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• W4362670147 abstract "Survivors of acute radiation exposure are likely to experience delayed effects that manifest as injury in late-responding organs such as the heart. Non-invasive indicators of radiation-induced cardiac dysfunction are important in the prediction and diagnosis of this disease. In this study, we aimed to identify urinary metabolites indicative of radiation-induced cardiac damage by analyzing previously collected urine samples from a published study. The samples were collected from male and female wild-type (C57BL/6N) and transgenic mice constitutively expressing activated protein C (APCHi), a circulating protein with potential cardiac protective properties, who were exposed to 9.5 Gy of γ-rays. We utilized LC-MS-based metabolomics and lipidomics for the analysis of urine samples collected at 24 h, 1 week, 1 month, 3 months, and 6 months post-irradiation. Radiation caused perturbations in the TCA cycle, glycosphingolipid metabolism, fatty acid oxidation, purine catabolism, and amino acid metabolites, which were more prominent in the wild-type (WT) mice compared to the APCHi mice, suggesting a differential response between the two genotypes. After combining the genotypes and sexes, we identified a multi-analyte urinary panel at early post-irradiation time points that predicted heart dysfunction using a logistic regression model with a discovery validation study design. These studies demonstrate the utility of a molecular phenotyping approach to develop a urinary biomarker panel predictive of the delayed effects of ionizing radia-tion. It is important to note that no live mice were used or assessed in this study; instead, we focused solely on analyzing previously collected urine samples." @default.
• W4362670147 created "2023-04-07" @default.
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• W4362670147 date "2023-04-06" @default.
• W4362670147 modified "2023-10-05" @default.
• W4362670147 title "Urinary Metabolomics for the Prediction of Radiation-Induced Cardiac Dysfunction" @default.
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• W4362670147 doi "https://doi.org/10.3390/metabo13040525" @default.
• W4362670147 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37110184" @default.
• W4362670147 hasPublicationYear "2023" @default.
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