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

A review of the advances in insect evidence analysis for estimating postmortem interval and detecting drug toxicity.

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.

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. 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. 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.

New contributions to the relationship between sequential changes of ATP-related metabolites and post-mortem interval in rats.

In the evolving field of forensic medicine, artificial intelligence (AI) technologies may revolutionize traditional autopsy practices by enhancing the precision and efficiency of postmortem examinations. A review of the literature was carried out on the Pub-med and Scopus search engines by inserting the keywords "artificial intelligence" AND "forensic" AND ("autopsy" OR "crime scene management" OR "forensic odontology" OR "post mortem interval" OR "forensic anthropology" OR "forensic sciences"). The works that analyzed the applications of artificial intelligence in the forensic and autopsy field were analyzed. The results showed the application of different forms of artificial intelligence such as machine learning, deep learning, robotics, artificial neural networks. Various applications are therefore possible in the autopsy field including forensic identification, analysis of radiological data through Virtopsy, estimation of the weapon used through analysis of firearm damage with ballistics, estimation of the Post-Mortem Interval (PMI), forensic toxicology. AI's potential to aid in the precise identification of causes of death, estimation of postmortem intervals. With forensic pathologists facing the constant challenge of making accurate diagnoses under pressure, AI applications can offer much-needed support by reducing subjective judgment and the inherent human error due to fatigue. Therefore, the integration of AI into autopsies, while promising in terms of efficiency and accuracy, demands a careful balance between technological advancement and ethical responsibility to ensure trust and integrity in forensic practices.

Accurate post-mortem interval (PMI) estimation is crucial in forensic investigations, and metabolomic analysis of bodily fluids offers a promising approach. However, the suitability of ocular fluids for PMI estimation remains underexplored. Objective: The objective of this study was to evaluate the suitability of metabolomic analysis of ocular fluids, specifically vitreous humor (VH) and aqueous humor (AH), compared to serum for post-mortem interval (PMI) estimation and to identify the most promising metabolites for this purpose. Methods: A cross-sectional study was conducted at a tertiary care hospital in Karachi. Blood, AH, and VH samples were collected from human cadavers with PMIs ranging from 5 to 60 hours. Samples were prepared by removing proteins and lipids, followed by lyophilization. Metabolomic profiling was performed using an NMR Spectrometer. Pearson correlation coefficients were calculated to assess the relationship between metabolite concentrations and PMI. Results: The study identified 24 metabolites in blood, AH, and VH samples. Several metabolites, including glutamate, choline, glycine, and formate, exhibited significant positive correlations with PMI in all three fluids, with Pearson coefficients greater than 0.5. Notably, glutamate (blood: r = 0.585**, AH: r = 0.488*, VH: r = 0.683**), choline (blood: r = 0.435*, AH: r = 0.691**, VH: r = 0.713**), and glycine (blood: r = 0.553*, AH: r = 0.598**, VH: r = 0.690**) showed strong correlations. Conversely, glucose and pyruvate significantly negatively correlated with PMI in AH and VH. Conclusion: The findings indicate that VH and AH are more reliable than serum for PMI estimation due to their more stable metabolite profiles. Glutamate, choline, and glycine emerged as particularly promising biomarkers. The study supports the potential of metabolomic analysis of ocular fluids in forensic investigations to provide accurate PMI estimations.

Circular RNAs (circRNAs) are conserved, abundant, stable, and specifically expressed in mammals. The postmortem interval (PMI) estimation is crucial in forensic medicine, particularly for case investigation and civil action. CircRNAs may serve as ideal PMI biomarkers. However, no research has explored PMI estimation in the brain using circRNAs. The total RNA, including circRNA, was sampled from mouse brain tissues at multiple temperatures (4℃, 25℃, and 35℃). The semi-quantitative reverse transcription (RT)-PCR and real-time quantitative PCR (RT-qPCR) were used to test the postmortem degradation levels at different PMIs. As a result, we found circFat3 is highly and specifically expressed in mouse brain tissue, with postmortem levels significantly correlated with PMI across multiple temperatures. In addition, mt-co1 and 28 S rRNA demonstrated stability under various temperature conditions, supporting their use as reliable reference genes for PMI models. Moreover, the error rates showed that the circFat3/28S rRNA model was more accurate at 4℃. The circFat3/mt-co1 and circFat3/28S rRNA models provided slightly better predictions for short-term and long-term PMI, respectively at 25℃, while the circFat3/mt-co1 model was more accurate at 35℃. The combined application of the two reference genes was beneficial primarily for long-term PMI estimation. Furthermore, the validation results confirmed that these models were more accurate for long-term PMI estimation. Thus, our mathematical models were constructed at multiple temperatures based on circFat3 and these two reference genes. Taken together, this is the first study to identify circRNA circFat3 as a novel biomarker that may serve as a complementary tool for PMI 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.

To present recent advances in forensic sciences with omics sciences and new biomarkers for postmortem interval (PMI) estimation. We conducted a narrative review screening PubMed and Scopus databases in the last 10 years (2015-2025) with the following keywords in the title and abstract: "postmortem interval" OR "post-mortem interval" AND "proteomics" OR "proteomic" OR "metabolomics" OR "metabolomic" OR "transcriptomic" OR transcriptomics" OR microRNA" OR "microRNAs" OR "lipidomic". Conventional methods of postmortem interval estimation are presented. Some of the most important studies and molecular techniques in genomics, transcriptomics, proteomics, metabolomics, lipidomics, old and new biomarkers for postmortem interval estimation are summarized. Single-omics or multi-omics, critical issues like data reproducibility and interpretation, judicial validity according to Daubert standard and ethical issues of PMI research are discussed. Postmortem interval estimation continues to be one of the most disputed issues of forensic medicine. Conventional methods for PMI estimation still offer a solid bench for practical means. As single-omics and multi-omics research continues to progress, we will likely discover new biomarkers and innovative techniques. Efforts will focus on identifying biomarkers that can deliver reliable and predictable outcomes, thereby facilitating their general acceptance and admissibility in legal proceedings.

Estimation of early postmortem intervals by a multiple regression analysis using rectal temperature and non-temperature based postmortem changes

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. 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. 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. 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. 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.

Biochemical analysis of vitreous humor and synovial fluid in the estimation of early postmortem interval: A meta-analytical approach

Classical methods for postmortem interval (PMI) estimation have been applied for nearly a century. Contrary to the notion of being simple or easily accessible, these approaches require highly specialized training, including a medical degree, postgraduate specialization in forensic pathology, and extensive practical experience. Classical PMI estimation relies on observable physical and chemical changes in the human cadaver, such as rigor mortis, livor mortis, algor mortis, and transformative processes during decomposition. These methods are fundamental in medicolegal practice but remain largely influenced by environmental and individual variability. Recent advances in forensic research, particularly in microbiology and biochemistry, have introduced innovative approaches that complement traditional methods, offering greater accuracy and reliability, though resource-intensive. Emerging approaches leverage the predictable postmortem succession of microbial communities (thanatomicrobiome) and biochemical alterations in cadaver fluids and tissues. Techniques such as metagenomics, metatranscriptomics, and metabolomics enable detailed analysis of these changes, while computational models and machine learning further refine PMI estimates. Despite advancements, challenges persist, including variability due to environmental factors and limited access to human decomposition data. Integrating multi-omics approaches and artificial intelligence offers a path forward, addressing these limitations and enhancing the accuracy of PMI estimation. This review provides a comprehensive overview of PMI estimation, critically examining classical approaches and highlighting cutting-edge methodologies rooted in thanatomicrobiology and thanatochemistry. We emphasize the transformative potential of multi-omics integration and artificial intelligence in improving PMI accuracy. Importantly, we propose a paradigm shift: redefining PMI estimation through evidence-based, interdisciplinary research that bridges scientific rigor and judicial application. Transdisciplinary collaboration and standardized methodologies will be essential to translate emerging knowledge into robust forensic tools that serve both science and justice.

Traditional postmortem interval (PMI) estimation methods rely on observable changes such as rigor mortis, livor mortis, and algor mortis but are often affected by environmental factors. Metabolomics, combined with techniques like nuclear magnetic resonance (NMR) and mass spectrometry, improves accuracy by identifying biochemical changes postmortem. Machine learning methods such as Principal Component Analysis (PCA), Partial Least Squares (PLS), and Support Vector Machines (SVMs), enhance PMI predictions by analyzing metabolite data. This review aims to summarize advances in using machine learning for PMI estimation and identify the optimal combination of tissue samples and algorithms for accurate predictions. We retrieved relevant articles up to September 2024 from PubMed, Scopus, Web of Science, IEEE, and Cochrane Library. Data were extracted from eligible studies by two independent reviewers. This included the number and species of subjects, tissue sample used, PMI range in the study, metabolic profiling technique, machine learning algorithms, potential PMI markers, and model performance. We compared machine learning models for PMI estimation across various tissues. Zhang et al. (2022) had the best performance with a random forest (RF) model using cardiac blood, achieving a mean absolute error (MAE) of 1.067h by selecting key metabolites. Wu et al. (2017) followed with an orthogonal signal-corrected PLS model (R2 > 0.99, MAE 1.18-2.37h). Lu et al. (2022) achieved 93% accuracy with a multi-organ stacking model. Other promising models include Zhang et al.'s (2017) nu-SVM on pericardial fluid (RMSE = 2.38h) and Sato et al.'s (2015) PLS model on cardiac blood (MAE = 5.73h). Cardiac blood is best for short PMIs with random forest models, while skeletal muscle and stacking models excel for longer PMIs. Future studies should refine and validate these findings as well as extend the findings to human subjects.

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.