Forensic identification of sudden cardiac death: a new approach combining metabolomics and machine learning

Manner of death prediction: A machine learning approach to classify suicide and non-suicide using blood metabolomics

The classification of the manner of death (MOD) is a critical step in forensic investigations. The process is based on scene investigation, autopsy, histological and toxicological findings. However, in complex suicide cases, these findings may be insufficient to clearly establish the MOD and need potential biomarkers to assist judicial determinations. This study aims to identify specific biomarkers in the blood that could distinguish suicide from the non-suicidal deaths group. Heart blood samples were collected from suicide (n = 45) and non-suicide cases (n = 45) and metabolomic profiles were analyzed using proton nuclear magnetic resonance spectroscopy. Nineteen blood metabolites were significantly different between the groups (p < 0.05); especially, 4-hydroxyproline, sarcosine and heparan sulfate emerged as potential biomarkers for differentiating between the groups. A logistic regression-based predictive model incorporating sarcosine and heparan sulfate achieved sensitivity and specificity values of 73 % and 72 %, respectively. The integration of machine learning with blood metabolomics holds significant potential in forensic science and may apply to the model to adopt in criminal justice.

Novel Strategy for Human Deep Vein Thrombosis Diagnosis Based on Metabolomics and Stacking Machine Learning

Deep vein thrombosis (DVT) is a serious health issue that often leads to considerable morbidity and mortality. Diagnosis of DVT in a clinical setting, however, presents considerable challenges. The fusion of metabolomics techniques and machine learning methods has led to high diagnostic and prognostic accuracy for various pathological conditions. This study explored the synergistic potential of dual-platform metabolomics (specifically, gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS)) to expand the detection of metabolites and improve the precision of DVT diagnosis. Sixty-one differential metabolites were identified in serum from DVT patients: 22 from GC-MS and 39 from LC-MS. Among these, five key metabolites were highlighted by SHapley Additive exPlanations (SHAP)-guided feature engineering and then used to develop a stacking diagnostic model. Additionally, a user-friendly interface application system was developed to streamline and automate the application of the diagnostic model, enhancing its practicality and accessibility for clinical use. This work showed that the integration of dual-platform metabolomics with a stacking machine learning model enables faster and more accurate diagnosis of DVT in clinical environments.

Emergence of Artificial Intelligence and Machine Learning Models in Sudden Cardiac Arrest: A Comprehensive Review of Predictive Performance and Clinical Decision Support

Sudden cardiac death/sudden cardiac arrest (SCD/SCA) is an increasingly prevalent cause of mortality globally, particularly in individuals with preexisting cardiac conditions. The ambiguous premortem warnings and the restricted interventional window related to SCD account for the complexity of the condition. Current reports suggest SCD to be accountable for 20% of all deaths hence accurately predicting SCD risk is an imminent concern. Traditional approaches for predicting SCA, particularly "track-and-trigger" warning systems have demonstrated considerable inadequacies, including low sensitivity, false alarms, decreased diagnostic liability, reliance on clinician involvement, and human errors. Artificial intelligence (AI) and machine learning (ML) models have demonstrated near-perfect accuracy in predicting SCA risk, allowing clinicians to intervene timely. Given the constraints of current diagnostics, exploring the benefits of AI and ML models in enhancing outcomes for SCA/SCD is imperative. This review article aims to investigate the efficacy of AI and ML models in predicting and managing SCD, particularly targeting accuracy in prediction.

Postmortem metabolomics: influence of time since death on the level of endogenous compounds in human femoral blood. Necessary to be considered in metabolome study planning?

INTRODUCTION

The (un)targeted analysis of endogenous compounds has gained interest in the field of forensic postmortem investigations. The blood metabolome is influenced by many factors, and postmortem specimens are considered particularly challenging due to unpredictable decomposition processes.

OBJECTIVES

This study aimed to systematically investigate the influence of the time since death on endogenous compounds and its relevance in designing postmortem metabolome studies.

METHODS

Femoral blood samples of 427 authentic postmortem cases, were collected at two time points after death (854 samples in total; t1: admission to the institute, 1.3-290 h; t2: autopsy, 11-478 h; median ∆t = 71 h). All samples were analyzed using an untargeted metabolome approach, and peak areas were determined for 38 compounds (acylcarnitines, amino acids, phospholipids, and others). Differences between t2 and t1 were assessed by Wilcoxon signed-ranked test (p < 0.05). Moreover, all samples (n = 854) were binned into time groups (6 h, 12 h, or 24 h intervals) and compared by Kruskal-Wallis/Dunn's multiple comparison tests (p < 0.05 each) to investigate the effect of the estimated time since death.

RESULTS

Except for serine, threonine, and PC 34:1, all tested analytes revealed statistically significant changes between t1 and t2 (highest median increase 166%). Unpaired analysis of all 854 blood samples in-between groups indicated similar results. Significant differences were typically observed between blood samples collected within the first and later than 48 h after death, respectively.

CONCLUSIONS

To improve the consistency of comprehensive data evaluation in postmortem metabolome studies, it seems advisable to only include specimens collected within the first 2 days after death.

Multi-omics integration strategy in the post-mortem interval of forensic science

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.

Preparation and application of UPLC silica microsphere stationary phase:A review

In this review, microspheres for ultra-performance liquid chromatography (UPLC) were reviewed in accordance with the literature in recent years. As people's demands for chromatography are becoming more and more sophisticated, the preparation and application of UPLC stationary phases have become the focus of researchers in this field. This new analytical separation science not only maintains the practicality and principle of high-performance liquid chromatography (HPLC), but also improves the step function of chromatographic performance. The review presents the morphology of four types of sub-2 μm silica microspheres that have been used in UPLC, including non-porous silica microspheres (NPSMs), mesoporous silica microspheres (MPSMs), hollow silica microspheres (HSMs) and core-shell silica microspheres (CSSMs). The preparation, pore control and modification methods of different microspheres are introduced in the review, and then the applications of UPLC in drug analysis and separation, environmental monitoring, and separation of macromolecular proteins was presented. Finally, a brief overview of the existing challenges in the preparation of sub-2 μm microspheres, which required further research and development, was given.