Changes in Microbial Communities Using Pigs as a Model for Postmortem Interval Estimation

From microbial data to forensic insights: systematic review of machine learning models for PMI estimation

BACKGROUND

Estimating post-mortem interval (PMI) is crucial for forensic timelines, yet traditional methods are prone to errors from witness testimony and biological markers sensitive to environmental factors. New molecular and microbial techniques, such as DNA degradation patterns and bacterial community analysis, have shown promise by improving PMI estimation accuracy and reliability over traditional methods. Machine learning further enhances PMI estimation by leveraging complex microbial data. This review addresses the gap by systematically analyzing how microbiome-based PMI predictions compare across organs, environments, and machine learning techniques.

METHODS

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, machine learning algorithms, and model performance.

RESULTS

We gathered 1252 records from five databases after excluding 750 duplicates. After screening titles and abstracts, 43 records were assessed for eligibility, resulting in 28 included articles. Our ranking of machine learning models for PMI estimation identified the top five based on error metrics and explained variance. Wang (2024) achieved a mean absolute error (MAE) of 6.93 h with a random forests (RF) model. Liu (2020) followed with an MAE of 14.483 h using a neural network. Cui (2022) used soil samples for PMI predictions up to 36 days, reaching an MAE of 1.27 days. Yang (2023) employed an RF model using soil samples, achieving an MAE of 1.567 days in summer and an MAE of 2.001 days in winter. Belk (2018) an RF model on spring soil samples with 16S rRNA data, attaining an MAE of 48 accumulated day degrees (ADD) (~ 3-5 days) across a PMI range of 142 days.

CONCLUSION

Machine learning models, particularly RF, have demonstrated effectiveness in PMI estimation when combined with 16S rRNA and soil samples. However, improving model performance requires standardized parameters and validation across diverse forensic environments.

Unveiling the Forensic Potential of Oral and Nasal Microbiota in Post-Mortem Interval Estimation

Microbiota have emerged as a promising tool for estimating the post-mortem interval (PMI) in forensic investigations. The role of oral and nasal microbiota in cadaver decomposition is crucial; however, their distribution across human cadavers at different PMIs remains underexplored. In this study, we collected 88 swab samples from the oral and nasal cavities of 10 healthy volunteers and 34 human cadavers. Using 16S rRNA gene sequencing, we conducted comprehensive analyses of the alpha diversity, beta diversity, and relative abundance distribution to characterize the microbial communities in both healthy individuals and cadavers at varying PMIs and under different freezing conditions. Random forest models identified Firmicutes, Proteobacteria, Bacteroidota, Actinobacteriota, and Fusobacteriota as potential PMI-associated biomarkers at the phylum level for both the oral and nasal groups, along with genus-level biomarkers specific to each group. These biomarkers exhibited nonlinear changes over increasing PMI, with turning points observed on days 5, 12, and 22. The random forest inference models demonstrated that oral biomarkers at both the genus and phylum levels achieved the lowest mean absolute error (MAE) values in the training dataset (MAE = 2.16 days) and the testing dataset (MAE = 5.14 days). Additionally, freezing had minimal impact on the overall phylum-level microbial composition, although it did affect the relative abundance of certain phyla. At the genus level, significant differences in microbial biomarkers were observed between frozen and unfrozen cadavers, with the oral group showing greater stability compared to the nasal group. These findings suggest that the influence of freezing should be considered when using genus-level microbial data to estimate PMIs. Overall, our results highlight the potential of oral and nasal microbiota as robust tools for PMI estimation and emphasize the need for further research to refine predictive models and explore the environmental factors shaping microbial dynamics.

Gut Microbiota of Ruminants and Monogastric Livestock: An Overview

The diversity and composition of the gut microbiota are widely recognized as fundamental factors influencing the well-being and productivity of domestic animals. Advancements in sequencing technologies have revolutionized studies in this research field, allowing for deeper insights into the composition and functionality of microbiota in livestock. Ruminants and monogastric animals exhibit distinct digestive systems and microbiota characteristics: ruminants rely on fermentation, while monogastrics use enzymatic digestion, and monogastric animals have simpler stomach structures, except for horses and rabbits, where both processes coexist. Understanding the gut microbiota's impact and composition in both animal types is essential for optimizing production efficiency and promoting animal health. Following this perspective, the present manuscript review aims to provide a comprehensive overview of the gut microbiota in ruminants (such as cattle, sheep, and goats) and monogastric animals (including horses, pigs, rabbits, and chickens).

The micro CT evaluation of crown and root pulp volume versus dentin thickness in teeth in postmortem interval (PMI)

Determining the postmortem interval (PMI) is one of the main study subjects of forensic sciences. The main purpose of this prospective in vitro study that was the Micro-CT evaluation of teeth crown and root pulp volume versus dentin thickness in terms of PMI determination. The study involved 60 female Wistar rats, with weights ranging from 270 to 320 g. These rats were grouped into six different post-mortem period categories. Following the animals' sacrifice, they were subjected to a natural putrefaction period, with a control group, in the grounds of a sheltered garden. Hemi-mandible samples were then extracted and placed in glass tubes for Micro-CT evaluations, following the progression of putrefaction processes. The pulp volume and dentin thickness were assessed using Micro-CT, and the gathered data underwent statistical analysis. Micro-CT was employed to analyze sixty right mandibular second molar teeth in the hemi-mandible. The crown pulp volume exhibited a reduction in group 6, with a value of 0.239 mm3 after a three-month period of natural putrefaction (p < 0.001). There is statistically differences among groups in case of pairwise comparison (p < 0.05). However, the root pulp volume and dentin thickness variables did not display any statistically significant changes. Despite certain limitations associated with this study, the Micro-CT findings concerning teeth pulp volume can serve as an objective parameter, especially for late postmortem investigations and the estimation of time of death.

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.