A novel approach for the forensic diagnosis of drowning by microbiological analysis with next-generation sequencing and unweighted UniFrac-based PCoA

A preliminary study on estimation of postmortem submersion interval of rat cadavers in freshwater through polyamine analysis in tissues

The estimation of postmortem submersion interval (PMSI) has always been an important scientific issue to be addressed in drowning cases. Traditional methods, such as corpse temperature analysis and the assessment of corpse surface corruption, have limitations and cannot meet the need for accurate estimation of the time of death in the mid to late stages. Biogenic amines, as small molecules produced by protein degradation after death, have a certain regularity in relation to PMSI. To further explore the possibility of utilizing polyamines to estimate PMSI, this experiment constructed a rat cadaver model in both laboratory constant-temperature water and natural water bodies. Furthermore, cadaverine and putrescine in the liver and skeletal muscle were detected at different PMSI using gas chromatography-mass spectrometry (GC-MS). Through statistical analysis, we have constructed eight sets of mathematical models for polyamines-PMSI estimation, and determined the applicable time range through derivative analysis. After evaluation the models, the error rate in inferring PMSI using the fitted equations was less than 30 % within 242 h. The models established in this study could accurately infer PMSI in the mid to late stages of the postmortem period, providing a feasible approach for the drowning forensic issue.

Antiepileptic Effects of Acorus tatarinowii Schott in a Rat Model of Epilepsy: Regulation of Metabolic Axes and Gut Microbiota

As a phytotherapeutic agent with historical applications in epilepsy management, Acorus tatarinowii Schott (ATS) remains pharmacologically enigmatic, particularly regarding its pathophysiological mechanisms. This knowledge gap significantly hinders the clinical application of ATS-based treatments. To explore the potential of ATS in combating epileptogenesis, we utilized a pentylenetetrazole (PTZ)-induced chronic epilepsy rat model. Brain metabolomic analysis was performed by ultra-performance liquid chromatography coupled with mass spectrometry (UPLC/MS). Principal component analysis (PCA) and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) were performed for screening differential metabolites. Gut microbiota composition was analyzed through 16S rRNA gene sequencing and examined using Spearman correlation analysis. The results show that oral ATS (50 mg/kg) significantly improved the seizure latency and pathology of rats with epilepsy. Ascorbate and aldarate metabolism, glycerophospholipid metabolism, arachidonic acid metabolism, and intestinal flora were crucial for ATS's ability to counteract epilepsy. The therapeutic effects of ATS against epilepsy were investigated with brain metabolomics and gut microbiota analysis, providing the basis for further comprehensive research.

Microbial community profiling for forensic drowning diagnosis across locations and submersion times

BACKGROUND

Drowning diagnosis has long been a critical issue in forensic research, influenced by various factors such as the environment and decomposition time. While traditional methods such as diatom analysis have limitations in decomposed remains, microbial community profiling offers a promising alternative. With the advancement of high-throughput sequencing technology, forensic microbiology has become a prominent focus in the field, providing new research avenues for drowning diagnosis. During drowning, microbial communities enter the lung tissue along with the water.

METHODS

In this study, using a murine model, we collected samples from three rivers at random sites at postmortem intervals (PMI) of 1, 4, and 7 days‌ to comprehensively evaluate the differences in microbial communities between mice subjected to drowning versus postmortem immersion.

RESULTS

The α-diversity analysis revealed that the observed Operational Taxonomic Units (OTUs) for the drowning group on day 1 was 234.77 ± 16.60, significantly higher than the postmortem immersion group (171.32 ± 9.22), indicating greater initial microbial richness in the drowning group. Additionally, Shannon index analysis showed a significant decline in evenness in the postmortem immersion group on day 7 (1.46 ± 0.09), whereas the drowning group remained relatively stable (2.38 ± 0.15), further indicating a rapid decrease in microbial diversity in the postmortem immersion group over time. PCoA analysis demonstrated that differences in microbial community composition between drowning and postmortem immersion groups were notably stable. Key microbial taxa differentiating the groups were identified through LEfSe analysis, with Enterococcaceae (family), Escherichia-Shigella (genus), and Proteus (genus), emerging as significant markers in drowning cases. A random forest model, trained using microbial community data, exhibited high predictive accuracy (AUC = 0.96) across locations and immersion times and identified microbial markers, including Enterococcaceae (family), Lactobacillales (order), Morganellaceae (family), as critical features influencing model performance.

CONCLUSION

These findings underscore the potential of combining 16 S rRNA sequencing with machine learning as a powerful tool for drowning diagnosis, offering novel insights into forensic microbiology.

Integrating microbial profiling and machine learning for inference of drowning sites: a forensic investigation in the Northwest River

Drowning incidents present significant challenges for forensic investigators in determining the exact site of occurrence. Traditional forensic methods often rely on physical evidence and circumstantial clues, but the emerging field of forensic microbiology offers a promising avenue for enhancing precision and reliability in site inference. Our study investigates the application of microbiome analysis in inferring drowning sites, focusing on microbial diversity in water samples and lung tissues of drowned animals from different sites in the Northwest River. We utilized 16S rDNA sequencing to analyze microbial diversity in water samples and lung tissues, revealing distinct microbial signatures associated with drowning sites. Our findings highlight variations in species richness and diversity across different sampling points, indicating the influence of environmental factors on microbial community structure. Machine learning models trained on microbial data from lung tissues demonstrated high accuracy in predicting drowning sites, with cross-validation accuracy ranging from 83.53% ± 3.99% to 95.07% ± 3.17%. Notably, the Gradient Boosting Machine (GBM) method achieved a classification accuracy of 95.07% ± 3.17% for different sampling points at a submersion time of 1 day. Moreover, our cross-species site inference results revealed that utilizing data from drowned mice to predict the drowning sites of rabbits in location W5 achieved an accuracy of 72.22%. In conclusion, our study underscores the potential of microbiome analysis in forensic investigations of drowning incidents. By integrating microbial data with traditional forensic techniques, there is significant potential to enhance the reliability of scene inferences, thereby making substantial contributions to case investigations and judicial trials.IMPORTANCEBy employing advanced techniques like microbial profiling and machine learning, the study aims to enhance the accuracy of determining drowning sites, which is crucial for both legal proceedings. By analyzing microbial diversity in water samples and drowned animal lung tissues, the study sheds light on how environmental factors and victim-related variables influence microbial communities. The findings not only advance our understanding of forensic microbiology but also offer practical implications for improving investigative techniques in cases of drowning.

A fundamental study on postmortem submersion interval estimation by metabolomics analyzing of gastrocnemius muscle from submersed rat models in freshwater

In forensic practice, determining the postmortem submersion interval (PMSI) and cause-of-death of cadavers in aquatic ecosystems has always been challenging task. Traditional approaches are not yet able to address these issues effectively and adequately. Our previous study proposed novel models to predict the PMSI and cause-of-death based on metabolites of blood from rats immersed in freshwater. However, with the advance of putrefaction, it is hardly to obtain blood samples beyond 3 days postmortem. To further assess the feasibility of PMSI estimation and drowning diagnosis in the later postmortem phase, gastrocnemius, the more degradation-resistant tissue, was collected from drowned rats and postmortem submersion model in freshwater immediately after death, and at 1 day, 3 days, 5 days, 7 days, and 10 days postmortem respectively. Then the samples were analyzed with liquid chromatography-tandem mass spectrometry (LC-MS/MS) to investigate the dynamic changes of the metabolites. A total of 924 metabolites were identified. Similar chronological changes of gastrocnemius metabolites were observed in the drowning and postmortem submersion groups. The difference in metabolic profiles between drowning and postmortem submersion groups was only evident in the initial 1 day postmortem, which was faded as the PMSI extension. Nineteen metabolites representing temporally-dynamic patterns were selected as biomarkers for PMSI estimation. A regression model was built based on these biomarkers with random forest algorithm, which yielded a mean absolute error (± SE) of 5.856 (± 1.296) h on validation samples from an independent experiment. These findings added to our knowledge of chronological changes in muscle metabolites from submerged vertebrate remains during decomposition, which provided a new perspective for PMSI estimation.

Different Prostatic Tissue Microbiomes between High- and Low-Grade Prostate Cancer Pathogenesis

Numerous human pathologies, such as neoplasia, are related to particular bacteria and changes in microbiome constituents. To investigate the association between an imbalance of bacteria and prostate carcinoma, the microbiome and gene functionality from tissues of patients with high-grade prostate tumor (HGT) and low-grade prostate tumor (LGT) were compared utilizing next-generation sequencing (NGS) technology. The results showed abnormalities in the bacterial profiles between the HGT and LGT specimens, indicating alterations in the make-up of bacterial populations and gene functionalities. The HGT specimens showed higher frequencies of Cutibacterium, Pelomonas, and Corynebacterium genera than the LGT specimens. Cell proliferation and cytokine assays also showed a significant proliferation of prostate cancer cells and elevated cytokine levels in the cells treated with Cutibacterium, respectively, supporting earlier findings. In summary, the HGT and LGT specimens showed differences in bacterial populations, suggesting that different bacterial populations might characterize high-grade and low-grade prostate malignancies.

Metatranscriptomics: A Tool for Clinical Metagenomics

In the field of bioinformatics, amplicon sequencing of 16S rRNA genes has long been used to investigate community membership and taxonomic abundance in microbiome studies. As we can observe, shotgun metagenomics has become the dominant method in this field. This is largely owing to advancements in sequencing technology, which now allow for random sequencing of the entire genetic content of a microbiome. Furthermore, this method allows profiling both genes and the microbiome's membership. Although these methods have provided extensive insights into various microbiomes, they solely assess the existence of organisms or genes, without determining their active role within the microbiome. Microbiome scholarship now includes metatranscriptomics to decipher how a community of microorganisms responds to changing environmental conditions over a period of time. Metagenomic studies identify the microbes that make up a community but metatranscriptomics explores the diversity of active genes within that community, understanding their expression profile and observing how these genes respond to changes in environmental conditions. This expert review article offers a critical examination of the computational metatranscriptomics tools for studying the transcriptomes of microbial communities. First, we unpack the reasons behind the need for community transcriptomics. Second, we explore the prospects and challenges of metatranscriptomic workflows, starting with isolation and sequencing of the RNA community, then moving on to bioinformatics approaches for quantifying RNA features, and statistical techniques for detecting differential expression in a community. Finally, we discuss strengths and shortcomings in relation to other microbiome analysis approaches, pipelines, use cases and limitations, and contextualize metatranscriptomics as a tool for clinical metagenomics.

Forensic Microbiology: When, Where and How

Forensic microbiology is a relatively new discipline, born in part thanks to the development of advanced methodologies for the detection, identification and characterization of microorganisms, and also in relation to the growing impact of infectious diseases of iatrogenic origin. Indeed, the increased application of medical practices, such as transplants, which require immunosuppressive treatments, and the growing demand for prosthetic installations, associated with an increasing threat of antimicrobial resistance, have led to a rise in the number of infections of iatrogenic origin, which entails important medico-legal issues. On the other hand, the possibility of detecting minimal amounts of microorganisms, even in the form of residual traces (e.g., their nucleic acids), and of obtaining gene and genomic sequences at contained costs, has made it possible to ask new questions of whether cases of death or illness might have a microbiological origin, with the possibility of also tracing the origin of the microorganisms involved and reconstructing the chain of contagion. In addition to the more obvious applications, such as those mentioned above related to the origin of iatrogenic infections, or to possible cases of infections not properly diagnosed and treated, a less obvious application of forensic microbiology concerns its use in cases of violence or violent death, where the characterization of the microorganisms can contribute to the reconstruction of the case. Finally, paleomicrobiology, e.g., the reconstruction and characterization of microorganisms in historical or even archaeological remnants, can be considered as a sister discipline of forensic microbiology. In this article, we will review these different aspects and applications of forensic microbiology.

A local dark tea - Liubao tea - extract exhibits remarkable performance in oral tissue regeneration, inflammation relief and oral microbiota reconstruction

The prevalence of oral health problems is ubiquitous in contemporary society, with particular emphasis placed on the central role of oral flora in mitigating this issue. Both ancient literature and modern research have highlighted the promising application of tea with substantial bioactive properties, particularly dark tea, in preserving and promoting oral health. Liubao tea, a widely consumed dark tea with increasing popularity in recent years, has been reported to possess abundant bioactive constituents, exhibit remarkable antioxidant and anti-inflammatory effects, modulate the flora structure and so on. It may be a promising candidate for addressing oral health problems. In this study, Liubao tea was meticulously extracted, purified and identified, followed by an investigation of its potential to modulate oral microecology by virtue of an acetic acid-induced oral disorder murine model. The results revealed that Liubao tea extract (LTE) application commendably reconstructed the oral mucosal barrier, promoted tissue regeneration and mitigated micro-inflammation. Furthermore, LTE treatment could also ameliorate the oral flora composition by decreasing the abundance of Proteobacteria and increasing the abundance of Firmicutes and Actinobacteria at the phylum level, as well as inhibiting pernicious bacteria such as Streptococcus and Delftia acidovorans. So, it could promote the generation of a beneficial microenvironment and regulate the immune process. Overall, LTE demonstrated remarkable potential in regulating the balance of oral microecology, suggesting that it may represent a promising therapeutic strategy for oral health concerns.

Finding phylogeny-aware and biologically meaningful averages of metagenomic samples: L2UniFrac

MOTIVATION

Metagenomic samples have high spatiotemporal variability. Hence, it is useful to summarize and characterize the microbial makeup of a given environment in a way that is biologically reasonable and interpretable. The UniFrac metric has been a robust and widely used metric for measuring the variability between metagenomic samples. We propose that the characterization of metagenomic environments can be improved by finding the average, a.k.a. the barycenter, among the samples with respect to the UniFrac distance. However, it is possible that such a UniFrac-average includes negative entries, making it no longer a valid representation of a metagenomic community.

RESULTS

To overcome this intrinsic issue, we propose a special version of the UniFrac metric, termed L2UniFrac, which inherits the phylogenetic nature of the traditional UniFrac and with respect to which one can easily compute the average, producing biologically meaningful environment-specific "representative samples." We demonstrate the usefulness of such representative samples as well as the extended usage of L2UniFrac in efficient clustering of metagenomic samples, and provide mathematical characterizations and proofs to the desired properties of L2UniFrac.

AVAILABILITY AND IMPLEMENTATION

A prototype implementation is provided at https://github.com/KoslickiLab/L2-UniFrac.git. All figures, data, and analysis can be reproduced at https://github.com/KoslickiLab/L2-UniFrac-Paper.

Inference of drowning sites using bacterial composition and random forest algorithm

Diagnosing the drowning site is a major challenge in forensic practice, particularly when corpses are recovered from flowing rivers. Recently, forensic experts have focused on aquatic microorganisms, including bacteria, which can enter the bloodstream during drowning and may proliferate in corpses. The emergence of 16S ribosomal RNA gene (16S rDNA) amplicon sequencing has provided a new method for analyzing bacterial composition and has facilitated the development of forensic microbiology. We propose that 16S rDNA amplicon sequencing could be a useful tool for inferring drowning sites. Our study found significant differences in bacterial composition in different regions of the Guangzhou section of the Pearl River, which led to differences in bacteria of drowned rabbit lungs at different drowning sites. Using the genus level of bacteria in the lung tissue of drowned rabbits, we constructed a random forest model that accurately predicted the drowning site in a test set with 100% accuracy. Furthermore, we discovered that bacterial species endemic to the water were not always present in the corresponding drowned lung tissue. Our findings demonstrate the potential of a random forest model based on bacterial genus and composition in drowned lung tissues for inferring drowning sites.

A preliminary study of the salivary microbiota of young male subjects before, during, and after acute high-altitude exposure

Background

The microbial community structure in saliva differs at different altitudes. However, the impact of acute high-altitude exposure on the oral microbiota is unclear. This study explored the impact of acute high-altitude exposure on the salivary microbiome to establish a foundation for the future prevention of oral diseases. Methods. Unstimulated whole saliva samples were collected from 12 male subjects at the following three time points: one day before entering high altitude (an altitude of 350 m, pre-altitude group), seven days after arrival at high altitude (an altitude of 4,500 m, altitude group) and seven days after returning to low altitude (an altitude of 350 m, post-altitude group). Thus, a total of 36 saliva samples were obtained. 16S rRNA V3-V4 region amplicon sequencing was used to analyze the diversity and structure of the salivary microbial communities, and a network analysis was employed to investigate the relationships among salivary microorganisms. The function of these microorganisms was predicted with a Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) analysis.

Results

In total, there were 756 operational taxonomic units (OTUs) identified, with 541, 613, and 615 OTUs identified in the pre-altitude, altitude, and post-altitude groups, respectively. Acute high-altitude exposure decreased the diversity of the salivary microbiome. Prior to acute high-altitude exposure, the microbiome mainly consisted of Proteobacteria, Firmicutes, Bacteroidetes, Fusobacteria, and Actinobacteria. After altitude exposure, the relative abundance of Streptococcus and Veillonella increased, and the relative abundance of Prevotella, Porphyromonas, and Alloprevotella decreased. The relationship among the salivary microorganisms was also affected by acute high-altitude exposure. The relative abundance of carbohydrate metabolism gene functions was upregulated, while the relative abundance of coenzyme and vitamin metabolism gene functions was downregulated.

Conclusion

Rapid high-altitude exposure decreased the biodiversity of the salivary microbiome, changing the community structure, symbiotic relationships among species, and abundance of functional genes. This suggests that the stress of acute high-altitude exposure influenced the stability of the salivary microbiome.

Biochemical Recurrence in Prostate Cancer Is Associated with the Composition of Lactobacillus: Microbiome Analysis of Prostatic Tissue

Many human pathologies, such as malignancy, are linked with specific bacteria and changes in the constituents of the microbiome. In order to examine the association between an imbalance of bacteria and prostate carcinoma, a comparison of the microbiomes present in patients with biochemical recurrence (BCR) or NO BCR (NBCR) was performed. Additionally, 16S rRNA-based next-generation sequencing was applied to identify the bacterial profiles within these tumors in terms of the bacteria and operational genes present. The percentage average taxonomic composition between the taxa indicated no difference between BCR and NBCR. In addition, alpha and beta diversity indices presented no distinction between the cohorts in any statistical method. However, taxonomic biomarker discovery indicated a relatively higher population of Lactobacillus in the NBCR group, and this finding was supported by PCR data. Along with that, differences in the operational activity of the bacterial genes were also determined. It is proposed that the biochemical recurrence was linked to the quantity of Lactobacillus present. The aim of this study was to investigate the microbiome involved in prostate carcinoma and the potential association between them.

Exploring the characteristics of gut microbiome in patients of Southern Fujian with hypocitraturia urolithiasis and constructing clinical diagnostic models

PURPOSE

Hypocitraturia is an important cause of urolithiasis. Exploring the characteristics of the gut microbiome (GMB) of hypocitriuria urolithiasis (HCU) patients can provide new ideas for the treatment and prevention of urolithiasis.

METHODS

The 24 h urinary citric acid excretion of 19 urolithiasis patients was measured, and patients were divided into the HCU group and the normal citrate urolithiasis (NCU) group. The 16 s ribosomal RNA (rRNA) was used to detect GMB composition differences and construct operational taxonomic units (OTUs) coexistence networks. The key bacterial community was determined by Lefse analysis, Metastats analysis and RandomForest analysis. Redundancy analysis (RDA) and Pearson correlation analysis visualized the correlation between key OTUs and clinical features and then established the disease diagnosis model of microbial-clinical indicators. Finally, PICRUSt2 was used to explore the metabolic pathway of related GMB in HCU patients.

RESULTS

The alpha diversity of GMB in HCU group was increased and Beta diversity analysis suggested significant differences between HCU and NCU groups, which was related to renal function damage and urinary tract infection. Ruminococcaceae_ge and Turicibacter are the characteristic bacterial groups of HCU. Correlation analysis showed that the characteristic bacterial groups were significantly associated with various clinical features. Based on this, the diagnostic models of microbiome-clinical indicators in HCU patients were constructed with the areas under the curve (AUC) of 0.923 and 0.897, respectively. Genetic and metabolic processes of HCU are affected by changes in GMB abundance.

CONCLUSION

GMB disorder may be involved in the occurrence and clinical characteristics of HCU by influencing genetic and metabolic pathways. The new microbiome-clinical indicator diagnostic model is effective.

Trends in forensic microbiology: From classical methods to deep learning

Forensic microbiology has been widely used in the diagnosis of causes and manner of death, identification of individuals, detection of crime locations, and estimation of postmortem interval. However, the traditional method, microbial culture, has low efficiency, high consumption, and a low degree of quantitative analysis. With the development of high-throughput sequencing technology, advanced bioinformatics, and fast-evolving artificial intelligence, numerous machine learning models, such as RF, SVM, ANN, DNN, regression, PLS, ANOSIM, and ANOVA, have been established with the advancement of the microbiome and metagenomic studies. Recently, deep learning models, including the convolutional neural network (CNN) model and CNN-derived models, improve the accuracy of forensic prognosis using object detection techniques in microorganism image analysis. This review summarizes the application and development of forensic microbiology, as well as the research progress of machine learning (ML) and deep learning (DL) based on microbial genome sequencing and microbial images, and provided a future outlook on forensic microbiology.

Finding phylogeny-aware and biologically meaningful averages of metagenomic samples: L 2 UniFrac

Metagenomic samples have high spatiotemporal variability. Hence, it is useful to summarize and characterize the microbial makeup of a given environment in a way that is biologically reasonable and interpretable. The UniFrac metric has been a robust and widely-used metric for measuring the variability between metagenomic samples. We propose that the characterization of metagenomic environments can be achieved by finding the average, a.k.a. the barycenter, among the samples with respect to the UniFrac distance. However, it is possible that such a UniFrac-average includes negative entries, making it no longer a valid representation of a metagenomic community. To overcome this intrinsic issue, we propose a special version of the UniFrac metric, termed L ₂ UniFrac, which inherits the phylogenetic nature of the traditional UniFrac and with respect to which one can easily compute the average, producing biologically meaningful environment-specific "representative samples". We demonstrate the usefulness of such representative samples as well as the extended usage of L ₂ UniFrac in efficient clustering of metagenomic samples, and provide mathematical characterizations and proofs to the desired properties of L ₂ UniFrac. A prototype implementation is provided at: https://github.com/KoslickiLab/L2-UniFrac.git .

Postmortem submersion interval estimation of cadavers recovered from freshwater based on gut microbial community succession

Microbial community succession during decomposition has been proven to be a useful tool for postmortem interval (PMI) estimation. Numerous studies have shown that the intestinal microbial community presented chronological changes after death and was stable in terrestrial corpses with different causes of death. However, the postmortem pattern of intestinal microbial community succession in cadavers retrieved from water remains unclear. For immersed corpses, the postmortem submersion interval (PMSI) is a useful indicator of PMI. To provide reliable estimates of PMSI in forensic investigations, we investigated the gut microbial community succession of corpses submersed in freshwater and explored its potential application in forensic investigation. In this study, the intestinal microbial community of mouse submersed in freshwater that died of drowning or CO2 asphyxia (i.e., postmortem submersion) were characterized by 16S rDNA amplification and high-throughput sequencing, followed by bioinformatic analyses. The results demonstrated that the chronological changes in intestinal bacterial communities were not different between the drowning and postmortem submersion groups. α-diversity decreased significantly within 14 days of decomposition in both groups, and the β-diversity bacterial community structure ordinated chronologically, inferring the functional pathway and phenotype. To estimate PMSI, a regression model was established by random forest (RF) algorithm based on the succession of postmortem microbiota. Furthermore, 15 genera, including Proteus, Enterococcus, and others, were selected as candidate biomarkers to set up a concise predicted model, which provided a prediction of PMSI [MAE (± SE) = 0.818 (± 0.165) d]. Overall, our present study provides evidence that intestinal microbial community succession would be a valuable marker to estimate the PMSI of corpses submerged in an aquatic habitat.

Microbial communities in the liver and brain are informative for postmortem submersion interval estimation in the late phase of decomposition: A study in mouse cadavers recovered from freshwater

INTRODUCTION

Bodies recovered from water, especially in the late phase of decomposition, pose difficulties to the investigating authorities. Various methods have been proposed for postmortem submersion interval (PMSI) estimation and drowning identification, but some limitations remain. Many recent studies have proved the value of microbiota succession in viscera for postmortem interval estimation. Nevertheless, the visceral microbiota succession and its application for PMSI estimation and drowning identification require further investigation.

METHODS

In the current study, mouse drowning and CO2 asphyxia models were developed, and cadavers were immersed in freshwater for 0 to 14 days. Microbial communities in the liver and brain were characterized via 16S rDNA high-throughput sequencing.

RESULTS

Only livers and brains collected from 5 to 14 days postmortem were qualified for sequencing. There was significant variation between microbiota from liver and brain. Differences in microbiota between the cadavers of mice that had drowned and those only subjected to postmortem submersion decreased over the PMSI. Significant successions in microbial communities were observed among the different subgroups within the late phase of the PMSI in livers and brains. Eighteen taxa in the liver which were mainly related to Clostridium_sensu_stricto and Aeromonas, and 26 taxa in the brain which were mainly belonged to Clostridium_sensu_stricto, Acetobacteroides, and Limnochorda, were selected as potential biomarkers for PMSI estimation based on a random forest algorithm. The PMSI estimation models established yielded accurate prediction results with mean absolute errors ± the standard error of 1.282 ± 0.189 d for the liver and 0.989 ± 0.237 d for the brain.

CONCLUSIONS

The present study provides novel information on visceral postmortem microbiota succession in corpses submerged in freshwater which sheds new light on PMSI estimation based on the liver and brain in forensic practice.

Advances in artificial intelligence-based microbiome for PMI estimation

Postmortem interval (PMI) estimation has always been a major challenge in forensic science. Conventional methods for predicting PMI are based on postmortem phenomena, metabolite or biochemical changes, and insect succession. Because postmortem microbial succession follows a certain temporal regularity, the microbiome has been shown to be a potentially effective tool for PMI estimation in the last decade. Recently, artificial intelligence (AI) technologies shed new lights on forensic medicine through analyzing big data, establishing prediction models, assisting in decision-making, etc. With the application of next-generation sequencing (NGS) and AI techniques, it is possible for forensic practitioners to improve the dataset of microbial communities and obtain detailed information on the inventory of specific ecosystems, quantifications of community diversity, descriptions of their ecological function, and even their application in legal medicine. This review describes the postmortem succession of the microbiome in cadavers and their surroundings, and summarizes the application, advantages, problems, and future strategies of AI-based microbiome analysis for PMI estimation.

Next-Generation Sequencing Results Vary Between Cultured and Uncultured Microbes

Next-generation sequencing (NGS) technology has led to innovations in environmental metagenomics and investigations involving humans and microbes. However, it is necessary to analyze the components that will affect analysis of the method upon processing a large amount of information. In particular, the processing method after sample collection affects the NGS results, and it is necessary to check for inaccurate results. Here, we show that the microbial communities obtained from fingertip samples differ from those obtained from fingertips remaining on mobile phones and desks, when cultured or not for 24 h. We also confirmed changes in microbial communities in fingertip samples from desks incubated for 2, 4, 8, 16, and 24 h. Samples of prints from mobile phones that are considerably vulnerable to external factors were not analyzed. Ratios of Firmicutes and Bacillus were, respectively, increased in cultures at the phylum and species levels. Collectively, we identified bacterial species that can aid in determining whether a sample has been cultured. In addition, although microbial communities differed depending on sample types, we confirmed changes after culture for 4 and 8 h. However, since this study is a sample limited to three types, it is necessary to analyze other types of samples in the same way and check whether they are applicable to all types. This strategy can verify the suitability of samples for deriving informative results from cultured or uncultured bacterial communities.

A preliminary study on early postmortem submersion interval (PMSI) estimation and cause-of-death discrimination based on nontargeted metabolomics and machine learning algorithms

Postmortem submersion interval (PMSI) estimation and cause-of-death discrimination of corpses in water have long been challenges in forensic practice. Recently, many studies have linked postmortem metabolic changes with PMI extension, providing a potential strategy for estimating PMSI using the metabolome. Additionally, there is a lack of potential indicators with high sensitivity and specificity for drowning identification. In the present study, we profiled the untargeted metabolome of blood samples from drowning and postmortem submersion rats at different PMSIs within 24 h by liquid chromatography-tandem mass spectrometry (LC-MS/MS). A total of 601 metabolites were detected. Four different machine learning algorithms, including random forest (RF), partial least squares (PLS), support vector machine (SVM), and neural network (NN), were used to compare the efficiency of the machine learning methods. Nineteen metabolites with obvious temporal regularity were selected as candidate biomarkers according to "IncNodePurity." Robust models were built with these biomarkers, which yielded a mean absolute error of 1.067 h. Additionally, 36 other metabolites were identified to build the classifier model for discriminating drowning and postmortem submersion (AUC = 1, accuracy = 95%). Our results demonstrated the potential application of metabolomics combined with machine learning in PMSI estimation and cause-of-death discrimination.

Sternal aspirate sampling of Bacillariophyceae (diatoms) and Cyanobacteria in suspected drowning cases

A diagnosis of drowning is not always possible based on the traditional autopsy findings. The most widely used ancillary methods are based on the detection of diatoms and other waterborne organisms in the organs of the systemic circulation by light microscope or polymerase chain reaction (PCR). One of the greatest concerns is sample contamination. Bone marrow is a favourable source because the compact bone protects the sample from water ingress in the case of advanced decay. In our pilot study, we aimed to adopt sternal bone marrow aspiration - which is a widely used technique in haematology - for postmortem sampling. Control experiments of non-drowning victims showed that cleaning the skin over the sternum can prevent external contamination. Sternal aspirate samples were taken from seven suspected drowning victims along with lung, spleen, and femoral bone marrow samples. All specimens were examined for the presence of diatoms by light microscope and Cyanobacteria-specific DNA by PCR. We were able to obtain bone marrow aspirates from all cases without complications. In four of the sternal samples both diatoms and cyanobacterial DNA were detected, while one additional sternum sample was tested positive with PCR, but no diatom shells were detectable. Sternal bone marrow aspiration is simple and quick, which can be performed at the beginning of an autopsy, minimizing the chance of contamination. We have shown that this sampling method can be adopted for postmortem diatom testing. This minimally invasive technique might be used in virtual autopsy (postmortem computed tomography, PMCT) settings without opening body cavities.