Validating the Efficacy of archival microclimate proxies for indoor postmortem interval Estimation: A case study using Boettcherisca peregrina (Robineau-Desvoidy, 1830).

The biochemical changes of corpse are using forthe postmortem interval (PMI) estimation, but decomposition isvaried due to many factors. Ambient temperature is believed to be the most important factor which is related tobiochemical change in the human corpse by using accumulated degree hours (ADH). The aim of this researchwas to develop exponential formula to estimate the PMI based on a potassium and ADH as the parameters. Thesecondary data analysis is conducted on 3 articles published in Thailand. The correlation data were analyzed bypotassium, ADH, and time of death in each experiment. The temperature data was provided by the ThailandMeteorology Department (TMD). These results suggested that this developed method can use for PMI estimation.The additional step, namely to calculate the ADH from the finding date back until the predicted day of death fromcase reported and TMD. The regression analysis had a high reliability. The potassium level significantly estimatesthe PMI (R2 = 0.9183, 0.8296 and 0.7142 for pig vitreous, corpse vitreous and corpse blood, respectively). Whenusing the ADH in pig vitreous, the R2 increased to 0.9931. The developed potassium regression models are apractical method to measure PMI. The PMI can be estimated using this method, but caution is advised in case witha long PMI. It can be used to estimate the mortality time of the fresh-early decomposition stage, up to 48 hours.Due to the longer mortality, there may be a gradual rate of change and samples cannot be collected.

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.

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.

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.

Forensic entomology is a crucial discipline in forensic investigations, primarily used for postmortem interval (PMI) estimation, species identification, and crime scene reconstruction. Recent advancements in molecular techniques, computational models, and climate-adaptive forensic entomology have enhanced the field's forensic applications. However, challenges related to methodological standardization, environmental variability, and legal admissibility persist. This comprehensive review examines recent developments in forensic entomology, focusing on molecular methodologies, AI-assisted species identification, and environmental influences on forensic insect succession. Literature was sourced from Scopus, Web of Science, and PubMed, emphasizing peer-reviewed studies published in the last 2 decades. Key themes include PMI estimation advancements, forensic insect ecology, and legal challenges in forensic entomology. Findings highlight improved accuracy in PMI estimation through genetic and computational approaches, the expansion of forensic molecular entomology applications, and the impact of climate change on forensic insect activity. While AI and molecular forensics have revolutionized species identification, standardization and legal frameworks remain insufficient. The study underscores the need for globally harmonized forensic entomology protocols, forensic training programs, and ethical guidelines. Future research should integrate metagenomics, forensic AI, and climate-adaptive forensic models to enhance forensic accuracy and reliability.

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.

The decomposition of organic remains is a complex ecological process governed by intricate interactions among insects, microorganisms, and environmental factors. This review comprehensively examines the colonisation dynamics and behavioural ecology of insects associated with human cadavers and animal carcasses, emphasizing their significance in forensic investigations. Over the past two decades (2005-2025), research has advanced substantially in understanding the role of necrophagous Diptera and Coleoptera in mediating tissue breakdown and providing crucial evidence for postmortem interval (PMI) estimation. Early colonisers, such as blowflies (Calliphoridae) and flesh flies (Sarcophagidae), exhibit predictable oviposition, feeding, and breeding behaviours influenced by temperature, humidity, and habitat type. Comparative analyses reveal broad ecological similarities between human and animal decomposition but also highlight physiological and microbial differences affecting colonisation timing, volatile organic compound (VOC) emission, and insect succession. Recent studies integrating molecular diagnostics, microbial profiling, and volatile analysis have improved accuracy in species identification and PMI estimation. Furthermore, the emerging field of entomotoxicology has demonstrated the potential of insect larvae as biological indicators of drugs and toxicants in advanced decomposition. Environmental and ecological determinants-including microclimate, seasonality, soil type, and vegetation-profoundly influence decomposition trajectories and insect behaviour, particularly in tropical regions such as India. Despite these advancements, gaps persist in methodological standardization, region-specific reference datasets, and the effects of climate change on insect distribution. Future research should focus on standardized global protocols, molecular integration, and climate-adaptive forensic models to enhance precision and reproducibility. This synthesis underscores that a comprehensive understanding of insect behaviour and colonisation ecology is fundamental to modern forensic science, bridging biological theory with practical legal application.

A computational approach to estimate postmortem interval using opacity development of eye for human subjects

Postmortem interval arises one of the most important research topics in worldwide modern forensic science applications. In spite of utilizing morphological, biochemical, flow-cytometric, microbiological, entomological, anthropological, spectroscopic and main postmortem changes in postmortem interval estimation, it does not seem possible to get certain results by only one test or method. Because, there are many physical, chemical and biological processes affecting the parameters. Therefore, postmortem interval estimation needs development of previous methods and implementation of novel methods. In this regard, taphonomic methods need to be improved in postmortem interval estimation and regional factors and climate impact need to be determined by experimental studies. In particular, we are of the opinion that that more accurate estimation of postmortem interval will be achieved by determining regional factor involving postmortem period. This paper aims to evaluate the relationship between postmortem interval and accumulated degree days in respect of decomposition stages.Key Words: Forensic Taphonomy, Postmortem Interval, Forensic Anthropology

Estimates of insect age can be informative in death investigations and, when certain assumptions are met, can be useful for estimating the postmortem interval (PMI). Currently, the accuracy and precision of PMI estimates is unknown, as error can arise from sources of variation such as measurement error, environmental variation, or genetic variation. Ecological models are an abstract, mathematical representation of an ecological system that can make predictions about the dynamics of the real system. To quantify the variation associated with the pre-appearance interval (PAI), we developed an ecological model that simulates the colonization of vertebrate remains by Cochliomyia macellaria (Fabricius) (Diptera: Calliphoridae), a primary colonizer in the southern United States. The model is based on a development data set derived from a local population and represents the uncertainty in local temperature variability to address PMI estimates at local sites. After a PMI estimate is calculated for each individual, the model calculates the maximum, minimum, and mean PMI, as well as the range and standard deviation for stadia collected. The model framework presented here is one manner by which errors in PMI estimates can be addressed in court when no empirical data are available for the parameter of interest. We show that PAI is a potential important source of error and that an ecological model is one way to evaluate its impact. Such models can be re-parameterized with any development data set, PAI function, temperature regime, assumption of interest, etc., to estimate PMI and quantify uncertainty that arises from specific prediction systems.

Post-mortem interval (PMI) estimation remains one of the major challenges in forensic practice, especially for late PMIs beyond 7-10 days after the death of the subject. In 2022, an innovative method to investigate the occurrence of mutations induced by the death of a subject in the DNA of post-mortem dental pulps at different PMIs was developed, applying a next-generation sequencing (NGS) analysis. The present study aims to apply the same method of analysis to a small sample of teeth belonging to the same subject and analyzed at different PMIs/accumulated degree days (ADDs), and of teeth extracted from different subjects but analyzed at the same PMI/ADD to verify the repeatability of the results obtained in relation to the time elapsed since death. A total of 10 teeth were collected from 6 patients (3 males and 3 females) with PMI varying from 8 to 35 days, and ADD from 157.4 to 753.8. We found 1754 mutations in 56 genes, with more than 700 mutations having a prevalence > 5% and more than 300 variants considered of interest for the purposes of the study. Mutations that were not present at lower PMIs but manifested in later PMIs in pulps belonging to the same subject demonstrate that they can only have been acquired by the subject after death and according to the time elapsed since death. In total, 67 somatic mutations in 29 out of the 56 genes of the used panel occurred in a fashion that allows an association with specific PMI/ADD ranges (within 8 days, between 17 and 28, and beyond 30 days after death). The results suggest that temperature and humidity could influence the rate of DNA degeneration in dental pulps, thus PMI should be estimated in ADD more than days. The preliminary validation supports the hypothesis that the innovative method could be a useful tool for estimating the post-mortem interval even beyond the first week after death, but further analyses are needed to customize a specific genetic panel for forensic investigations and verify the influence of degenerative processes of soft tissues surrounding dental elements on DNA degeneration of pulps.

The postmortem interval is estimated based on physical, biochemical, and morphologic changes in dead bodies, scene investigation findings, and judicial investigation findings. Many factors affect the onset and the course of the postmortem changes. There is no established method for accurate estimation of the postmortem interval, especially with regard to putrefied dead bodies. Aiming to determine the consistency and the variation of postmortem interval estimations in cases with various putrefaction degrees by forensic medicine assistants and specialists from different centers, a prospective study was undertaken. A form containing data about the time, environment, and geographical location where the cases were found, along with their postmortem changes was mailed to 110 forensic medicine assistants and specialists from different centers, who were also asked to note their postmortem interval estimations. About half of the forms were returned. Those postmortem interval estimations made based on the information included in the forms, showed great variability between the participants, and in only 6 cases a consistency exceeding 50% was found between the participants and the autopsy teams. It is not possible to estimate the postmortem interval in a standard way by using only the postmortem changes of putrefied bodies. Therefore, especially in the cases involving putrefied bodies, in addition to the postmortem changes, factors such as environmental conditions, the scene, and judicial investigation findings should be taken into consideration.

Current research involving studies of the effects of drugs on fly’s development has also allowed better estimations of Postmortem Intervals (PMI) or cause of death. Insects’ specimen collected from decomposing or decomposed bodies enable forensic entomologists to estimate the minimum Post-Mortem Interval (PMI) or cause of death. Drugs within a corpse may affect the development rate of insects that feed on them. This study consequently points out the possibilities of analyzing insects, larvae and pupae or empty pupa cases when suitable tissues or other source of information’s are absent. This study investigated the effects of commonly abused drugs on growth rates of insects. These results can be used by the forensic entomologist in forensic entomology for the postmortem interval (PMI) estimation or cause of death of decomposed bodies or tissues in medico criminal investigations.

MicroRNA transcriptome analysis for post-mortem interval estimation.

Background: Accurate estimation of the postmortem interval (PMI) is critical in forensic death investigations. Traditional signs of death—algor mortis, livor mortis, and rigor mortis—are generally reliable only within the first two to three days after death, with their accuracy decreasing as decomposition progresses. This paper presents a systematic review conducted in accordance with PRISMA guidelines, aiming to evaluate and compare current methods for estimating the PMI. Specifically, the study identifies both traditional and modern techniques, analyzes their advantages, limitations, and applicable timeframes, critically synthesizes the literature, and highlights the importance of combining multiple approaches to improve accuracy. Methods: A systematic search was conducted in the PubMed, Scopus, and Web of Science databases, following the PRISMA guidelines. The review included original articles and reviews that evaluated PMI estimation methods (through thanatological signs, entomology, microbial succession, molecular, imaging, and omics approaches). Extracted data included study design, methodology, PMI range, and accuracy information. Out of the 1245 identified records, 50 studies met the inclusion criteria for qualitative synthesis. Results: Emerging methods, such as molecular markers, microbial succession, omics technologies, and advanced imaging show improved accuracy across extended postmortem intervals. RNA degradation methods demonstrated higher accuracy within the first 72 h, while entomology and microbial analysis are more applicable during intermediate and late decomposition stages. Although no single method is universally reliable, combining traditional and modern approaches tailored to case-specific factors improves overall PMI estimation accuracy. Conclusions: This study supports the use of an integrative, multidisciplinary, and evidence-based approach to improve time-since-death estimation. Such a strategy enhances forensic outcomes by enabling more precise PMI estimates in complex or delayed cases, increasing legal reliability, and supporting court-admissible expert testimony based on validated, multi-method protocols.