JOURNAL OF CHROMATOGRAPHY B-ANALYTICAL TECHNOLOGIES IN THE BIOMEDICAL AND LIFE SCIENCES
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2021
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IF 3.205
Kai Zhang,Hui Yan,Ruina Liu,Ping Xiang,Kaifei Deng,Ji Zhang,Ya Tuo,Zhenyuan Wang,Ping Huang
The determination of cause of death is one of the most important tasks in forensic practice. However, asphyxia is a difficult cause of death to determine, especially when the deceased has an underlying disease that can lead to a sudden unexpected death, such as coronary atherosclerotic heart disease (CAHD, which is the leading cause of sudden cardiac death, SCD), because its determination is currently still based on an exclusion strategy. In this study, gas chromatography coupled with high-resolution mass spectrometry (GC-HRMS)-based untargeted metabolomics was employed to obtain the pulmonary metabolic profiles of rats who died from asphyxia and SCD. First, fourteen metabolites were identified to investigate the mechanism of death from asphyxia, and we proposed some explanations that may account for these metabolic alterations, including the perturbation of amino acid metabolism, lipid metabolism, and energy metabolism (TCA cycle). Second, we discovered eight potential biomarkers to differentiate between asphyxia and SCD as the cause of death. The excellent classification performances of the eight individual biomarkers and their combination in fresh lung tissue were observed. Third, we also explored the relative change in the concentration of the eight metabolites and their classification performance in decomposed tissue (at 24 h postmortem). Lactic acid, pantothenic acid, and the combination of the eight biomarkers can be recognized as perfect classifiers to discriminate asphyxia from SCD even when decomposition has occurred. Our results showed that GC-HRMS-based untargeted metabolomics can be used as a promising tool to explore the metabolic alterations of the death process and to determine the cause of death.
研究领域:
法医学
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代谢组学
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病理学
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产品关联
DOI
PubMed
高影响因子
Analytical Methods
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2026
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IF 3
Yiming Tian,Yixin Gu,Jianfeng Wu,Jia Chen,Jinqiao Jiang,Yulong Liu,Wei You,Kaikai Li,Jianwei Xie
Synthesis route attribution enables accurate source tracing of chemical warfare agents and effective discrimination between distinct synthetic pathways. In the current study, we report for the first time the identification of trace process-related impurities derived from the synthesis of the vesicant ethyl sesquimustard, which is listed in Schedule 1.A.04 of the Chemical Weapons Convention (CWC). Using 1,2-ethanedithiol and 2-mercaptoethanol as precursors, 22 different synthetic routes were designed, and 88 batches of samples were produced through micro-synthesis. Gas chromatography-high resolution mass spectrometry (GC-HRMS) coupled with a non-targeted screening strategy was employed to analyze route-specific compounds. The obtained dataset was further used to train the orthogonal partial least squares discriminant analysis algorithm and generate a classification model consisting of eight sub-models. The verification results showed that the overall classification accuracy of the model was 21/22 (about 95%).
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DOI
PubMed
高影响因子
ANALYTICAL CHEMISTRY
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2021
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IF 6.986
Caiming Tang,Jianhua Tan,Caixing Tang,Deyun Liu,Peilin Zhang,Xianzhi Peng
Distributions of chlorine isotopologues are potentially a fingerprint feature of organochlorines. However, the exact distributions remain little known. This study measured compound-specific chlorine isotopologue distributions of six polychlorinated organic compounds (POCs) for source identification. Complete chlorine isotopologues of POCs were detected by gas chromatography coupled to high-resolution mass spectrometry. The measured relative abundances (Ameas), theoretical relative abundances (Atheo), and relative variations between Ameas and Atheo (ΔA) of chlorine isotopologues were determined. These ΔA values were applied to characterize differences in isotopologue distribution patterns, and the ΔA patterns directly illustrated the distribution characteristics. Perchloroethylene (PCE) and trichloroethylene (TCE) from two manufacturers were chosen as model analytes to develop and validate the analytical method, including precision, concentration dependency, and temporal drift. The ΔA values of isotopologues of the PCE and TCE chemicals were from −82.5 to 19.9‰ with standard deviations (SDs) of 0.3–16.9‰. In addition, the ΔA values of the first three isotopologues (with 0–2 37Cl atoms) were from −15.5 to 19.9‰ with SDs of 0.3–1.6‰, showing sufficient precisions. No concentration dependency and temporal drift of ΔA were observed. The method has been successfully applied to source identification for PCE and TCE in commercial chemicals and plastic materials, and four polychlorinated biphenyls in chemicals and sediments, demonstrating that the ΔA values and ΔA patterns were discernable for POCs from different sources. This study demonstrates that compound-specific chlorine isotopologue distributions of POCs are differentiable and measurable, proposing a novel approach to perform fingerprinting analysis for the distributions, which is anticipated to facilitate source identification for organochlorine pollutants.
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PubMed
高影响因子
TALANTA
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2025
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IF 6.1
Xian Li,Xinxin Wen,Zewei Luo,Xuejun Wang,Yilin Zhang,Jing Wei,Yonghui Tian,Rui Ling,Yixiang Duan
Breast cancer (BC) is the primary cause of cancer-related deaths in women. Currently, the discovery of biomarkers primarily relies on single platform, which might overlook other potential biomarkers and lead to inaccurate diagnoses. This study aims to: (1) expand the detection range of biomarkers through multiple analytical techniques, thereby improving the accuracy of BC diagnosis, and (2) analyze the metabolic pathways of the biomarkers to explore the metabolic mechanisms underlying BC. Urine samples from BC patients and healthy controls were analyzed using two techniques: Ultra-high performance liquid chromatography combined with Quadrupole-Exactive-Orbitrap mass spectrometry (UPLC-Q-Exactive Orbitrap-MS), and headspace solid-phase microextraction combined with gas chromatography-high resolution mass spectrometry (HS-SPME/GC-HRMS). Data from each platform was analyzed independently using both univariate and multivariate statistical approaches to identify candidate biomarkers. Subsequently, a mid-level data fusion approach was applied to integrate the candidate biomarkers identified by each platform. The fused data were used to construct orthogonal partial least squares discriminant analysis (OPLS-DA) models and random forest (RF) models, which were then compared against models based on individual platform. The fused RF and OPLS-DA models demonstrated enhanced diagnostic accuracy compared to the individual model. Integrating GC-HRMS and UPLC-Q-Exactive Orbitrap-MS achieved the best performance, with an AUC value of 0.967, sensitivity of 86.37 %, and specificity of 89.19 %. Metabolic pathway analysis revealed that 10 metabolic pathways exert an impact on BC. Four pathways—pyruvate metabolism, sulfur metabolism, taurine and hypotaurine metabolism, and tyrosine metabolism—were found to be associated with BC in both metabolomics and volatolomics studies, indicating that these pathways play pivotal roles in BC. This study confirmed the potential of merging multi-platforms to enhance the accuracy of BC diagnosis, offering new avenues for understanding the metabolic mechanisms of BC.
研究领域:
医学
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代谢组学
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生物信息学
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产品关联
DOI
PubMed
高影响因子
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