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Estimates of post-mortem interval (PMI), which often serve as pivotal evidence in forensic contexts, are fundamentally based on assessments of variability among diverse molecular markers (including proteins and metabolites), their correlations, and their temporal changes in post-mortem organisms. Nevertheless, the present approach to estimating the PMI is not comprehensive and exhibits poor performance. We developed an innovative approach that integrates multi-omics and artificial intelligence, using multimolecular, multimarker, and multidimensional information to accurately describe the intricate biological processes that occur after death, ultimately enabling inference of the PMI. Called the multi-omics stacking model (MOSM), it combines metabolomics, protein microarray electrophoresis, and fourier transform-infrared spectroscopy data. It shows improved prediction accuracy of the PMI, which is urgently needed in the forensic field. It achieved an accuracy of 0.93, generalized area under the receiver operating characteristic curve of 0.98, and minimum mean absolute error of 0.07. The MOSM integration framework not only considers multiple markers but also incorporates machine-learning models with distinct algorithmic principles. The diversity of biological mechanisms and algorithmic models further ensures the generalizability and robustness of PMI estimation., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest., (Copyright © 2023 Elsevier B.V. All rights reserved.)

The microbial communities may undergo a meaningful successional change during the progress of decay and decomposition that could aid in determining the post-mortem interval (PMI). However, there are still challenges to applying microbiome-based evidence in law enforcement practice. In this study, we attempted to investigate the principles governing microbial community succession during decomposition of rat and human corpse, and explore their potential use for PMI of human cadavers. A controlled experiment was conducted to characterize temporal changes in microbial communities associated with rat corpses as they decomposed for 30 days. Obvious differences of microbial community structures were observed among different stages of decomposition, especially between decomposition of 0-7d and 9-30d. Thus, a two-layer model for PMI prediction was developed based on the succession of bacteria by combining classification and regression models using machine learning algorithms. Our results achieved 90.48% accuracy for discriminating groups of PMI 0-7d and 9-30d, and yielded a mean absolute error of 0.580d within 7d decomposition and 3.165d within 9-30d decomposition. Furthermore, samples from human cadavers were collected to gain the common succession of microbial community between rats and humans. Based on the 44 shared genera of rats and humans, a two-layer model of PMI was rebuilt to be applied for PMI prediction of human cadavers. Accurate estimates indicated a reproducible succession of gut microbes across rats and humans. Together these results suggest that microbial succession was predictable and can be developed into a forensic tool for estimating PMI., Competing Interests: Declaration of Competing Interest The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

The inter-group heterogeneity and intra-group homogeneity of relative expression are very necessary when the mRNA were used to determine wound age accurately in forensic medicine. The aim of this study was to assess the intra-group homogeneity of SFRP5, FZD4 and Fosl1 mRNAs in post-injury intervals. The corresponding proteins show different subcellular locations. A total of 78 Sprague-Dawley rats were divided into control and contusion groups. At 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, or 48 h (n = 6 per group) after contusion (under anesthesia by chloral hydrate intraperitoneally), the rats were sacrificed using a lethal dose of pentobarbital, and samples of the injured muscles were collected. The raw Ct values of SFRP5, FZD4, and Fosl1 mRNAs were obtained using real-time PCR. After normalized to RPL13 mRNA levels, the coefficient of variation (CV) and the relative average deviation (d%) of each normalized Ct, and their relative expression levels, were calculated in each post-injury interval. Two methods were applied to compare the homogeneity of the three genes. First, each gene was given a score based on its CV value in each post-injury interval. Then, the sum of the 13 scores was calculated; a low sum indicated high homogeneity. Second, the 13 calculated CVs or d%s were used as raw data, which was described as the mean ± SD. Based on this mean ± SD, a CV of the CVs and a d% of the d%s were calculated to represent the variation; a low value indicated high homogeneity. The sum of the variability of FZD4 mRNA was lower than those of the SFRP5 and Fosl1 mRNAs, consistent with the results that the FZD4 mRNA had the lowest mean, the smallest CV of all CVs, and the smallest d% of all d%s, among the three genes. In conclusion, these data indicated that mRNA encoding membranous FZD4 was likely to be more homogeneous than those encoding SFRP5 and Fosl1 within post-injury intervals., (Copyright © 2016 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.)