法医学杂志 ›› 2021, Vol. 37 ›› Issue (2): 151-157.DOI: 10.12116/j.issn.1004-5619.2020.400406

• 论著 • 上一篇    下一篇

大鼠皮肤切创损伤时间与血清标志代谢物的关系

田甜1, 李雪榕1, 翟豪杰1, 张旭东2, 李明3, 刘敏1   

  1. 1. 四川大学华西基础医学与法医学院,四川 成都 610041; 2. 山西医科大学法医学院,山西 太原 030001; 3. 黄南藏族自治州公安局,青海 黄南 811399
  • 收稿日期:2020-04-11 发布日期:2021-04-25 出版日期:2021-04-28
  • 通讯作者: 刘敏,男,教授,主要从事法医病理学教学、科研和鉴定;E-mail:min8liu@hotmail.com
  • 作者简介:田甜(1993—),女,硕士研究生,主要从事法医病理学研究;E-mail:13620619303@163.com

Relationship between Wound Age and Serum Marker Metabolites of Rats Skin Incised Wound

TIAN Tian1, LI Xue-rong1, ZHAI Hao-jie1, ZHANG Xu-dong2, LI Ming3, LIU Min1   

  1. 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
  • Received:2020-04-11 Online:2021-04-25 Published:2021-04-28

摘要: 目的 观察大鼠皮肤切创后血清代谢组学变化情况,推断皮肤切创的损伤时间。 方法 建立大鼠皮肤切创模型,将21只SD大鼠分为切创后1、2、4、8、16、24 h组和对照组,实验组大鼠于伤后相应时间点取血,对照组直接取血。使用气相色谱-质谱联用(gas chromatography-mass spectrometry,GC-MS)技术检测血清代谢物并筛选标志代谢物,采用正交偏最小二乘-判别分析(orthogonal partial least square-discriminant analysis,OPLS-DA)模式建立标志代谢物含量与损伤时间关系的回归模型,对皮肤切创损伤时间进行推断。 结果 使用GC-MS对所取血清进行检测,初筛得到21种标志代谢物,使用多元统计分析筛选出4种标志代谢物,其含量的变化规律与损伤时间之间无对应关系,不能直接用于损伤时间推断。使用OPLS模式可得到21种标志代谢物和4种标志代谢物的含量与损伤时间的回归模型,两者均可对损伤时间进行推断,但21种标志代谢物的回归模型预测准确性明显更高。 结论 利用代谢组学方法建立代谢物含量与损伤时间的回归模型,有望应用于皮肤切创的损伤时间推断。

关键词: 法医病理学, 代谢组学, 创伤和损伤, 代谢物, 损伤时间推断, 回归模型, 大鼠

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.

Key words: forensic pathology, metabolomics, wounds and injuries, metabolite, wound age determination, regression model, rats

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