The time of death in Dutch court; using the Daubert criteria to evaluate methods to estimate the PMI used in court

Comparative analysis of anticoagulant influence on PMI estimation based on porcine blood metabolomics profile measured using GC-MS

Introduction

Accurate post-mortem interval (PMI) estimation is essential in forensic investigations. Although various methods for PMI determination have been developed, only an approximate estimation is still achievable, and an accurate PMI indication is still challenging. Therefore, in this study, we employed gas chromatography-mass spectrometry (GC-MS)-based metabolomics to assess post-mortem changes in porcine blood samples collected with and without the addition of anticoagulant (EDTA). Our study aimed to identify metabolites dependent on the EDTA addition and time (taking into account the biodiversity of the studied organism) and those that are time-dependent but resistant to the addition of an anticoagulant.

Methods

The experiment was performed on blood samples collected from 16 animals (domestic pig, breed: Polish Large White), 8 with and 8 without EDTA addition. The moment of death (time 0) and 15 additional time points (from 3 to 168 h after death) were selected to examine changes in metabolites' levels in specific time intervals. We employed linear mixed models to study the relationship between metabolite intensities, time and presence of EDTA while accounting for the effect of individual pigs.

Results and Discussion

We confirmed that the intensity of 16 metabolites (mainly amino acids) significantly depends on PMI and the presence of EDTA. However, the intensity of the ideal biomarker(s) for PMI estimation should be determined only by the time after death and not by external factors such as the presence of the anticoagulant agent. Thus, we identified 41 metabolites with time-dependent intensities that were not susceptible to EDTA presence. Finally, we assessed the performance of these metabolites in a PMI predictive model. Citraconic acid yielded one of the lowest errors in general PMI estimation (32.82 h). Moreover, similar errors were observed for samples with and without EDTA (33.32 h and 32.34 h, respectively). Although the small sample size and information leak in predictive modelling prevent drawing definite conclusions, citraconic acid shows potential as a robust PMI estimator.

Classifying forensically important flies using deep learning to support pathologists and rescue teams during forensic investigations

Forensic entomology can help estimate the postmortem interval in criminal investigations. In particular, forensically important fly species that can be found on a body and in its environment at various times after death provide valuable information. However, the current method for identifying fly species is labor intensive, expensive, and may become more serious in view of a shortage of specialists. In this paper, we propose the use of computer vision and deep learning to classify adult flies according to three different families, Calliphoridae, Sarcophagidae, Rhiniidae, and their corresponding genera Chrysomya, Lucilia, Sarcophaga, Rhiniinae, and Stomorhina, which can lead to efficient and accurate estimation of time of death, for example, with the use of camera-equipped drones. The development of such a deep learning model for adult flies may be particularly useful in crisis situations, such as natural disasters and wars, when people disappear. In these cases drones can be used for searching large areas. In this study, two models were evaluated using transfer learning with MobileNetV3-Large and VGG19. Both models achieved a very high accuracy of 99.39% and 99.79%. In terms of inference time, the MobileNetV3-Large model was faster with an average time per step of 1.036 seconds than the VGG19 model, which took 2.066 seconds per step. Overall, the results highlight the potential of deep learning models for the classification of fly species in forensic entomology and search and rescue operations.

Estimation of the post-mortem interval; added value of mechanical excitability of human skeletal muscle

Estimation of the post-mortem interval (PMI) is mainly based on the state of body cooling, post-mortem lividity (livor mortis) and post-mortem muscle stiffness (rigor mortis). However, the time span of development of these post-mortem phenomena are influenced by a variety of factors concerning the body of the deceased and the environment in which the body is found. Subsequently, this leads to a substantial spread in upper and lower limits of PMI based on determination of the state of these phenomena. Moreover, interpretation of post-mortem phenomena like lividity, rigor and interpretation of the correction factor for Henssge's nomogram is subjective. For this reason, PMI estimations are often broad, possibly too broad to be helpful for answering questions which are relevant for the criminal investigation. Therefore, combining the outcome of different methods for estimating the PMI, the so-called compound method, is recommended. Supravital muscle reaction by mechanical stimulation of skeletal muscle is a less known aspect of the compound method. Here we present a series of cases series in which supravital muscle reaction contributed to a more precise estimation of the PMI.

Immunohistochemical expression of HMGB1 and related proteins in the skin as a possible tool for determining post-mortem interval: a preclinical study

Determining the post-mortem interval (PMI) is one of forensic pathology's primary objectives and one of its most challenging tasks. Numerous studies have demonstrated the accuracy of histomorphology and immunohistochemical investigations in determining the time of death. Nevertheless, the skin, a robust and easy-to-remove tissue, has only been partially analyzed so far. By studying 20 adult male mice, we tried to determine whether post-mortem immunohistochemical detection in the skin of HMGB1 proteins and associated components (Beclin1 and RAGE) could be used for this purpose. We discovered that nuclear HMGB1 overexpression indicates that death occurred within the previous 12 h, nuclear HMGB1 negativization with high cytoplasmic HMGB1 intensity indicates that death occurred between 12 and 36 h earlier and cytoplasmic HMGB1 negativization indicates that more than 48 h have passed since death. RAGE and Beclin1 levels in the cytoplasm also decreased with time. The latter proteins' negativization might indicate that more than 24 and 36 h, respectively, have passed from the time of death. These indicators might potentially be helpful in forensic practice for determining the PMI using immunohistochemistry.

Technical note: Excel spreadsheet calculation of the Henssge equation as an aid to estimating postmortem interval

In forensic cases for which the time of death is unknown, several methods are used to estimate the postmortem interval. The quotient (Q) defined as the difference between the rectal and ambient temperature (Tr - Ta) divided by the initial difference (T0 - Ta) represents the progress of postmortem cooling: Q = (Tr - Ta)/(T0 - Ta), (1 ≥ Q ≥ 0). Henssge was able to show that with the body weight and its empirical corrective factor, Q can be reasonably predicted as a double exponential decay function of time (Qp(t)). On the other hand, actual Q is determined as Qd by measuring Tr and Ta under an assumption of T0 = 37.2 °C. Then, the t value at which Qp(t) is equal to Qd (Qd=Qp(t)) would be a good estimate of the postmortem interval (the Henssge equation). Since the equation cannot be solved analytically, it has been solved using a pair of nomograms devised by Henssge. With greater access to computers and spreadsheet software, computational methods based on the input of actual parameters of the case can be more easily utilized. In this technical note, we describe two types of Excel spreadsheets to solve the equation numerically. In one type, a fairly accurate solution was obtained by iteration using an add-in program Solver. In the other type (forward calculation), a series of Qp(t) was generated at a time interval of 0.05 h and the t value at which Qp(t) was nearest to Qd was selected as an approximate solution using a built-in function, XLOOKUP. Alternatively, a series of absolute values of the difference between Qd and Qp(t) (|Dq(t)| = |Qd - Qp(t)|) was generated with time interval 0.1 h and the t value that produces the minimum |Dq(t)| was selected. These Excel spreadsheets are available as Supplementary Files.

Technical note: Practical application of post-mortem mechanical stimulation of skeletal muscle, a field study

BACKGROUND

Estimation of the post-mortem interval (PMI) is a crucial aspect in crime scene investigation. PMI is defined as the time between the moment of death and the moment of finding the dead body. A combination of methods for estimating the PMI in forensic casework is internationally mostly used. Supravital muscle reaction (SMR) is one of those methods. SMR is an idiomuscular contraction and can be provoked by mechanical stimulation.

OBJECTIVES

A field study was carried out with the aim to investigate whether a reflex hammer can be used as tool for triggering an idiomuscular contraction and, furthermore, to determine if a learning period has to be taken into account by a forensic physician for appropriate application of a reflex hammer to trigger SMR.

METHODS

From January 2017 to January 2022, four forensic physicians used this SMR by mechanically stimulating the musculus brachioradialis and musculus biceps brachii. In total, 332 cases were included with a PMI less than 24 h. The cases were divided in chronological clusters of 20 cases. The ratio of the number of positive SMR versus the total number stimulations per forensic physician was used as a measure of accuracy of a reflex hammer for triggering SMR. The distribution of the data was analyzed by comparing the clusters in chronological order to assess whether a learning curve applies.

RESULTS

In 55.7%, a muscle reaction could be provoked by mechanical stimulation. Comparable outcome of SMR between the participating physicians was observed after 40 stimulations.

CONCLUSION

A reflex hammer is usable for provoking SMR. A learning period has to be taken in to account during the first forty cases per forensic physician.

Metabolomics investigation of post-mortem human pericardial fluid

INTRODUCTION

Due to its peculiar anatomy and physiology, the pericardial fluid is a biological matrix of particular interest in the forensic field. Despite this, the available literature has mainly focused on post-mortem biochemistry and forensic toxicology, while to the best of authors' knowledge post-mortem metabolomics has never been applied. Similarly, estimation of the time since death or post-mortem interval based on pericardial fluids has still rarely been attempted.

OBJECTIVES

We applied a metabolomic approach based on 1H nuclear magnetic resonance spectroscopy to ascertain the feasibility of monitoring post-mortem metabolite changes on human pericardial fluids with the aim of building a multivariate regression model for post-mortem interval estimation.

METHODS

Pericardial fluid samples were collected in 24 consecutive judicial autopsies, in a time frame ranging from 16 to 170 h after death. The only exclusion criterion was the quantitative and/or qualitative alteration of the sample. Two different extraction protocols were applied for low molecular weight metabolites selection, namely ultrafiltration and liquid-liquid extraction. Our metabolomic approach was based on the use of 1H nuclear magnetic resonance and multivariate statistical data analysis.

RESULTS

The pericardial fluid samples treated with the two experimental protocols did not show significant differences in the distribution of the metabolites detected. A post-mortem interval estimation model based on 18 pericardial fluid samples was validated with an independent set of 6 samples, giving a prediction error of 33-34 h depending on the experimental protocol used. By narrowing the window to post-mortem intervals below 100 h, the prediction power of the model was significantly improved with an error of 13-15 h depending on the extraction protocol. Choline, glycine, ethanolamine, and hypoxanthine were the most relevant metabolites in the prediction model.

CONCLUSION

The present study, although preliminary, shows that PF samples collected from a real forensic scenario represent a biofluid of interest for post-mortem metabolomics, with particular regard to the estimation of the time since death.

Subaerial Decomposition of Small-Sized Remains in The Netherlands: Important Findings Regarding the PMI of a Four-Year Taphonomic Study

Studying post-mortem changes based on signs of decomposition (e.g., using scoring models) is one of the methods used in scientific studies to relate observable changes to the post-mortem interval (PMI). The majority of the studies on cadaver decomposition are based on large cadavers. There is limited literature on the decomposition pattern and rate of small cadavers, even though it is at least as important to be able to estimate the PMI for infants and subadults. Therefore, it is crucial to acquire knowledge of the decomposition process of child-sized remains. To fill this knowledge gap, a season-based subaerial outdoor decomposition study was conducted with small pig cadavers at the Forensic Anthropological Outdoor Research Facility located in Den Ham, The Netherlands, over a period of 4 years. Den Ham is located in the eastern part of the Netherlands, close to the German border, and has a temperate maritime climate, with a Cfb classification according to the Köppen-Geiger system. Salient findings were acquired during the decomposition study, specifically regarding a deviating decomposition rate during winter and the subsequent spring, reproducibility, the effect of body weight, post-mortem movement, the effect of heavy rainfall on insect activity, delayed bloating, the interaction of different insect species, and invertebrate activity. This article includes a systemic review of the results obtained during this four-year decomposition study and discusses the impact of the findings on the estimation of the PMI.

Post-mortem interval estimation using miRNAs of road traffic accident cases: A forensic molecular approach

In forensic examination accurate estimation of post-mortem interval (PMI) is a challenging task, particularly in the advanced stages of decomposition. The existing methods (algor mortis, livor mortis, rigor mortis, putrefaction etc) used for estimating PMI rely on analyzing the physical, biochemical, and metabolic changes that occur in the corpse after death. While these methods have shown some level of effectiveness in estimating PMI during the early stages of decomposition, accurate estimation becomes increasingly challenging during the later stages of putrefaction when the body undergoes significant changes. Recently, microRNA (miRNA) profiling due to its relatively small size and stability has emerged as a promising tool in several areas of forensics. This study demonstrates the potential of miRNA for PMI estimation in advanced stages of death. In this study, miRNA-195, miRNA-206, and miRNA-378 were selected as target miRNAs and miRNA-1 as reference miRNA. Left ventricle tissue (5 g) of the heart from 20 forensic autopsies of traffic accident victims (18-32 years) were collected and processed. The samples were held at room temperature for eight different time intervals (12, 24, 48, 72, 96, 120, 168 and 196 h), and RNA was extracted from all the samples using Trizol-based RNA isolation protocol, followed by cDNA synthesis and amplification with commercially available specific miRNA probes in Real-Time PCR (RT-PCR), Ct was calculated. The result showed that miRNAs were associated with PMI. Over time, there were substantial changes in the Ct values of all three miRNAs, with significant reductions observed at 196 h compared to 12 h. miRNA-206 demonstrated significant changes at multiple time intervals, while miRNA-1 remained stable for up to 196 h and thus holds caas an endogenous marker. In conclusion, miRNA has the potential to serve as a valuable tool for estimating PMI, especially during the advanced stages of decomposition, when used in conjunction with established techniques. However, further validation of the study is required to obtain more accurate estimates of PMI.

Detection of Fatal Potassium Overdose: A Case Report and Review of the Literature

Potassium overdose usually occurs accidentally, but potassium is also used for judicial executions, assisted death, and, rarely, suicides. In addition to exogenous overdose, various drugs, and renal failure, diabetic ketoacidosis can cause hyperkalemia. Potassium tablets are used in most cases of suicidal potassium overdose. Suicide by intravenous administration of potassium is rare but usually fatal. The author reports a rare case of suicide with potassium infusion. Autopsy and histology findings, along with post-mortem biochemical analysis of different body fluids and fluid from the infusion set, are reported. Previously published reports of potassium overdose were reviewed, and the detection possibilities of potassium overdose are discussed. The detection possibilities of lethal hyperkalemia are very limited since hyperkalemia produces only nonspecific autopsy and histology findings. Post-mortem potassium concentrations are not indicative of ante-mortem potassium concentrations; therefore, post-mortem biochemical analysis has limited value in determining potassium overdose. The best way to prove potassium overdose is via the collection and analysis of circumstantial evidence.

Next-generation time of death estimation: combining surrogate model-based parameter optimization and numerical thermodynamics

The postmortem interval (PMI), i.e. the time since death, plays a key role in forensic investigations, as it aids in the reconstruction of the timeline of events. Currently, the standard method for PMI estimation empirically correlates rectal temperatures and PMIs, frequently necessitating subjective correction factors. To address this shortcoming, numerical thermodynamic algorithms have recently been developed, providing rigorous methods to simulate postmortem body temperatures. Comparing these with measured body temperatures then allows non-subjective PMI determination. This approach, however, hinges on knowledge of two thermodynamic input parameters, which are often irretrievable in forensic practice: the ambient temperature prior to discovery of the body and the body temperature at the time of death (perimortem). Here, we overcome this critical limitation by combining numerical thermodynamic modelling with surrogate model-based parameter optimization. This hybrid computational framework predicts the two unknown parameters directly from the measured postmortem body temperatures. Moreover, by substantially reducing computation times (compared with conventional optimization algorithms), this powerful approach is uniquely suited for use directly at the crime scene. Crucially, we validated this method on deceased human bodies and achieved the lowest PMI estimation errors to date (0.18 h ± 0.77 h). Together, these aspects fundamentally expand the applicability of numerical thermodynamic PMI estimation.

A Fly in the Ointment: How to Predict Environmentally Driven Phenology of an Organism That Partially Regulates Its Microclimate

Phenological models representing physiological and behavioral processes of organisms are used to study, predict, and optimize management of ecological subsystems. One application of phenological models is the prediction of temporal intervals associated with the measurable physiological development of arthropods, for the purpose of estimating future time points of interest such as the emergence of adults, or estimating past time points such as the arrival of ovipositing females to new resources. The second of these applications is of particular use in the conduct of forensic investigations, where the time of a suspicious death must be estimated on the basis of evidence, including arthropods with measurable size/age, found at the death scene. Because of the longstanding practice of using necrophagous insects to estimate time of death, standardized data and methods exist. We noticed a pattern in forensic entomological validation studies: bias in the values of a model parameter is associated with improved model fit to data, for a reason that is inconsistent with how the models used in this practice are interpreted. We hypothesized that biased estimates for a threshold parameter, representing the lowest temperature at which insect development is expected to occur, result in models’ accounting for behavioral and physiological thermoregulation but in a way that results in low predictive reliability and narrowed applicability of models involving these biased parameter estimates. We explored a more realistic way to incorporate thermoregulation into insect phenology models with forensic entomology as use context, and found that doing so results in improved and more robust predictive models of insect phenology.