Biomarker profiling of postmortem blood for diabetes mellitus and discussion of possible applications of metabolomics for forensic casework

Nariai, Maika; Abe, Hiroko; Hoshioka, Yumi; Makino, Yohsuke; Iwase, Hirotaro

doi:10.1007/s00414-021-02767-w

Cited by 4 publications

(2 citation statements)

References 45 publications

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“…It is difficult to determine the cause of death in judicial autopsy, when forensic pathologists suspect the cause of death to be diabetes mellitus and its complications, due to the scarcity of biomarkers. Nariai et al [108] used the metabolomics approach to select biomarkers related to diabetes mellitus after death, and found a group of metabolites to diagnose diabetes mellitus, including sphingomyelin, plasmalogen lipid, and lysophospholipid. In postmortem diagnosis, Zhang et al [109] selected 36 metabolite biomarkers to develop the classification model, which can help forensic pathologists in PMI estimation and the identification of drowning and postmortem submersion.…”

Section: Metabolomicsmentioning

confidence: 99%

The Role of Protein Degradation in Estimation Postmortem Interval and Confirmation of Cause of Death in Forensic Pathology: A Literature Review

Huang,

Zhao,

Liu

et al. 2024

IJMS

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It is well known that proteins are important bio-macromolecules in human organisms, and numerous proteins are widely used in the clinical practice, whereas their application in forensic science is currently limited. This limitation is mainly attributed to the postmortem degradation of targeted proteins, which can significantly impact final conclusions. In the last decade, numerous methods have been established to detect the protein from a forensic perspective, and some of the postmortem proteins have been applied in forensic practice. To better understand the emerging issues and challenges in postmortem proteins, we have reviewed the current application of protein technologies at postmortem in forensic practice. Meanwhile, we discuss the application of proteins in identifying the cause of death, and postmortem interval (PMI). Finally, we highlight the interpretability and limitations of postmortem protein challenges. We believe that utilizing the multi-omics method can enhance the comprehensiveness of applying proteins in forensic practice.

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Section: Metabolomicsmentioning

confidence: 99%

The Role of Protein Degradation in Estimation Postmortem Interval and Confirmation of Cause of Death in Forensic Pathology: A Literature Review

Huang,

Zhao,

Liu

et al. 2024

IJMS

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“…The independent t-test is a parametric test that is commonly used to compare the mean difference between two groups (ADHD vs. healthy control). It is also widely used for the identification of significant biomarkers for various diseases like cancers [57], ADHD [25], and diabetes [71] etc. In this study, we used an independent t-test for the selection of channels.…”

Section: ) Independent T-test Based Channel Selectionmentioning

confidence: 99%

Optimal Channels and Features Selection Based ADHD Detection From EEG Signal Using Statistical and Machine Learning Techniques

et al. 2023

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A TTENTION deficit hyperactivity disorder (ADHD) is one of the major psychiatric and neurodevelopment disorders worldwide. Electroencephalography (EEG) signal-based approach is very important for the early detection and classification of children with ADHD. However, diagnosing children with ADHD using full EEG channels with all features may lead to computational complexity and overfitting problems.To solve these problems, machine learning (ML)-based ADHD detection was designed by identifying optimal channels and its significant features. In this work, support vector machine and t-test based, two separate approaches were devised to select optimal channels individually and then proposed a hybrid channel selection approach by combining these two channel selection methods in order to select the optimal channels. After that, LASSO logistic regression-based model was used to select the important features from the selected channels. Finally, six ML-based classifiers, like Gaussian process classification (GPC), random forest, k-nearest neighbors, multilayer perceptron, decision tree, and logistic regression were applied for the detection of children with ADHD and evaluated their performances using accuracy and area under the curve (AUC). This study utilized a total of one hundred twenty-one children, with sixty-one children with ADHD, aged 7-12 years, and had nineteen channels. Ten different channels were selected by SVM based and an independent t-test-based approach separately and six overlapping channels were identified from both channel selection methods. Then, we selected twenty-eight features from selected six channels using LASSO. Using only six channels and twenty-eight features, GP-based classifier achieved an accuracy rate of 97.53% and AUC of 0.999. This is an improvement of 3% over previously published values in the literature. This study illustrated that LASSO with GP-based system performed outstanding performance in distinguishing children with ADHD from healthy children. This proposed system will be helpful to doctors and physicians in order to detect children with ADHD at an early stage and take the necessary steps for the patients to access appropriate healthcare services, receive effective treatment, and be more conscious of maintaining their lives.

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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?

Steuer,

Wartmann,

Schellenberg

et al. 2024

Metabolomics

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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.

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Biomarker profiling of postmortem blood for diabetes mellitus and discussion of possible applications of metabolomics for forensic casework

Cited by 4 publications

References 45 publications

The Role of Protein Degradation in Estimation Postmortem Interval and Confirmation of Cause of Death in Forensic Pathology: A Literature Review

The Role of Protein Degradation in Estimation Postmortem Interval and Confirmation of Cause of Death in Forensic Pathology: A Literature Review

Optimal Channels and Features Selection Based ADHD Detection From EEG Signal Using Statistical and Machine Learning Techniques

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?

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