Candidate biomarkers in brown adipose tissue for post-mortem diagnosis of fatal hypothermia.

Post-mortem diagnosis of fatal hypothermia (FHT) is challenging in forensic practice because traditional morphological and biochemical methods lack specificity. Recent studies have reported that brown adipose tissue (BAT) is activated during cold-induced non-shivering thermogenesis in mammals, but BAT has not been used to diagnose FHT. The aim of this study was to identify novel biomarkers in BAT for FHT based on morphological changes and differential protein expression. Two FHT animal models were created by exposing mice to 4 or -20 °C at 50% humidity. Morphologically, the unilocular lipid droplet content was significantly increased in BAT of FHT model mice compared with that of control mice. Proteomics analysis revealed a total of 283 and 266 differentially expressed proteins (DEPs) between the 4 or -20 °C FHT subgroups and control group, respectively. In addition, 140 proteins were shared between the FHT subgroups. GO and KEGG analyses revealed that the shared DEPs were mainly enriched in pathways associated with metabolism, oxidative phosphorylation, and thermogenesis. Further screening (|log2FC| > 1.6, q-value (FDR) < 0.05) identified GMFB, KDM1A, DDX6, RAB1B, SHMT-1, CLPTM1, and LMF1 as candidate biomarkers of FHT. Subsequent validation experiments were performed in FHT model mice using classic immunohistochemistry and western blotting. RAB1B and GMFB expression was further verified in BAT specimens from human cases of FHT. The results demonstrate that BAT can be used as a target organ for FHT diagnosis employing RAB1B and GMFB as biological markers, thus providing a new strategy for the post-mortem diagnosis of FHT in forensic practice.

China rheumatoid arthritis registry of patients with Chinese medicine (CERTAIN): Rationale, design, and baseline characteristics of the first 11,764 enrollees.

BACKGROUND: Chinese medicine (CM) has become a popular interventional treatment for rheumatoid arthritis (RA). However, limited knowledge about general characteristics and long-term clinical outcomes hampers the development of CM for RA. PURPOSE: The main objectives of the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) were to describe the population of RA patients receiving CM treatment in multiple centers in China using different variables and compare these findings with internationally reported data. STUDY DESIGN: The CERTAIN is a prospective, multicenter, observational disease registry. METHODS: Adult RA patients who fulfilled the 2010 American College of Rheumatology/ European League Against Rheumatism classification criteria for RA and received CM treatment were recruited into the CERTAIN by rheumatologists from 145 hospitals across 30 provinces in China. Data on demographics, disease characteristics, comorbidities, treatments, and adverse events, with a 2-year follow-up, were collected and documented using a predefined protocol. RESULTS: In the 2 years since the study began in September 2019, 11,764 patients have been enrolled (enrolment is ongoing), and 13.10% of participants have completed the 6-month follow-up. We present the baseline characteristics of the first 11,764 enrollees. CONCLUSIONS: The CERTAIN is the first nationwide registry to document comprehensive data on CM treatment in patients with RA. The development of the CERTAIN resource is a significant step forward for Chinese RA patients, herbal medicine users, and research communities and will deepen our understanding of CM for RA. REGISTRATION: The study was registered at ClinicalTrials.gov (NCT05219214).

Inferring Postmortem Submersion Interval in Rats Found in Water Based on Vitreous Humor Metabolites.

OBJECTIVES: The metabolomics technique of LC-MS/MS combined with data analysis was used to detect changes and differences in metabolic profiles in the vitreous humor of early rat carcasses found in water, and to explore the feasibility of its use for early postmortem submersion interval (PMSI) estimation and the cause of death determination. METHODS: The experimental model was established in natural lake water with 100 SD rats were randomly divided into a drowning group (n=50) and a postmortem (CO2 suffocation) immediately submersion group (n=50). Vitreous humor was extracted from 10 rats in each group at 0, 6, 12, 18 and 24 h postmortem for metabolomics analyses, of which 8 were used as the training set to build the model, and 2 were used as test set. PCA and PLS multivariate statistical analysis were performed to explore the differences in metabolic profiles among PMSI and causes of death in the training set samples. Then random forest (RF) algorithm was used to screen several biomarkers to establish a model. RESULTS: PCA and PLS analysis showed that the metabolic profiles had time regularity, but no differences were found among different causes of death. Thirteen small molecule biomarkers with good temporal correlation were selected by RF algorithm. A simple PMSI estimation model was constructed based on this indicator set, and the data of the test samples showed the mean absolute error (MAE) of the model was 0.847 h. CONCLUSIONS: The 13 metabolic markers screened in the vitreous humor of rat corpses in water had good correlations with the early PMSI. The simplified PMSI estimation model constructed by RF can be used to estimate the PMSI. Additionally, the metabolic profiles of vitreous humor cannot be used for early identification of cause of death in water carcasses.

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.

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

The diagnosis of drowning is one of the major challenges in forensic practice, especially when the corpse is in a state of decomposition. Novel indicators of drowning are desired in the field of forensic medicine. In the past decade, aquatic bacteria have attracted great attention from forensic experts because they can easily enter the blood circulation with drowning medium, and some of them can proliferate in the corpse. Recently, the advent of next-generation sequencing (NGS) has created new opportunities to efficiently analyze whole microbial communities and has catalyzed the development of forensic microbiology. We presumed that NGS could be a potential method for diagnosing drowning. In the present study, we verified this hypothesis by fundamental experiments in drowned and postmortem-submersed rat models. Our study revealed that detecting the bacterial communities with NGS and processing the data in a transparent way with unweighted UniFrac-based principal coordinates analysis (PCoA) could clearly discriminate the skin, lung, blood, and liver specimens of the drowning group and postmortem submersion group. Furthermore, the acquired information could be used to identify new cases. Taken together, these results suggest that we could build a microbial database of drowned and postmortem-submersed victims by NGS and subsequently use a bioinformatic method to diagnose drowning in future forensic practice.