ArticleForensic toxicology2025
Bioinformatics-driven untargeted metabolomic profiling for clinical screening of methamphetamine abuse.
Article in Forensic toxicology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- A comprehensive assessment of carbon monoxide caused mortality via bioinformatics assisted untargeted metabolomics analysis.Forensic science, medicine, and pathology · 2026Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeAmphetamine-type stimulants are very common, and their usage is becoming a very big social problem all over the world. Thousands of addicts encounter several health problems including mental, metabolic, behavioral and neurological disorders. In addition to these, there are several reports about the elevated risk of tendency on committing criminal cases by addicted persons. Hence, methamphetamine addiction is not only an individual health problem but also a social problem. In our study, we aimed to investigate the pathogenesis of chronic usage of methamphetamine via untargeted metabolomics approach.
methods38 plasma samples were carefully collected and extracted for untargeted metabolomics assay. A liquid-liquid extraction was performed to get as much metabolite as possible from the samples. After the extraction procedure, samples were transferred into vials and they were evaluated via time of flight mass spectrometry instrument.
resultsSignificantly, altered metabolites were identified by the fold analysis and Welch's test between the groups. 42 different compounds were annotated regarding to data-dependent acquisition method. Pathway analysis were also performed to understand the hazardous effect of methamphetamine on human body.
conclusionIt has been reported that drug exposure may affect several metabolic pathways for amino acids, fats, energy metabolism and vitamins. An alternative bioinformatic model was also developed and validated in order to predict the chronic methamphetamine drug users in any criminal cases. This generated model passes the ROC curve analysis and permutation test and classify the controls and drug users correctly by evaluating the metabolic alterations between the groups.
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