Recommendations for the Standardization of Forensic Microbiological Analysis

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.

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.

To analyze the literature on artificial intelligence in forensic research from 2012 to 2022 in the Web of Science Core Collection Database, to explore research hotspots and developmental trends. A total of 736 articles on artificial intelligence in forensic medicine in the Web of Science Core Collection Database from 2012 to 2022 were visualized and analyzed through the literature measuring tool CiteSpace. The authors, institution, country (region), title, journal, keywords, cited references and other information of relevant literatures were analyzed. A total of 736 articles published in 220 journals by 355 authors from 289 institutions in 69 countries (regions) were identified, with the number of articles published showing an increasing trend year by year. Among them, the United States had the highest number of publications and China ranked the second. Academy of Forensic Science had the highest number of publications among the institutions. Forensic Science International, Journal of Forensic Sciences, International Journal of Legal Medicine ranked high in publication and citation frequency. Through the analysis of keywords, it was found that the research hotspots of artificial intelligence in the forensic field mainly focused on the use of artificial intelligence technology for sex and age estimation, cause of death analysis, postmortem interval estimation, individual identification and so on. It is necessary to pay attention to international and institutional cooperation and to strengthen the cross-disciplinary research. Exploring the combination of advanced artificial intelligence technologies with forensic research will be a hotspot and direction for future research.

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.

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

The researches on postmortem interval （PMI） estimation are very important and meaningful in forensic science. PMI estimation is also an important issue that must be solved in practice of forensic pathology. There are many defects existing in traditional methods for PMI estimation, so it is imperative to introduce new pathways. With the emergence of various new technologies, the researches on PMI estimation have a tendency from simple to complex with a growth of data. The present review firstly summarizes a series of methods used for PMI estimation, and then gives an outlook for the application of artificial intelligence algorithms in this field.

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.

Estimation of postmortem interval （PMI） is very important for judging the nature of cases, restricting the scope of investigation and suspect, which is always the emphasis and difficulty for forensic pathology. Early postmortem interval is the time between 0 and 24 hours after death. Due to the shorter time after the case occurred, precisely estimating early postmortem interval can help solve crimes, which has important significance in forensic medicine. In recent years, series of advanced methods and technologies are used to estimate the early PMI by the internal and overseas scholars who work in the forensic area. This paper reviews the research progress on fluids biochemistry, supravital reactions, metabolomics, imageology and the degradation rule of genetic material to provide a new idea to the study and application for estimation of early PMI.

Analysis of gastrocnemius compound muscle action potential in rat after death: significance for the estimation of early postmortem interval

IntroductionAccurate postmortem interval (PMI) estimation is a major challenge in forensic medicine, especially for the rapidly and highly putrefied bodies in tropical high-temperature areas. Despite numerous studies, there are still no reliable, objective methods to accurately estimate PMI for corpses found in a high-temperature environment.MethodsIn the current research, an ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF MS)-based metabolomics approach was employed for the analysis of the metabolic profile of rat femoral muscle tissue in a high-temperature environment at different postmortem intervals. Multivariate pattern recognition and pathway analyses were employed for the identification of differential metabolites.ResultsThis study identified 14 significantly changed metabolites and two altered metabolic pathways. Among them, L-threonine, histidyl-threonine, L-tryptophan, N6-acetyl- L-lysine, eicosapentaenoic acid, glycerol 3-phosphate, and creatine were selected as possible diagnostic biomarkers for PMI estimation. Their impact value and potential biological function in the organisation’s degradation process were the basis for selection.DiscussionThis study demonstrated the feasibility of UPLC/Q-TOF MS-based metabolomics in femoral muscle tissue research and identified several differential metabolites that may provide forensic scientists with a helpful reference in PMI estimation in a high-temperature environment.

The postmortem interval (PMI) estimation is a key and difficult point in the practice of forensic medicine, and forensic scientists at home and abroad have been searching for objective, quantifiable and accurate methods of PMI estimation. With the development and combination of high-throughput sequencing technology and artificial intelligence technology, the establishment of PMI model based on the succession of the microbial community on corpses has become a research focus in the field of forensic medicine. This paper reviews the technical methods, research applications and influencing factors of microbial community in PMI estimation explored by using high-throughput sequencing technology, to provide a reference for the related research on the use of microbial community to estimate PMI.

Background: Postmortem interval estimation (PMI) is an important goal in forensic medicine.Aim: to estimate PMI using a scoring method for postmortem changes; hypostasis, rigidity and corneal turbidity in burn cases, potassium (K + ) in vitreous humor (VH) and serum and compare accuracy of thanatochemistry and scoring method for postmortem changes in estimation of PMI.Methods: The study was conducted from May 2017 to May 2018, 50 burn cases where autopsy confirmed the cause of death as severe burns and another 50 control cases.Physical postmortem changes were assessed and numerically scored.The VH (K + ) and serum levels were measured.Data were statistically analyzed and linear regression analysis was used to obtain equations for calculation of PMI.Results: All studied variables in the present study were significantly correlated with PMI; highest correlation coefficient in control and burn groups was for VH (K + ) level, followed by corneal turbidity and serum K + then rigidity in burn groups, but in control groups the highest correlation coefficient was for VH (K + ) and serum levels, followed by rigidity then corneal turbidity and lastly hypostasis.All equations obtained from the present study can predict PMI but with different levels of accuracy. Conclusion:The scoring method for physical postmortem changes was proved to be more valuable in PMI estimation than thanatochemistry.

Postmortem interval （PMI） estimation is a major problem in forensic medicine and a vital indicator of crime investigations. Forensic entomology provides a new way to the PMI estimation, and its superiority has been confirmed through many cases, especially in relation to the cadaver at the stage of advanced decomposition even skeleton. The technologies of forensic entomology have become more mature. This review describes the mechanism of forensic entomology used for PMI estimation, emphasizes the standardized application of technology, and lists the data for the reference of forensic experts in China.

The Arab Society for Forensic Sciences and Forensic Medicine (ASFSFM) at Naif Arab University for Security Sciences seeks to present the latest developments in all fields of forensic sciences through holding specialized scientific events and academic activities. This is also achieved through its periodic scientific peer-reviewed journal, the Arab Journal of Forensic Sciences and Forensic Medicine. It also seeks to promote scientific research in all fields of forensic science and forensic medicine, and seeks actively to contribute in holding scientific meetings in accordance with advanced scientific standards, including the 3rd International Arab Forensic Sciences & Forensic Medicine Conference. This important event was attended by scientists and experts from various fields of criminal and forensic sciences from both Arab and non-Arab countries. This conference was a significant scientific accomplishment that contributed to the advancement of forensic sciences and forensic medicine in the Arab world. The conference aimed, in accordance with the vision of Naif Arab University for Security Sciences, to enhance peace, security and justice in Arab societies. Naif Arab University for Security Sciences, represented by the Arab Society for Forensic Sciences and Forensic Medicine, held the 3rd International Arab Forensic Sciences & Forensic Medicine Conference on the University's campus during the period from 21st to 23rd November 2017. The event included the participation of more than 720 experts in forensic sciences and forensic medicine from 33 countries all over the world. Experts discussed and presented the latest developments in their fields. The conference provided a creative environment for students from both local and international universities to benefit from experts and specialists, and to access the most recent research. On behalf of His Excellency the president of Naif Arab University for Security Sciences, and the Arab Society for Forensic Sciences and Forensic Medicine, we would like to pay tribute to all scientists and experts who contributed by sharing their scientific researches through lectures, workshops, and posters within this event. We appreciate their contributions that added great value to the subject of the conference.

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.