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Abstract: Objective To observe the metabolomics changes of serum after skin incision of rats and to determine the wound age of skin incision. Methods A rat skin incision model was established, 21 SD rats were divided into 1 h, 2 h, 4 h, 8 h, 16 h, 24 h after skin incision groups and the control group, then blood was taken from rats in the experimental groups at the corresponding time points after injury, and taken from the control group directly. Gas chromatography-mass spectrometry （GC-MS） technology was used to detect serum metabolites and screen marker metabolites, then orthogonal partial least square-discriminant analysis （OPLS-DA） model was used to establish a regression model for the relationship between marker metabolite content and wound age to determine wound age of skin. Results GC-MS was used to detect the serum collected, and 21 marker metabolites were obtained through initial screening, and 4 marker metabolites were further analyzed and screened using multivariate statistical analysis methods. There was no correspondence between the change rule of the serum content and wound age, therefore it cannot be used directly to determine wound age. OPLS model could be used to obtain regression models of the content and wound age of 21 marker metabolites and 4 marker metabolites, both of which can determine wound age, but the prediction accuracy of the regression model of 21 marker metabolites was significantly higher. Conclusion Using metabolomics to establish a regression model of the metabolite content and wound age has the potential to be applied to skin incision wound age determination., Competing Interests: The authors of this article and the planning committee members and staff have no relevant financial relationships with commercial interests to disclose., (Copyright© by the Editorial Department of Journal of Forensic Medicine.)

Abstract: Objective To screen serum biomarkers after skeletal muscle contusion in rats based on gas chromatography-mass spectrometry （GC-MS） metabolomics technology, and support vector machine （SVM） regression model was established to estimate skeletal muscle contusion time. Methods The 60 healthy SD rats were randomly divided into experimental group （n=50）, control group （n=5） and validation group （n=5）. The rats in the experimental group and the validation group were used to establish the model of skeletal muscle contusion through free fall method, the rats in experimental group were executed at 0 h, 2 h, 4 h, 8 h, 12 h, 24 h, 48 h, 96 h, 144 h and 240 h, respectively, and the rats in validation group were executed at 192 h, while the rats in the control group were executed after three days' regular feeding. The skeletal muscles were stained with hematoxylin-eosin （HE）. The serum metabolite spectrum was detected by GC-MS, and orthogonal partial least square-discriminant analysis （OPLS-DA） pattern recognition method was used to discriminate the data and select biomarkers. The SVM regression model was established to estimate the contusion time. Results The 31 biomarkers were initially screened by metabolomics method and 6 biomarkers were further selected. There was no regularity in the changes of the relative content of the 6 biomarkers with the contusion time and the SVM regression model can be successfully established according to the data of 6 biomarkers and the 31 biomarkers. Compared with the injury time ［（55.344±7.485） h］ estimated from the SVM regression model based on the data of 6 biomarkers, the injury time ［（195.781±1.629） h］ estimated from the SVM regression model based on the data of 31 biomarkers was closer to the actual value. Conclusion The SVM regression model based on metabolites data can be used for the contusion time estimation of skeletal muscles., Competing Interests: The authors of this article and the planning committee members and staff have no relevant financial relationships with commercial interests to disclose., (Copyright© by the Editorial Department of Journal of Forensic Medicine.)