Estimation Of Postmortem Interval Research Articles

The mouse gingiva and HIF-1α, a key gene of hypoxic environment, as tools for post-mortem time estimation.

The post-mortem interval (PMI) is the time elapsed between the death of an individual and its forensic examination. It is a crucial information for judicial authorities, but current techniques still cannot establish a precise time interval. Novel approaches are therefore required. Recently, gingival tissue has emerged as interesting for forensic analysis thanks to the protection offered by lips to this tissue, limiting the influence of environmental factors. It is also easily accessible, and its sampling is minimally invasive even in the presence of rigor mortis. Moreover, the expression of HIF-1α, a master mediator of the hypoxic environment, has been described in gingival samples at different post-mortem (PM) times. We have hypothesized that the time-dependent post-mortem expression of HIF-1α could serve as a biomarker to more accurately predict the PMI. Our analyses were performed in an animal model, the mouse, where environment can be precisely controlled. Therewith, gingival tissue morphology was evaluated through histochemical staining and HIF-1α expression was analyzed by qPCR, western blots and immunofluorescence at different post-mortem times (0h to 100h). Our results showed (a) a global post-mortem stability of gingival tissue (b) a rapid increase in HIF-1α mRNA expression in the short post-mortem times followed by a slow decrease in transcript expression until 100h PM (c) an expression of the HIF- 1α protein and its degradation products, that follows the mRNA pattern (d) the presence of HIF-1α protein in the epithelial and connective layers of the tissue, with signal accumulation in both gingival strata until at least 32h post-mortem. This pilot study thus validated the mouse and the gingival tissue as models for post-mortem analyses, as well as for studying the fate of proteins such as HIF-1α. Transferring these approaches to human subjects may provide a more accurate estimate of PMI.

Raman Handheld Versus Microscopic Spectroscopy for Estimating the Post-Mortem Interval of Human Bones: A Comparative Pilot Study

The post-mortem interval estimation for human skeletal remains is critical in forensic medicine. This study used Raman spectroscopy, specifically comparing a handheld device to a Raman microscope for PMI estimations. Analyzing 99 autopsy bone samples and 5 archeological samples, the research categorized them into five PMI classes using conventional methods. Key parameters—like ν1PO43− intensity and crystallinity—were measured and analyzed. A principal component analysis effectively distinguished between PMI classes, indicating high classification accuracy for both devices. While both methods proved reliable, the fluorescence interference presented challenges in accurately determining the age of archeological samples. Ultimately, the study highlighted how Raman spectroscopy could enhance PMI estimation accuracy, especially in non-specialized labs, suggesting the potential for improved device optimization in the field.

Altitudinal variations in forensically relevant dipterans in Trentino Region (Italy): implications for PMI estimation and forensic ecology.

This study investigates the populations of dipterans in the southeastern Prealps of Trentino, Italy, along an altitudinal gradient that exceeds 1000m. The study is important because dipterans play a significant role in crime scene analysis by helping to determine the post-mortem interval (PMI) and understand corpse relocation dynamics. Nine aerial traps were used across three sites from May to November 2023, and a total of 17,876 individuals from diverse species were captured. Statistical analyses revealed significant differences in dipteran populations across sites and exposure levels. The study identified relationships between species and environmental factors such as altitude, temperature, and sunlight exposure using Canonical Correspondence Analysis (CCA). Results demonstrated that species composition varied with environmental conditions, offering insights into potential shifts due to climate change. The presence of specific species was notably affected by temperature fluctuations, which could impact their usefulness in PMI estimation. Continuous monitoring is crucial to track dipteran population dynamics amidst changing environmental conditions. Such knowledge is important for improving accuracy in PMI estimations and enhancing forensic investigations. In conclusion, ongoing research is pivotal in adapting forensic entomological analyses to evolving ecological contexts, ensuring their reliability in forensic science applications. This study highlights the dynamic nature of dipteran ecology within forensic contexts and emphasises the need for further investigation to observe shifting population dynamics under climate change impacts.

Influence of Drugs and Toxins on Decomposition Dynamics: Forensic Implications

Drug and toxin-related deaths are common worldwide, making it essential to detect the postmortem concentration of various toxic substances at different stages of decomposition in a corpse. Indeed, determining the postmortem interval (PMI) and cause of death in an advanced stage of decomposed corpses has been a significant challenge in forensic investigations. Notably, the presence of drugs or toxins can have a significant impact on the microbial profile, potentially altering the succession of microbial communities and subsequent production of volatile organic compounds (VOCs), which, in turn, affect insect colonization patterns. This review aims to highlight the importance of investigating the interactions between drugs or toxins, microbial succession, VOC profiles, and insect behavior, which can provide valuable insights into forensic investigations as well as the ecological consequences of toxins occurring in decomposition. Overall, the detection of drugs and other toxins at different stages of decomposition can yield more precise forensic evidence, thereby enhancing the accuracy of PMI estimation and determination of the cause of death in decomposed remains.

Visual analysis of postmortem interval estimation trends and collaborative networks: a 15-year study (2006-2020).

Utilizing a visual analysis of the literature on postmortem interval (PMI) estimation indexed by Web of Science (WOS), this study investigates developmental trends and research hot points across each 5-year period from 2006 to 2020. Additionally, collaborative efforts among authors, countries, and institutions were examined. Research hot points, high-frequency keywords, authors, countries and institutions in relevant papers were analyzed using CiteSpace.5.7.R2 information visualization analysis software over the past 15 years. The literature related to PMI estimation has witnessed consistent growth over time. In the keyword co-occurrence network, several impactful terms stand out, including blowfly, mitochondrial DNA, and emerging concepts like virtual autopsy. Technological advancements, such as RNA stability analysis and virtual autopsy tools, have played a pivotal role in shaping the direction of PMI research. Scientific research institutions dominate the high-frequency affiliations within the institutional cooperative network. Additionally, the country cooperative network exhibits a trend of co-occurrence and multi-clustering. As science and technology continue to advance, traditional PMI estimation methods mature while novel interdisciplinary approaches drive innovation. By identifying emerging trends and research hotspots, this study provides a roadmap for future investigations, guiding researchers toward new opportunities in PMI estimation.

Multimodal Approaches Based on Microbial Data for Accurate Postmortem Interval Estimation

Accurate postmortem interval (PMI) estimation is critical for forensic investigations, aiding case classification and providing vital trial evidence. Early postmortem signs, such as body temperature and rigor mortis, are reliable for estimating PMI shortly after death. However, these indicators become less useful as decomposition progresses, making late-stage PMI estimation a significant challenge. Decomposition involves predictable microbial activity, which may serve as an objective criterion for PMI estimation. During decomposition, anaerobic microbes metabolize body tissues, producing gases and organic acids, leading to significant changes in skin and soil microbial communities. These shifts, especially the transition from anaerobic to aerobic microbiomes, can objectively segment decomposition into pre- and post-rupture stages according to rupture point. Microbial communities change markedly after death, with anaerobic bacteria dominating early stages and aerobic bacteria prevalent post-rupture. Different organs exhibit distinct microbial successions, providing valuable PMI insights. Alongside microbial changes, metabolic and volatile organic compound (VOC) profiles also shift, reflecting the body’s biochemical environment. Due to insufficient information, unimodal models could not comprehensively reflect the PMI, so a muti-modal model should be used to estimate the PMI. Machine learning (ML) offers promising methods for integrating these multimodal data sources, enabling more accurate PMI predictions. Despite challenges such as data quality and ethical considerations, developing human-specific multimodal databases and exploring microbial–insect interactions can significantly enhance PMI estimation accuracy, advancing forensic science.

Enhancing late postmortem interval prediction: a pilot study integrating proteomics and machine learning to distinguish human bone remains over 15 years

BackgroundDetermining the postmortem interval (PMI) accurately remains a significant challenge in forensic sciences, especially for intervals greater than 5 years (late PMI). Traditional methods often fail due to the extensive degradation of soft tissues, necessitating reliance on bone material examinations. The precision in estimating PMIs diminishes with time, particularly for intervals between 1 and 5 years, dropping to about 50% accuracy. This study aims to address this issue by identifying key protein biomarkers through proteomics and machine learning, ultimately enhancing the accuracy of PMI estimation for intervals exceeding 15 years.MethodsProteomic analysis was conducted using LC–MS/MS on skeletal remains, specifically focusing on the tibia and ribs. Protein identification was performed using two strategies: a tryptic-specific search and a semitryptic search, the latter being particularly beneficial in cases of natural protein degradation. The Random Forest algorithm was used to model protein abundance data, enabling the prediction of PMI. A thorough screening process, combining importance scores and SHAP values, was employed to identify the most informative proteins for model’s training and accuracy.ResultsA minimal set of three biomarkers—K1C13, PGS1, and CO3A1—was identified, significantly improving the prediction accuracy between PMIs of 15 and 20 years. The model, based on protein abundance data from semitryptic peptides in tibia samples, achieved sustained 100% accuracy across 100 iterations. In contrast, non-supervised methods like PCA and MCA did not yield comparable results. Additionally, the use of semitryptic peptides outperformed tryptic peptides, particularly in tibia proteomes, suggesting their potential reliability in late PMI prediction.ConclusionsDespite limitations such as sample size and PMI range, this study demonstrates the feasibility of combining proteomics and machine learning for accurate late PMI predictions. Future research should focus on broader PMI ranges and various bone types to further refine and standardize forensic proteomic methodologies for PMI estimation.

Postmortem interval estimation of human skeletonized remains through luminol chemiluminescence.

Postmortem interval (PMI) estimation represents a significant challenge in the forensic sciences, particularly when dealing with human skeletal remains. A screening protocol for distinguishing possible remains of forensic interest is a crucial tool for judicial purposes. In this context, luminol chemiluminescence emerges as a promising method, with low overall costs and required time. This method is primarily used as a presumptive test, based on the understanding that the intensity of the chemiluminescence reactions decreases with an increase in the postmortem interval, thus underlining its practical implications.This research aims to expand previous research on the potential of luminol chemiluminescence, evaluating its usefulness in estimating PMI. Our sample comprised 239 human clavicles, with known PMI. The luminol solution was sprayed on each powder bone sample in a dark room, observed by the naked eye and photographed. The intensity of the chemiluminescence reaction was measured using a binary and a 5-level scale.The present results reveal that this method is a suitable tool for PMI estimation as a presumptive test, reducing time and costs in criminal investigations. The findings underscore the high sensitivity of luminol chemiluminescence for detecting recent PMI but also highlight a notable incidence of false positives. Thus, our results confirm luminol chemiluminescence as a powerful tool for dating time of death, particularly for identifying forensic relevant remains. Still, the relatively low specificity indicates that it should be complemented with additional tests for further confirmation and scientific validation of the remains' forensic relevance.

The impact of burnt carcass on the occurrence probability of Chrysomya albiceps and Lucilia ochricornis (Diptera: Calliphoridae) in southern Rio Grande do Sul, Brazil

We evaluated the impact of burnt carcasses on the probability of finding Chrysomya albiceps (Wiedemann, 1819) and Lucilia ochricornis (Wiedemann, 1830) (Diptera: Calliphoridae), and the time it took for them to arrive at the experimental carcasss. These species are biological indicators of the postmortem interval (PMI) in forensic scenarios. Using stillborn pig carcasses, this study analysed how different degrees of burning affect the level of attraction and colonisation by these species. Experimental models were subjected to level 2 (CG2) and 4 (CG4) burning according to the Crow-Glassman (CG) scale, ranging from fresh to skeletonization. Generalised Linear Models (GLM) with a Binomial distribution were used to evaluate the influence of decomposition time and experimental treatment on the probability of occurrence of each species. Additionally, survival analyses with the Weibull distribution were used to investigate how long it takes until the arrival of the first fly species in each experimental group. The results indicate that burning affects the probability of occurrence and colonisation time of the studied fly species. Although both species were affected by high degrees of burning, Lucilia ochricornis was more heavily affected than Chrysomya albiceps. This pattern was reflected in the time it took for the first occurrence of each fly species. In both cases, it took longer for flies to arrive at carcasses that had been more severely burnt. In the case of C. albiceps, the longest mean time until a fly was detected (Weibull, α = 79.9 h) happened in the CG4 group, followed by the CG2 group (α = 65.6 h) and the control group (α = 51.5 h). In contrast, for L. ochricornis, the longest mean first occurrence time (α = 85.4 h) was in CG2 group, followed by the Control (α = 49.1 h) and CG4 (α = 54.4 h) groups. This study emphasises the importance of considering whether or not a carcass was burnt and its degree of burning in forensic investigations, given that this variable can influence the accuracy of PMI estimation in crime scenes.

The application of GIS technology in building a multivariate taphonomic profile for improving PMI estimations in Greece.

Environmental conditions highly affect decomposition rates and therefore a forensic practitioner should consider context-specific information when estimating the post mortem interval (PMI). Traditional methods of collecting environmental data, however, are time-consuming and often impractical for large-scale studies or routine forensic investigations. This study developed an automated computer method by employing the technology of geographic information systems (GIS) and Python programming language to provide contextual information for bodies found outdoors in Greece. The generated coding script underwent testing on 95 bodies in various stages of decomposition, which were examined between the years 1999 and 2022 at the National and Kapodistrian University of Athens and the Forensic Medical Service of Thessaloniki. Using ArcGIS Pro software and publicly available online data, a multilayer map was developed. Individual layers included high-resolution aerial images and data on the European Nature Information System ecosystem type, the Köppen-Geiger climatic type, the population density, the elevation, and the slope. Additionally, 99 national weather stations and their corresponding meteorological data were integrated. By leveraging the geographical coordinates of the recovery site of each case and information about the decedent's disappearance and recovery dates, this script automatically generates details from each of the above layers. Additionally, it calculates the accumulated degree days (ADD) and accumulated humidity days (AHD) values by extracting data from the nearest weather station. The GIS-based approach enables rapid, objective, and reproducible taphonomic profile construction, which can greatly improve the reliability of PMI estimations. By utilizing this method, forensic practitioners can accurately evaluate environmental effects on decomposition, thus standardizing taphonomic profiling globally.

Environmental predictors impact microbial-based postmortem interval (PMI) estimation models within human decomposition soils.

Microbial succession has been suggested to supplement established postmortem interval (PMI) estimation methods for human remains. Due to limitations of entomological and morphological PMI methods, microbes are an intriguing target for forensic applications as they are present at all stages of decomposition. Previous machine learning models from soil necrobiome data have produced PMI error rates from two and a half to six days; however, these models are built solely on amplicon sequencing of biomarkers (e.g., 16S, 18S rRNA genes) and do not consider environmental factors that influence the presence and abundance of microbial decomposers. This study builds upon current research by evaluating the inclusion of environmental data on microbial-based PMI estimates from decomposition soil samples. Random forest regression models were built to predict PMI using relative taxon abundances obtained from different biological markers (bacterial 16S, fungal ITS, 16S-ITS combined) and taxonomic levels (phylum, class, order, OTU), both with and without environmental predictors (ambient temperature, soil pH, soil conductivity, and enzyme activities) from 19 deceased human individuals that decomposed on the soil surface (Tennessee, USA). Model performance was evaluated by calculating the mean absolute error (MAE). MAE ranged from 804 to 997 accumulated degree hours (ADH) across all models. 16S models outperformed ITS models (p = 0.006), while combining 16S and ITS did not improve upon 16S models alone (p = 0.47). Inclusion of environmental data in PMI prediction models had varied effects on MAE depending on the biological marker and taxonomic level conserved. Specifically, inclusion of the measured environmental features reduced MAE for all ITS models, but improved 16S models at higher taxonomic levels (phylum and class). Overall, we demonstrated some level of predictability in soil microbial succession during human decomposition, however error rates were high when considering a moderate population of donors.

Development of Piophila megastigmata (Diptera: Piophilida) at seven constant temperatures

In forensic entomology, the time-related growth and development of carrion insects allows for the estimation of the minimum postmortem interval (PMImin). Piophila megastigmata (Diptera: Piophilidae) is of great significance in estimating PMImin in the late stage of corpse decay. In this paper, the development of P. megastigmata was investigated under the seven constant temperatures of 16–34 °C. The total development time at each temperature was 970.38, 824.50, 593.13, 498.25, 392.00, 385.63 and 405.87 h, respectively. The isomorphen diagram shows the trend of development during different developmental stages at different temperatures. By using a revised linear regression model, the estimated lower lethal developmental thresholds (TL) and thermal summation constant (K) were found to be 10.57 °C and 6936.78 degree hours, respectively. According to a nonlinear model, the TL, upper lethal developmental thresholds (TH), and intrinsic optimum temperature (TΦ) were determined to be 7.53, 34.68, and20.43 °C, respectively. Also, logistic function and an isomegalen diagram were constructed according to the continuous changes in larval body length, and illustrates the time required to develop to a certain length at different temperatures. The obtained results offer crucial fundamental developmental information regarding P. megastigmata, which can be applied in PMImin estimation.

IdentiFLY: The Development and Validation of a 15-Plex SNP Assay for Forensic Identification of UK Blowfly Species (Calliphoridae)

Members of the blowfly family (Calliphoridae) are usually the first insect species to arrive at a corpse, using the body as an oviposition site, and, as such, they are the most important group of insects used to estimate the post-mortem interval (PMI). PMI estimations are based on species-specific developmental timings; therefore, accurate species identification is crucial. Current identification methods are based on morphological characteristics, which are time-consuming and difficult to perform on damaged, immature specimens and closely related species. Advances have led to specimens being identified via a host of molecular techniques, mainly DNA sequencing. Although molecular identification is becoming increasingly more common, there is currently a lack of genetic data regarding UK Calliphoridae species. This study aimed to address this issue. We present the development and validation of an identification assay capable of differentiating six UK species (Calliphora vicina, Calliphora vomitoria, Lucilia sericata, Lucilia illustris, Lucilia caesar, and Protophormia terranovae). The sequencing of six genes, including both nuclear (28S rRNA and Elongation factor 1 alpha) and mitochondrial markers (Cytochrome oxidase I and II, Cytochrome b and 16S rRNA) identified 298 species-specific single nucleotide polymorphisms (SNPs). Fifteen SNPs from six genes were chosen for inclusion in a SNaPshot™ multiplex assay. The developed assay is capable of differentiating the species based on between 4 and 12 SNPs. Validation following guidelines by the International Society of Forensic Genetics (ISFG) demonstrated the assay to be accurate, reproducible, sensitive, and specific.

Development of a Machine Learning Framework for Real-Time PMI (Post-Mortem Interval) Estimation in Field Forensics

Accurate estimation of the Post-Mortem Interval (PMI) is critical in forensic investigations, aiding in determining the time of death. However, traditional PMI estimation methods, often reliant on physiological observations and environmental factors, face significant limitations in accuracy and efficiency, especially in field conditions. This paper presents the development of a machine learning (ML) framework designed for real-time PMI estimation, integrating multimodal sensor data to address the challenges encountered in field forensics. Our framework utilizes environmental and physiological features, including body temperature, ambient humidity, and biochemical decomposition markers, to predict PMI with high precision. The ML model, trained on historical forensic data, is deployed on a real-time processing platform, enabling rapid analysis and decision-making in resource- constrained environments. The system is optimized for field operations, incorporating low-power hardware and edge computing capabilities to provide forensic investigators with reliable PMI estimates on-site. Through a series of controlled experiments simulating forensic scenarios, our framework demonstrates a significant improvement in PMI accuracy compared to traditional methods, while maintaining low latency for real-time applications. This research highlights the potential of machine learning to revolutionize forensic practices, offering a scalable and adaptive solution for time-sensitive investigations. Here are some relevant keywords for the development of a machine learning framework for real-time PMI (Post- Mortem Interval) estimation in field forensics: Keywords: Field Forensics, Real-Time Machine Learning, Body Decomposition Stages, Machine Learning in Forensic Science, Artificial Intelligence for PMI Analysis, Sensor Data in PMI Estimation, Deep Learning for PMI Estimation, Automated Forensic Analysis, Data Acquisition in Field Forensics.

Age estimation of Phormia regina pupae based on ATR-FTIR and chemometrics

Accurate postmortem interval estimation is vital in the investigation of homicides, suicides, and accidental deaths. It is key in narrowing suspect lists, improving crime-solving efficiency, and offering solace to bereaved families. The intra-puparial period, comprising about half of a fly’s developmental cycle, presents challenges for morphological age estimation. External changes are limited to color shifts and the appearance of respiratory horns on the puparium only within several hours after pupariation, while detailed internal development analysis often requires invasive methods like removing the puparium, which can be damaging. Additionally, these techniques usually depend on a forensic entomologist’s expertise, which lead to subjective biases. This study employed attenuated total reflection-fourier transform infrared spectroscopy, a rapid, non-destructive method for analyzing proteins, chitosan, and chitin in puparia. Data showed a consistent reduction in the concentration of the amide I band within the puparium during the intra-puparial development at five constant temperatures (19 °C, 22 °C, 25 °C, 28 °C and 31 °C). This trend in the spectral data effectively distinguishes pupae at various stages of intra-puparial development, facilitating precise age estimation, which is critical for the estimation of the minimum postmortem interval (PMImin). Finally, this work combined the total reflection-fourier transform infrared spectroscopy with chemometric analysis and successfully developed a partial least squares discriminant analysis model and a random forest model, with accuracies of 88 % and 81 %, respectively. These models enable the non-invasive age estimation of P. regina in its intra-puparial period, a stage traditionally difficult to assess morphologically, thus laying the groundwork for PMImin estimation using fly pupae.

Impact of ante-mortem fluoxetine administration on estimation of post-mortem interval and insect activity in rabbit carcasses

BackgroundIncreasing the number of drug-related deaths has affected medico-legal death investigations. Drugs within a corpse have a great impact on the insects’ development rate which in turn will affect the rate of post-mortem decomposition and the estimation of the post-mortem interval. This explains the importance of the application of forensic entomotoxicology, which studies the impact of drugs and toxins on the development and succession patterns of insects. The current study aimed to determine the impact of fluoxetine, one of the selective serotonin reuptake inhibitors (SSRIs), on post-mortem decomposition, insects’ attraction, and its pattern of succession on carcasses. Sixteen healthy male and female Oryctolagus cuniculus rabbits were chosen to be included in this study. They were divided into a treated group of eight rabbits received oral fluoxetine for 28 days and a control group of eight rabbits received oral distilled water for 28 days. After oral administration of 10 mg/kg/day of fluoxetine and distilled water for 28 days, rabbits were sacrificed, and carcasses were transmitted to the roof of Research and Training Centre on Vectors of Diseases at faculty of Sciences, Ain Shams University, for following up the post-mortem decomposition process and insect’s attraction to carcasses for 60 days. Carcasses were put in two outdoor sites that differ in temperature to assess the effect of temperature on decomposition process.ResultsFluoxetine administration has accelerated the rate of post-mortem decomposition in the treated carcasses by 3–9 days compared to the control ones and affected the numbers and species of attracted insects, while it had no effect on the insects’ succession patterns. Exposure of carcasses to direct sunlight has accelerated the rate of decomposition in comparison to that of carcasses put in shade in the overall period of decomposition by about 14 to 16 days.ConclusionsFluoxetine has an important and effective role in post-mortem decomposition and estimation of post-mortem interval (PMI) and has a great impact on attracted insects to the treated carcasses. Temperature has a great effect on the rate of decomposition of carcasses. Higher temperature accelerates the rate of post-mortem decomposition.

A nondestructive technique for the sex identification of third instar Cochliomyia macellaria larvae.

Forensic entomology plays an important role in medicolegal investigations by using insects, primarily flies, to estimate the time of colonization. This estimation relies on the development of the flies found at the (death) scene and can be affected (and sometimes corrected) by external factors, such as temperature and humidity, and internal factors, such as species and sex. This study leverages infrared (IR) spectroscopy combined with machine learning models-Partial Least Squares Discriminant Analysis (PLS-DA) and eXtreme Gradient Boosting trees Discriminant Analysis (XGBDA)-to differentiate between male and female Cochliomyia macellaria larvae, commonly found on human remains. Significant vibrational differences were detected in the infrared spectra of third instar C. macellaria larvae, with distinct peaks showing variations in relative absorbance between sexes, suggesting differences in biochemical compositions such as cuticular proteins and lipids. The application of PLS-DA and XGBDA yielded high classification accuracies of about 94% and 96%, respectively, with female spectra consistently having higher sensitivity than males. This non-destructive approach offers the potential to refine supplemental post-mortem interval estimations significantly, enhancing the accuracy of forensic analyses.

Artificial Intelligence in Forensic Sciences: A Systematic Review of Past and Current Applications and Future Perspectives.

The aim of this study is to review the available knowledge concerning the use of artificial Intelligence (AI) in general in different areas of Forensic Sciences from human identification to postmortem interval estimation and the estimation of different causes of death. This paper aims to emphasize the different uses of AI, especially in Forensic Medicine, and elucidate its technical part. This will be achieved through an explanation of different technologies that have been so far employed and through new ideas that may contribute as a first step to the adoption of new practices and to the development of new technologies. A systematic literature search was performed in accordance with the Preferred Reported Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines in the PubMed Database and Cochrane Central Library. Neither time nor regional constrictions were adopted, and all the included papers were written in English. Terms used were MACHINE AND LEARNING AND FORENSIC AND PATHOLOGY and ARTIFICIAL AND INTELIGENCEAND FORENSIC AND PATHOLOGY. Quality control was performed using the Joanna Briggs Institute critical appraisal tools. A search of 224 articleswas performed. Seven more articles were extracted from the references of the initial selection. After excluding all non-relevant articles, the remaining 45 articles were thoroughly reviewed through the whole text. A final number of 33 papers were identified as relevant to the subject, in accordance with the criteria previously established. It must be clear that AI is not meant to replace forensic experts but to assist them in their everyday work life.

MiR-122, miR-133a, and miR-206 as potential biomarkers for post-mortem interval estimation.

Accurate estimation of post-mortem interval (PMI) is crucial in forensic investigations. MicroRNAs (miRNAs or miRs) are small non-coding RNAs that remain relatively stable within the cell nucleus despite post-mortem changes. We assessed three target genes (miR-122, miR-133a, and miR-206) for PMI estimation using 72 healthy adult male BALB/c mice exposed to two different temperatures (4 and 21℃) at nine different time points over 10days. Initially, the stability of the two reference genes (RNU6B and 5 srRNA) was evaluated using gene stability analysis tools (Delta Ct, Best Keeper, and Genorm) to select the optimal reference gene. RNU6B was found to be the most stable endogenous control. Subsequently, the expression patterns of miR-122, miR-133a, and miR-206 were analyzed within a 10-day PMI period using the heart, skeletal muscle, liver, and brain tissues. At 4℃, miR-122 levels significantly decreased on days 8 and 10 in all tissues, with only the liver showing significant changes at 21℃. MiR-133a decreased over time in the heart, muscles, and brain, showing a dramatic decrease on days 8 and 10 in the heart and muscles at both temperatures. Although miR-206 levels decreased over time in muscles and liver at 4℃, these increased in the brain at 21℃, with no expression changes in other organs. In summary, miR-122, miR-133a, and miR-206 are potential PMI markers in heart and skeletal muscle tissues.

Post-mortem Interval estimate based on dental pulp: A histomorphology approach.

Estimating the post-mortem interval (PMI) of human remains based on the histomorphology of dental pulp parameters is promising, but available evidence is scarce and sometimes contradictory without a scientific model. The aim of the study is to characterise the histomorphological changes of dental pulp associated with the decomposition of human remains by a qualitative and quantitative approach. The main aim is to establish a correlation based on post-mortem (PM) dental pulp histomorphology and the PMI, and whether pulp degradation could be an available medico-legal tool for PMI estimation beyond the first week after death (late PMI). The eligible sample consisted of 27 sound teeth from 16 healthy patients aged 16 to 72 years due to orthodontic or oral surgery treatment, to create PMI's simulating the death of the subject as the time elapsed from tooth avulsion. Data collected from patients (sex, date of birth, tooth position, date and hour of the avulsion, date and hour of pulp extraction) were anonymised in accordance with the requirements of Faculty of Dental Medicine of the University of Lisbon. The sample was divided into 9 groups of 3 teeth according to different PMI sets from T0 (baseline) up to 2 weeks (T0, 7, 12, 24, 36, 48, and 72 hours, 1 and 2 weeks). All the dental samples were stored at room temperature up to the time of pulp extraction and then prepared with haematoxylin and eosin stain. High-resolution microscopy was performed to obtain histological images. An operator performed the qualitative evaluation of blood vessels, collagen fibres, and the extra-cellular matrix (ECM) in PM pulps and measured the variation in cells/nuclei density by counting 6 different ROIs (Regions of Interest) for each pulp manually and automatically (quantitative analysis). Qualitative results showed that the degeneration of dental pulp appears 7 hours after death but histological changes in vessels, fibres, and ECM in PM dental pulp are characterised by high variability, consequently it is not possible to generalise the results for early PMIs. Quantitative measurements proved that cell count cannot be standardised due to the presence of superimposed layers of cells and nuclei fragmentation. Odontoblasts did not demonstrate evidence of cellular or nuclear lysis up to 14 PM suggesting their applicability in late PMIs. Future research will focus on late PMIs and different techniques of tooth preparation.