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.

Film-Stabilizing Attributes of Polymeric Core-Shell Nanoparticles.

Self-organization of nanoparticles into stable, molecularly thin films provides an insightful paradigm for manipulating the manner in which materials interact at nanoscale dimensions to generate unique material assemblies at macroscopic length scales. While prior studies in this vein have focused largely on examining the performance of inorganic or organic/inorganic hybrid nanoparticles (NPs), the present work examines the stabilizing attributes of fully organic core-shell microgel (CSMG) NPs composed of a cross-linked poly(ethylene glycol dimethacrylate) (PEGDMA) core and a shell of densely grafted, but relatively short-chain, polystyrene (PS) arms. Although PS homopolymer thin films measuring from a few to many nanometers in thickness, depending on the molecular weight, typically dewet rapidly from silica supports at elevated temperatures, spin-coated CSMG NP films measuring as thin as 10 nm remain stable under identical conditions for at least 72 h. Through the use of self-assembled monolayers (SAMs) to alter the surface of a flat silica-based support, we demonstrate that such stabilization is not attributable to hydrogen bonding between the acrylic core and silica. We also document that thin NP films consisting of three or less layers (10 nm) and deposited onto SAMs can be fully dissolved even after extensive thermal treatment, whereas slightly thicker films (40 nm) on Si wafer become only partially soluble during solvent rinsing with and without sonication. Taken together, these observations indicate that the present CSMG NP films are stabilized primarily by multidirectional penetration of relatively short, unentangled NP arms caused by NP layering, rather than by chain entanglement as in linear homopolymer thin films. This nanoscale "velcro"-like mechanism permits such NP films, unlike their homopolymer counterparts of comparable chain length and thickness, to remain intact as stable, free-floating sheets on water, and thus provides a viable alternative to ultrathin organic coating strategies.