Xingchun Zhao

Accurately estimating the postmortem interval (PMI) is a pivotal forensic challenge, as the precision of “chemical clocks” based on volatile organic compounds (VOCs) is confounded by intrinsic factors like age and extrinsic factors like insect colonization. This study aimed to disentangle these effects using TD-GC-MS with multivariate analysis to profile VOCs from rats of three ages with and without Sarcophaga peregrina (Robineau-Desvoidy, 1830) (Diptera: Sarcophagidae) colonization. While decomposition time was the principal driver of VOC succession, exhibiting stage-specific signatures like early-phase 2-hexanone and mid-phase aromatics, age and insects modulated this timeline via distinct mechanisms. Age increased the chemical complexity of VOC profiles, with adult rats showing significantly more diverse VOC profiles than younger cohorts during days 10–14 (p < 0.05). In contrast, insect colonization acted as a potent accelerator, advancing the chemical timeline by approximately 6 days, with the day 4 profile of colonized rats mirroring the day 10 profile of insect-free ones. We demonstrate that age increases the chemical heterogeneity of VOC profiles while insects accelerate temporal progression. Both factors must therefore be integrated as critical variables in VOC-based PMI models to ensure accuracy. Graphical abstract

As the largest human organ, the skin frequently interacts with the environment and retains abundant microbial information, making it a crucial source of forensic biological evidence. However, the temporal dynamics of microbial communities between in situ and in vitro samples, as well as the traceability of in vitro samples back to their donors based on corresponding in situ samples, remain unverified through longitudinal sampling and dynamic tracking. A total of 15 young adult volunteers participated in the study, during which skin microbiome samples were collected from their palms and cheeks. A short-term exposure experiment was designed, with a duration of between 0 and 72 hours. Fresh samples were collected at corresponding timepoints, in a synchronised manner. In this study, we utilised a combination of 16S rRNA gene (V3–V4 regions) sequencing and machine learning algorithms to analyse the environmental exposure effects on microbial community structure and their forensic applicability. The results indicated that the relative abundance of dominant genera remained largely stable, regardless of exposure status, with no significant temporal variations observed in the short term. Although individual lifestyles exerted an influence on microbiome composition, they did not affect significant alterations to the overall community architecture. The random forest model attained an accuracy of 91.33% in skin site identification, while the individual differentiation accuracy attained 97.33% when integrating palm and cheek data. These results indicate that the skin microbiome exhibits considerable structural stability under both in situ and in vitro conditions during short-term exposure and maintains high host specificity and site-specific characteristics.

Microbial communities are critical drivers of mammalian carcass decomposition in natural ecosystems. Many studies have attempted to establish a microbial clock to estimate the postmortem interval (PMI); however, several obstacles remain to be solved. This study examines how age and insect activity influence microbial dynamics and emphasizes the role of ‘rupture’ in the decay. Notably, microbial diversity exhibited more pronounced shifts in immature cadavers, while insect activity suppressed overall diversity. Conversely, older age and insect colonization promoted the dominance of the Pseudomonadota phylum. We constructed random forest models (MAE: 0.62–0.95 days, R²: 0.976–0.987) for PMI estimation. These findings provide novel insights into refining PMI estimation in forensic contexts. Future research will further investigate the mechanisms behind these changes. Additionally, it will explore how other factors influence the decay, improving the accuracy and applicability of PMI estimation in various contexts.

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.

The estimation of postmortem interval (PMI) has long been a focal point in the field of forensic science. Following the death of an organism, microorganisms exhibit a clock-like proliferation pattern during the course of cadaver decomposition, forming the foundation for utilizing microbiology in PMI estimation. The establishment of PMI estimation models based on datasets from different seasons is of great practical significance. In this experiment, we conducted microbiota sequencing and analysis on gravesoil and mouse intestinal contents collected during both the winter and summer seasons and constructed a PMI estimation model using the Random Forest algorithm. The results showed that the MAE of the gut microbiota model in summer was 0.47 ± 0.26 d, R2 = 0.991, and the MAE of the gravesoil model in winter was 1.04 ± 0.22 d, R2 = 0.998. We propose that, in practical applications, it is advantageous to selectively build PMI estimation models based on seasonal variations. Additionally, through a combination of morphological observations, gravesoil microbiota sequencing results, and soil physicochemical data, we identified the time of cadaveric rupture for mouse cadavers, occurring at around days 24–27 in winter and days 6–9 in summer. This study not only confirms previous research findings but also introduces novel insights, contributing to the foundational knowledge necessary to advance the utilization of microbiota for PMI estimation.

Microbial communities can undergo significant successional changes during decay and decomposition, potentially providing valuable insights for determining the postmortem interval (PMI). The microbiota produce various gases that cause cadaver bloating, and rupture releases nutrient-rich bodily fluids into the environment, altering the soil microbiota around the carcasses. In this study, we aimed to investigate the underlying principles governing the succession of microbial communities during the decomposition of pig carcasses and the soil beneath the carcasses. At early decay, the phylum Firmicutes and Bacteroidota were the most abundant in both the winter and summer pig rectum. However, Proteobacteria became the most abundant in the winter pig rectum in late decay. Using genus as a biomarker to estimate the PMI could get the MAE from 1.375 days to 2.478 days based on the RF model. The abundance of bacterial communities showed a decreasing trend with prolonged decomposition time. There were statistically significant differences in microbial diversity in the two periods (pre-rupture and post-rupture) of the four groups (WPG 0–8Dvs. WPG 16–40D, p < 0.0001; WPS 0–16Dvs. WPS 24–40D, p = 0.003; SPG 0D vs. SPG 8–40D, p = 0.0005; and SPS 0D vs. SPS 8–40D, p = 0.0208). Most of the biomarkers in the pre-rupture period belong to obligate anaerobes. In contrast, the biomarkers in the post-rupture period belong to aerobic bacteria. Furthermore, the genus Vagococcus shows a similar increase trend, whether in winter or summer. Together, these results suggest that microbial succession was predictable and can be developed into a forensic tool for estimating the PMI.