithmic principles. The diversity of biological mechanisms and algorithmic models further ensures the generalizability and robustness of PMI estimation.

The Role of Protein Degradation in Estimation Postmortem Interval and Confirmation of Cause of Death in Forensic Pathology: A Literature Review

It is well known that proteins are important bio-macromolecules in human organisms, and numerous proteins are widely used in the clinical practice, whereas their application in forensic science is currently limited. This limitation is mainly attributed to the postmortem degradation of targeted proteins, which can significantly impact final conclusions. In the last decade, numerous methods have been established to detect the protein from a forensic perspective, and some of the postmortem proteins have been applied in forensic practice. To better understand the emerging issues and challenges in postmortem proteins, we have reviewed the current application of protein technologies at postmortem in forensic practice. Meanwhile, we discuss the application of proteins in identifying the cause of death, and postmortem interval (PMI). Finally, we highlight the interpretability and limitations of postmortem protein challenges. We believe that utilizing the multi-omics method can enhance the comprehensiveness of applying proteins in forensic practice.

Estimating the time of human decomposition based on skeletal muscle biopsy samples utilizing an untargeted LC-MS/MS-based proteomics approach

Accurate estimation of the postmortem interval (PMI) is crucial in forensic medico-legal investigations to understand case circumstances (e.g. narrowing down list of missing persons or include/exclude suspects). Due to the complex decomposition chemistry, estimation of PMI remains challenging and currently often relies on the subjective visual assessment of gross morphological/taphonomic changes of a body during decomposition or entomological data. The aim of the current study was to investigate the human decomposition process up to 3 months after death and propose novel time-dependent biomarkers (peptide ratios) for the estimation of decomposition time. An untargeted liquid chromatography tandem mass spectrometry-based bottom-up proteomics workflow (ion mobility separated) was utilized to analyse skeletal muscle, collected repeatedly from nine body donors decomposing in an open eucalypt woodland environment in Australia. Additionally, general analytical considerations for large-scale proteomics studies for PMI determination are raised and discussed. Multiple peptide ratios (human origin) were successfully proposed (subgroups < 200 accumulated degree days (ADD), < 655 ADD and < 1535 ADD) as a first step towards generalised, objective biochemical estimation of decomposition time. Furthermore, peptide ratios for donor-specific intrinsic factors (sex and body mass) were found. Search of peptide data against a bacterial database did not yield any results most likely due to the low abundance of bacterial proteins within the collected human biopsy samples. For comprehensive time-dependent modelling, increased donor number would be necessary along with targeted confirmation of proposed peptides. Overall, the presented results provide valuable information that aid in the understanding and estimation of the human decomposition processes.

A pilot study investigating early postmortem interval of rats based on ambient temperature and postmortem interval-related metabolites in blood

Estimation of the postmortem interval (PMI), especially the early PMI, plays a key role in forensic practice. Although several studies based on metabolomics approaches have presented significant findings for PMI estimation, most did not examine the effects of ambient temperature. In this study, gas chromatography-mass spectrometry (GC‒MS)‒based metabolomics was adopted to explore the changes in metabolites in the cardiac blood of suffocated rats at various ambient temperatures (5 °C, 15 °C, 25 °C, and 35 °C) from 0 to 24 h after death. Isoleucine, alanine, proline, valine, glycerol, glycerol phosphate, xanthine, and hypoxanthine were found to contribute to PMI in all temperature groups. Hypoxanthine and isoleucine were chosen to establish estimation models (equations) with an interpolation function using PMI as the dependent variable (f_{(x, y)}), relative intensity as the independent variable x, and temperature as the independent variable y. Thereafter, these two models were validated with predictive samples and shown to have potential predictive ability. The findings indicate that isoleucine, alanine, proline, valine, glycerol, glycerol phosphate, xanthine, and hypoxanthine may be significant for PMI estimation at various ambient temperatures. Furthermore, a method to determine PMI based on ambient temperature and PMI-related metabolites was explored, which may provide a basis for future studies and practical applications.

Combining with lab-on-chip technology and multi-organ fusion strategy to estimate post-mortem interval of rat

Background

The estimation of post-mortem interval (PMI) is one of the most important problems in forensic pathology all the time. Although many classical methods can be used to estimate time since death, accurate and rapid estimation of PMI is still a difficult task in forensic practice, so the estimation of PMI requires a faster, more accurate, and more convenient method.

Materials and methods

In this study, an experimental method, lab-on-chip, is used to analyze the characterizations of polypeptide fragments of the lung, liver, kidney, and skeletal muscle of rats at defined time points after death (0, 1, 2, 3, 5, 7, 9, 12, 15, 18, 21, 24, 27, and 30 days). Then, machine learning algorithms (base model: LR, SVM, RF, GBDT, and MLPC; ensemble model: stacking, soft voting, and soft-weighted voting) are applied to predict PMI with single organ. Multi-organ fusion strategy is designed to predict PMI based on multiple organs. Then, the ensemble pruning algorithm determines the best combination of multi-organ.

Results

The kidney is the best single organ for predicting the time of death, and its internal and external accuracy is 0.808 and 0.714, respectively. Multi-organ fusion strategy dramatically improves the performance of PMI estimation, and its internal and external accuracy is 0.962 and 0.893, respectively. Finally, the best organ combination determined by the ensemble pruning algorithm is all organs, such as lung, liver, kidney, and skeletal muscle.

Conclusion

Lab-on-chip is feasible to detect polypeptide fragments and multi-organ fusion is more accurate than single organ for PMI estimation.

A novel method for determining postmortem interval based on the metabolomics of multiple organs combined with ensemble learning techniques

Determining postmortem interval (PMI) is one of the most challenging and essential endeavors in forensic science. Developments in PMI estimation can take advantage of machine learning techniques. Currently, applying an algorithm to obtain information on multiple organs and conducting joint analysis to accurately estimate PMI are still in the early stages. This study aimed to establish a multi-organ stacking model that estimates PMI by analyzing differential compounds of four organs in rats. In a total of 140 rats, skeletal muscle, liver, lung, and kidney tissue samples were collected at each time point after death. Ultra-performance liquid chromatography coupled with high-resolution mass spectrometry was used to determine the compound profiles of the samples. The original data were preprocessed using multivariate statistical analysis to determine discriminant compounds. In addition, three interrelated and increasingly complex patterns (single organ optimal model, single organ stacking model, multi-organ stacking model) were established to estimate PMI. The accuracy and generalized area under the receiver operating characteristic curve of the multi-organ stacking model were the highest at 93% and 0.96, respectively. Only 1 of the 14 external validation samples was misclassified by the multi-organ stacking model. The results demonstrate that the application of the multi-organ combination to the stacking algorithm is a potential forensic tool for the accurate estimation of PMI.

In Vitro Animal Model for Estimating the Time since Death with Attention to Early Postmortem Stage

Estimating the postmortem interval (PMI) has remained the subject of investigations in forensic medicine for many years. Every kind of death results in changes in metabolites in body tissues and fluids due to lack of oxygen, altered circulation, enzymatic reactions, cellular degradation, and cessation of anabolic production of metabolites. Metabolic changes may provide markers determining the time since death, which is challenging in current analytical and observation-based methods. The study includes metabolomics analysis of blood with the use of an animal model to determine the biochemical changes following death. LC-MS is used to fingerprint postmortem porcine blood. Metabolites, significantly changing in blood after death, are selected and identified using univariate statistics. Fifty-one significant metabolites are found to help estimate the time since death in the early postmortem stage. Hypoxanthine, lactic acid, histidine, and lysophosphatidic acids are found as the most promising markers in estimating an early postmortem stage. Selected lysophosphatidylcholines are also found as significantly increased in blood with postmortal time, but their practical utility as PMI indicators can be limited due to a relatively low increasing rate. The findings demonstrate the great potential of LC-MS-based metabolomics in determining the PMI due to sudden death and provide an experimental basis for applying this attitude in investigating various mechanisms of death. As we assume, our study is also one of the first in which the porcine animal model is used to establish PMI metabolomics biomarkers.

Application of artificial intelligence and machine learning technology for the prediction of postmortem interval: A systematic review of preclinical and clinical studies

BACKGROUND /PURPOSE

Establishing an accurate postmortem interval (PMI) is exceptionally crucial in forensic investigation. Artificial intelligence (AI) and Machine learning (ML) models are widely employed in forensic practice. ML is a part of AI, both terms are highly associated and sometimes used interchangeably. This systematic review aims to evaluate the application and performance of AI technology for the prediction of PMI.

METHODS

Systematic literature search across different electronic databases using PubMed/Google Scholar/EMBASE/Scopus/CINAHL/Web of Science/Cochrane library was conducted from inception to 3 December 2021 for preclinical and clinical studies reported ML models for PMI estimation.

RESULTS

We identified 18 studies (12 preclinical and 06 clinical) that met the inclusion criteria in the qualitative analysis. Most of the studies employed supervised learning (N = 15), and others employed unsupervised learning (N = 3). Due to the heterogeneity of the samples, quantitative analysis was not performed.

CONCLUSION

In this systematic review, we discussed the performance of AI-based automated systems in PMI estimation. ML models have demonstrated accuracy and precision and the ability to overcome human errors and bias. However, the research is limited, conducted in primarily small, selected human populations. In addition, we suggest further research in larger population-based studies is needed to fully understand the extent of integrated ML models.

Proteomic profiling of postmortem prefrontal cortex tissue of suicide completers

Suicide is a leading cause of death worldwide, presenting a serious public health problem. We aimed to investigate the biological basis of suicide completion using proteomics on postmortem brain tissue. Thirty-six postmortem brain samples (23 suicide completers and 13 controls) were collected. We evaluated the proteomic profile in the prefrontal cortex (Broadmann area 9, 10) using tandem mass tag-based quantification with liquid chromatography-tandem mass spectrometry. Bioinformatics tools were used to elucidate the biological mechanisms related to suicide. Subgroup analysis was conducted to identify common differentially expressed proteins among clinically different groups. Of 9801 proteins identified, 295 were differentially expressed between groups. Suicide completion samples were mostly enriched in the endocannabinoid and apoptotic pathways (CAPNS1, CSNK2B, PTP4A2). Among the differentially expressed proteins, GSTT1 was identified as a potential biomarker among suicide completers with psychiatric disorders. Our findings suggest that the previously under-recognized endocannabinoid system and apoptotic processes are highly involved in suicide.

Applications of Metabolomics in Forensic Toxicology and Forensic Medicine

Forensic toxicology and forensic medicine are unique among all other medical fields because of their essential legal impact, especially in civil and criminal cases. New high-throughput technologies, borrowed from chemistry and physics, have proven that metabolomics, the youngest of the “omics sciences”, could be one of the most powerful tools for monitoring changes in forensic disciplines. Metabolomics is a particular method that allows for the measurement of metabolic changes in a multicellular system using two different approaches: targeted and untargeted. Targeted studies are focused on a known number of defined metabolites. Untargeted metabolomics aims to capture all metabolites present in a sample. Different statistical approaches (e.g., uni- or multivariate statistics, machine learning) can be applied to extract useful and important information in both cases. This review aims to describe the role of metabolomics in forensic toxicology and in forensic medicine.

Comparative Decomposition of Humans and Pigs: Soil Biogeochemistry, Microbial Activity and Metabolomic Profiles

Vertebrate decomposition processes have important ecological implications and, in the case of human decomposition, forensic applications. Animals, especially domestic pigs (Sus scrofa), are frequently used as human analogs in forensic decomposition studies. However, recent research shows that humans and pigs do not necessarily decompose in the same manner, with differences in decomposition rates, patterns, and scavenging. The objective of our study was to extend these observations and determine if human and pig decomposition in terrestrial settings have different local impacts on soil biogeochemistry and microbial activity. In two seasonal trials (summer and winter), we simultaneously placed replicate human donors and pig carcasses on the soil surface and allowed them to decompose. In both human and pig decomposition-impacted soils, we observed elevated microbial respiration, protease activity, and ammonium, indicative of enhanced microbial ammonification and limited nitrification in soil during soft tissue decomposition. Soil respiration was comparable between summer and winter, indicating similar microbial activity; however, the magnitude of the pulse of decomposition products was greater in the summer. Using untargeted metabolomics and lipidomics approaches, we identified 38 metabolites and 54 lipids that were elevated in both human and pig decomposition-impacted soils. The most frequently detected metabolites were anthranilate, creatine, 5-hydroxyindoleacetic acid, taurine, xanthine, N-acetylglutamine, acetyllysine, and sedoheptulose 1/7-phosphate; the most frequently detected lipids were phosphatidylethanolamine and monogalactosyldiacylglycerol. Decomposition soils were also significantly enriched in metabolites belonging to amino acid metabolic pathways and the TCA cycle. Comparing humans and pigs, we noted several differences in soil biogeochemical responses. Soils under humans decreased in pH as decomposition progressed, while under pigs, soil pH increased. Additionally, under pigs we observed significantly higher ammonium and protease activities compared to humans. We identified several metabolites that were elevated in human decomposition soil compared to pig decomposition soil, including 2-oxo-4-methylthiobutanoate, sn-glycerol 3-phosphate, and tryptophan, suggesting different decomposition chemistries and timing between the two species. Together, our work shows that human and pig decomposition differ in terms of their impacts on soil biogeochemistry and microbial decomposer activities, adding to our understanding of decomposition ecology and informing the use of non-human models in forensic research.

Postmortomics: The Potential of Untargeted Metabolomics to Highlight Markers for Time Since Death

The success of forensic investigations involving fatalities very often depends on the establishment of the correct timeline of events. Currently used methods for estimating the postmortem interval (PMI) are mostly dependent on the professional and tacit experience of the investigator, and often with poor reliability in the absence of robust biological markers. The aim of this study was to investigate the potential of metabolomic approaches to highlight molecular markers for PMI. Rat and human muscle tissues, collected at various times postmortem, were analyzed using an untargeted metabolomics approach. Levels of certain metabolites (skatole, xanthine, n-acetylneuraminate, 1-methylnicotinamide, choline phosphate, and uracil) as well as most proteinogenic amino acids increased steadily postmortem. Threonine, tyrosine, and lysine show the most predictable evolution over the postmortem period, and may thus have potential for possible PMI markers in the future. This study demonstrates how a biomarker discovery approach can be extended to forensic investigations using untargeted metabolomics.

Omics era in forensic medicine: towards a new age

Background/aim

Forensic medicine and sciences is a multidisciplinary branch of science, which frequently benefit from novel technologies. State of the art omics technologies have begun to be performed in forensic medicine and sciences, particularly in postmortem interval, intoxication, drugs of abuse, diagnosis of diseases and cause of death. This review aims to discuss the role and use of great omics (metabolomics, proteomics, genomics and transcriptomics) in forensic sciences, in detail.

Materials and methods

A detailed review of related literature was performed, and studies were subdivided as per the type of omics.

Results and conclusion

Omics seems as a revolutionary step in forensic science and sure carries it towards a new age. The number of forensic studies utilizing omics steadily increases in last years. Omics strategies should be used together in order to gather more accurate and certain data. Additional studies need to be performed to incorporate omics into routine forensic methodology.

Post-mortem changes in metabolomic profiles of human serum, aqueous humor and vitreous humor

INTRODUCTION

Application of metabolomic methods to forensic studies may expand the limits of the post-mortem interval (PMI) estimation, and improve the accuracy of the estimation. To this end, it is important to determine which tissue is the most suitable for analysis, and which compounds are the most promising candidates for PMI estimation.

OBJECTIVES

This work is aimed at the comparison of human serum, aqueous humor (AH), and vitreous humor (VH) as perspective tissues for metabolomic-based PMI estimation, at the determination of most promising PMI biomarkers, and at the development of method of PMI estimation based on the measurement of concentrations of PMI biomarkers.

METHODS

Quantitative metabolomic profiling of samples of the human serum, AH, and VH taken at different PMIs has been performed with the use of NMR spectroscopy.

RESULTS

It is found that the metabolomic changes in anatomically isolated ocular fluids are slower and smoother than that in blood. A good positive time correlation (Pearson coefficient r > 0.5) was observed for several metabolites, including hypoxanthine, choline, creatine, betaine, glutamate, and glycine. A model for PMI estimation based on concentrations of several metabolites in AH and VH is proposed.

CONCLUSIONS

The obtained results demonstrate that the metabolomic analysis of AH and VH is more suitable for the PMI estimation than that of serum. The compounds with good positive time correlation can be considered as potential PMI biomarkers.

Postmortem metabolomics: Correlating time-dependent concentration changes of xenobiotic and endogenous compounds

Postmortem redistribution (PMR) describes the artificial postmortem concentration changes of xenobiotics that may pose major challenges in forensic toxicology. Only a few studies have systematically investigated time-dependent postmortem drug concentration changes so far and the a posteriori estimation of the occurrence of PMR is not yet possible. In this context, the general concept that postmortem biochemical changes in blood might parallel drug redistribution mechanisms seems promising. Thus, the current study investigated the possible correlations between time-dependent postmortem concentration changes of xenobiotic and endogenous compounds; exemplified for authentic morphine (n = 19) and methadone (n = 11) cases. Peripheral blood samples at two time-points postmortem were analyzed for morphine and methadone concentrations and an (un)targeted postmortem metabolomics approach was utilized to combine targeted quantitative analysis of 56 endogenous analytes and untargeted screening for endogenous compounds (characterizing 1174 features); liquid and gas chromatography-mass spectrometry was used respectively. Individual statistically significant correlations between morphine/methadone and endogenous compounds/features could be determined. Hence, the general applicability of the proposed concept could successfully be confirmed. To verify the reproducibility and robustness of the correlating behavior, a larger dataset must be analyzed next. Once a marker/set of markers is found (e.g. robust correlation with specific xenobiotic or xenobiotic class), these could be used as surrogates to further study the time-dependent PMR in a broader variety of cases (e.g. independent of a xenobiotic drug present). A crucial next step will also be the attempt to create a statistical model that allows a posteriori estimation of PMR occurrence of xenobiotics to assist forensic toxicologists in postmortem case interpretation.

Analytical considerations for postmortem metabolomics using GC-high-resolution MS

Metabolomics studies that aim to qualitatively and quantitatively characterize the entirety of small endogenous biomolecules in an organism are widely conducted in the clinical setting. They also become more and more popular in the field of forensics (toxicology), e.g., to assist in postmortem investigations by objective postmortem interval estimation. However, other issues in postmortem toxicology, such as the phenomenon of (time-dependent) postmortem redistribution, have not yet been tackled by metabolomics studies. Hence, the aim of the current study was to develop an (un)targeted gas chromatography-high-resolution mass spectrometry-based method for endogenous metabolites as a tool for large-scale (un)targeted human postmortem metabolomics investigations (e.g., to objectively assess PMR) with thorough analytical evaluation of this method to ensure fitness-to-purpose in terms of reliability and robustness. This was achieved by using a targeted metabolite subset (n = 56) and a targeted processing workflow. Evaluation experiments have shown that using an artificial matrix (revised simulated body fluid (rSBF) + 5% bovine serum albumin (BSA)) for calibration purposes, all parameters lay within the scope of the method (sensitivity, selectivity, calibration model, accuracy, precision, processed sample stability, and extraction efficiency). When applying this method to large-scale studies, samples should be run in randomized order if analysis time is expected to exceed 18-24 h and potential biomarkers that are found with this method should be verified by a specialized, targeted method (e.g., by using standard addition in authentic matrix for quantification purposes). Overall, the current method can be successfully used for conduction of time-dependent postmortem metabolomics investigations. Graphical abstract.

A 1H NMR metabolomic approach for the estimation of the time since death using aqueous humour: an animal model

INTRODUCTION

The estimation of the time since death, or post-mortem interval (PMI), still remains a main conundrum in forensic science. Several approaches have been so far proposed from either a qualitative or a quantitative point of view, but they still lack reliability and robustness. Recently, metabolomics has shown to be a potential tool to investigate the time-related post-mortem metabolite modifications in animal models.

OBJECTIVES

Here we propose, for the first time, the use of a ¹H NMR metabolomic approach for the estimation of PMI from aqueous humour (AH) in an ovine model.

METHODS

AH samples were collected at different times after death (from 118 to 1429 min). ¹H NMR experiments were performed and spectral data analysed by multivariate statistical tools.

RESULTS

A multivariate calibration model was built to estimate PMI on the basis of the metabolite content of the samples. The model was validated with an independent test set, obtaining a prediction error of 59 min for PMI < 500 min, 104 min for PMI from 500 to 1000 min, and 118 min for PMI > 1000 min. Moreover, the metabolomic approach suggested a picture of the mechanisms underlying the post-mortem biological modifications, highlighting the role played by taurine, choline, and succinate.

CONCLUSION

The time-related modifications of the ¹H NMR AH metabolomic profile seem to be encouraging in addressing the issue of a reproducible and robust model to be employed for the estimation of the time since death.

Analytical considerations for (un)-targeted metabolomic studies with special focus on forensic applications

Over the past few years, the interest in metabolomics has increased in various fields including forensic toxicology. Forensic analysis typically requires a high degree of accuracy, which is often a problem in metabolomics applications. We aimed for a systematic evaluation of different analytical considerations of a metabolomics workflow allowing a targeted approach within an untargeted setup. Samples with 69 metabolites from different chemical classes were qualitatively and quantitatively analyzed on a high resolution quadrupole time of flight mass spectrometer coupled to liquid chromatography (UHPLC-QTOF). Three issues were addressed: (a) Two different approaches on "blind matrix" a simulated body fluid (SBF) and plasma-filtrate, were tested for calibration samples; (b) comparison of two different HPLC columns, reverse-phase (RP) and hydrophilic interaction chromatography (HILIC); and (c) comparison of three different acquisition modes (TOF-MS, information dependent data acquisition (IDA), and sequential window acquisition of all theoretical fragment-ion spectra (SWATH). Samples were measured repeatedly for method comparison based on sensitivity, accuracy, precision, and detection robustness. The blind matrices showed similar accuracy for most analytes, while SBF provided an easier preparation with satisfying results. To cover a wide part of the human metabolome, a combination of RP and HILIC showed the best results. The different scan modes performed equally regarding metabolite quantification while TOF-MS was more sensitive but lacked MS/MS spectra generation. IDA and SWATH files were aligned to various databases where IDA showed good MS/MS spectra matches. SWATH seemed to be beneficial in detection rate but was incompatible with many important software tools in metabolomics.

An experimental study on investigating the postmortem interval in dichlorvos poisoned rats by GC/MS-based metabolomics

The estimation of the postmortem interval (PMI) is always a key issue in forensic science. Although many attempts based on metabolomics approaches have been proven to be feasible and accurate for PMI estimation, there have been no reports regarding the determination of the PMI in acute dichlorvos (DDVP) poisoning. In this study, all rats were killed by acute DDVP poisoning at a dose three fold the oral LD₅₀ (240 mg/kg). Gas chromatography-mass spectrometry (GC/MS) was applied to investigate the metabolic profiling of blood samples at various times after death up to 72 h. A total of 39 metabolites were found to be associated with PMI, and the combinations of various numbers of metabolites were used to establish support vector regression (SVR) models to investigate the PMI. The SVR model constructed by 23 metabolites had a minimum mean squared error (MSE) of 5.49 h for the training set. Then, the SVR model was validated by prediction set with an MSE of 10.33 h, suggesting good predictive ability of the model for investigating the PMI. The findings demonstrated the great potential of GC/MS-based metabolomics combined with the SVR model in determining the PMI of DDVP poisoned rats and provided an experimental basis for the application of this approach in investigating the PMI of other toxicants.

Metabolic profiling of femoral muscle from rats at different periods of time after death

Clarification of postmortem metabolite changes can help characterize the process of biological degradation and facilitate investigations of forensic casework, especially in the estimation of postmortem interval (PMI). Metabolomics can provide information on the molecular profiles of tissues, which can aid in investigating postmortem metabolite changes. In this study, liquid chromatography-mass spectrometric (LC-MS) analysis was performed to identify the metabolic profiles of rat femoral muscle at ten periods of time after death within 168 h. The results obtained by reversed-phase liquid chromatography (RPLC)- and hydrophilic interaction liquid chromatography (HILIC)- electrospray ionization (ESI±) have revealed more than 16,000 features from all four datasets. Furthermore, 915 of these features were identified using an in-house database. Principal component analysis (PCA) demonstrated the time-specific features of molecular profiling at each period of time after death. Moreover, results from partial least squares projection to latent structures-discriminant analysis (PLS-DA) disclosed a strong association of metabolic alterations of at least 59 metabolites with the time since death, especially within 48 h after death, which expounds these metabolites as potential indicators in PMI estimation. Altogether, our results illustrate the potentiality of metabolic profiling in the evaluation of PMI and provide candidate metabolite markers with strong correlation with time since death for forensic purpose.

Vitality and wound-age estimation in forensic pathology: review and future prospects

Determining the age of a wound is challenging in forensic pathology, but it can contribute to the reconstruction of crime scenes and lead to arrest of suspects. Forensic scholars have tended to focus on evaluating wound vitality and determining the time elapsed since the wound was sustained. Recent progress in forensic techniques, particularly high-throughput analyses, has enabled evaluation of materials at the cellular and molecular levels, as well as simultaneous assessment of multiple markers. This paper provides an update on wound-age estimation in forensic pathology, summarizes the recent literature, and considers useful additional information provided by each marker. Finally, the future prospects for estimating wound age in forensic practise are discussed with the hope of providing something useful for further study.

Estimation of early postmortem interval in rats by GC-MS-based metabolomics

Accurately predicting the early postmortem interval (PMI) is of great significance in forensic practice. This study aimed to establish a novel method for estimating the early PMI by analyzing endogenous substances in the cardiac blood of male and female rats and compare different model for estimating early PMI using these data. Adult Sprague-Dawley (SD) rats (50% male) were sacrificed by suffocation. Then, cardiac blood was collected at various time intervals (0, 3, 6, 12, 24, 48, and 72 h) after death, and the collected samples were analyzed by gas chromatography-tandem mass spectrometry (GC-MS). The data were analyzed by multivariate statistical analysis. An orthogonal signal correction-partial least squares (OSC-PLS) regression model was constructed with whole endogenous metabolites to validate the PMI. The OSC-PLS regression model successfully predicted the PMI of the forecast set and no significant differences was observed between male and female rats. This is the first study to establish an OSC-PLS regression model for predicting PMI with the metabolome, which provides a new technical method and platform for estimating PMI through metabolomics.

Application of Fourier transform infrared spectroscopy with chemometrics on postmortem interval estimation based on pericardial fluids

Postmortem interval (PMI) evaluation remains a challenge in the forensic community due to the lack of efficient methods. Studies have focused on chemical analysis of biofluids for PMI estimation; however, no reports using spectroscopic methods in pericardial fluid (PF) are available. In this study, Fourier transform infrared (FTIR) spectroscopy with attenuated total reflectance (ATR) accessory was applied to collect comprehensive biochemical information from rabbit PF at different PMIs. The PMI-dependent spectral signature was determined by two-dimensional (2D) correlation analysis. The partial least square (PLS) and nu-support vector machine (nu-SVM) models were then established based on the acquired spectral dataset. Spectral variables associated with amide I, amide II, COO-, C-H bending, and C-O or C-OH vibrations arising from proteins, polypeptides, amino acids and carbohydrates, respectively, were susceptible to PMI in 2D correlation analysis. Moreover, the nu-SVM model appeared to achieve a more satisfactory prediction than the PLS model in calibration; the reliability of both models was determined in an external validation set. The study shows the possibility of application of ATR-FTIR methods in postmortem interval estimation using PF samples.

UV-Vis and ATR-FTIR spectroscopic investigations of postmortem interval based on the changes in rabbit plasma

Estimating PMI is of great importance in forensic investigations. Although many methods are used to estimate the PMI, a few investigations focus on the postmortem redistribution. In this study, ultraviolet-visible (UV-Vis) measurement combined with visual inspection indicated a regular diffusion of hemoglobin into plasma after death showing the redistribution of postmortem components in blood. Thereafter, attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy was used to confirm the variations caused by this phenomenon. First, full-spectrum partial least-squares (PLS) and genetic algorithm combined with PLS (GA-PLS) models were constructed to predict the PMI. The performance of GA-PLS model was better than that of full-spectrum PLS model based on its root mean square error (RMSE) of cross-validation of 3.46 h (R2 = 0.95) and the RMSE of prediction of 3.46 h (R2 = 0.94). The investigation on the similarity of spectra between blood plasma and formed elements also supported the role of redistribution of components in spectral changes in postmortem plasma. These results demonstrated that ATR-FTIR spectroscopy coupled with the advanced mathematical methods could serve as a convenient and reliable tool to study the redistribution of postmortem components and estimate the PMI.

MALDI-TOF MS as a Novel Tool for the Estimation of Postmortem Interval in Liver Tissue Samples

Estimation of the postmortem interval (PMI) is a complicated task in forensic medicine, especially during homicide and unwitnessed death investigations. Many biological, chemical, and physical indicators can be used to determine the postmortem interval, but most are not accurate. Here, we present a novel matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) method that can be used for the estimation of PMI using molecular images and multivariate analyses. In this study, we demonstrate that both rat and human liver tissues of various PMIs (0, 2, 4, and 6days) can be discriminated using MALDI imaging and principal component analysis (PCA). Using genetic algorithm (GA), supervised neural network (SNN), and quick classifier (QC) methods, we built 6 classification models, which showed high recognition capability and good cross-validation. The histological changes in all the samples at different time points were also consistent with the changes seen in MALDI imaging. Our work suggests that MALDI-TOF MS, along with multivariate analysis, can be used to determine intermediate PMIs.

Microbiome Tools for Forensic Science

Microbes are present at every crime scene and have been used as physical evidence for over a century. Advances in DNA sequencing and computational approaches have led to recent breakthroughs in the use of microbiome approaches for forensic science, particularly in the areas of estimating postmortem intervals (PMIs), locating clandestine graves, and obtaining soil and skin trace evidence. Low-cost, high-throughput technologies allow us to accumulate molecular data quickly and to apply sophisticated machine-learning algorithms, building generalizable predictive models that will be useful in the criminal justice system. In particular, integrating microbiome and metabolomic data has excellent potential to advance microbial forensics.

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.

Research progress in the estimation of the postmortem interval by Chinese forensic scholars

The determination of time since death or the postmortem interval (PMI) is one of the most important and frequently asked questions in forensic medicine. Medicolegal scholars and forensic pathologists around the world have studied the estimation of PMI extensively in the past, and many novel methods and advanced technologies have now been applied in the field. For several centuries, Chinese forensic examiners have also worked on the estimation of the PMI, and there are a large number of excellent studies published in Chinese rather than in English, and these are not easily accessible or known internationally. Therefore we have conducted a review of relevant studies published by Chinese forensic scholars in the last few decades. The scope of this review is to provide a concise summary of the current progress in the estimation of PMI by Chinese forensic researchers using molecular biology, spectroscopic technology, entomological methods, energy changes, thanatochemistry and other methods.

The experimental design of postmortem studies: the effect size and statistical power

Purpose

The aim is of this study was to show the poor statistical power of postmortem studies. Further, this study aimed to find an estimate of the effect size for postmortem studies in order to show the importance of this parameter. This can be an aid in performing power analysis to determine a minimal sample size.

Methods

GPower was used to perform calculations on sample size, effect size, and statistical power. The minimal significance (α) and statistical power (1 − β) were set at 0.05 and 0.80 respectively. Calculations were performed for two groups (Student’s t-distribution) and multiple groups (one-way ANOVA; F-distribution).

Results

In this study, an average effect size of 0.46 was found (n = 22; SD = 0.30). Using this value to calculate the statistical power of another group of postmortem studies (n = 5) revealed that the average statistical power of these studies was poor (1 − β < 0.80).

Conclusion

The probability of a type-II error in postmortem studies is considerable. In order to enhance statistical power of postmortem studies, power analysis should be performed in which the effect size found in this study can be used as a guideline.