Relationship between Wound Age and Serum Marker Metabolites of Rats Skin Incised Wound.
10.12116/j.issn.1004-5619.2020.400406
- Author:
Tian TIAN
1
;
Xue Rong LI
1
;
Hao Jie ZHAI
1
;
Xu Dong ZHANG
2
;
Ming LI
3
;
Min LIU
1
Author Information
1. West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu 610041, China.
2. School of Forensic Medicine, Shanxi Medical University, Taiyuan 030001, China.
3. Huangnan Tibetan Autonomous Prefecture Public Security Bureau, Huangnan 811399, Qinghai Province, China.
- Publication Type:Journal Article
- Keywords:
forensic pathology;
metabolomics;
wounds and injuries;
metabolite;
wound age determination;
regression model;
rats
- MeSH:
Animals;
Biomarkers;
Gas Chromatography-Mass Spectrometry;
Metabolomics;
Rats;
Rats, Sprague-Dawley;
Skin
- From:
Journal of Forensic Medicine
2021;37(2):151-157
- CountryChina
- Language:English
-
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.
