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Metabonomics Analysis of Brain Stem Tissue in Rats with Primary Brain Stem Injury Caused Death.
Su, Qin; Chen, Qian-Ling; Wu, Wei-Bin; Xiang, Qing-Qing; Yang, Cheng-Liang; Qiao, Dong-Fang; Li, Zhi-Gang.
  • Su Q; Key Laboratory of Forensic Pathology, Ministry of Public Security, Guangzhou Forensic Science Institute, Guangzhou 510442, China.
  • Chen QL; Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China.
  • Wu WB; School of Forensic Medicine, Southern Medical University, Guangzhou 510515, China.
  • Xiang QQ; Key Laboratory of Forensic Pathology, Ministry of Public Security, Guangzhou Forensic Science Institute, Guangzhou 510442, China.
  • Yang CL; Key Laboratory of Forensic Pathology, Ministry of Public Security, Guangzhou Forensic Science Institute, Guangzhou 510442, China.
  • Qiao DF; School of Forensic Medicine, Southern Medical University, Guangzhou 510515, China.
  • Li ZG; School of Forensic Medicine, Southern Medical University, Guangzhou 510515, China.
Fa Yi Xue Za Zhi ; 39(4): 373-381, 2023 Aug 25.
Article en En, Zh | MEDLINE | ID: mdl-37859476
ABSTRACT

OBJECTIVES:

To explore the potential biomarkers for the diagnosis of primary brain stem injury (PBSI) by using metabonomics method to observe the changes of metabolites in rats with PBSI caused death.

METHODS:

PBSI, non-brain stem brain injury and decapitation rat models were established, and metabolic maps of brain stem were obtained by LC-MS metabonomics method and annotated to the HMDB database. Partial least square-discriminant analysis (PLS-DA) and random forest methods were used to screen potential biomarkers associated with PBSI diagnosis.

RESULTS:

Eighty-six potential metabolic markers associated with PBSI were screened by PLS-DA. They were modeled and predicted by random forest algorithm with an accuracy rate of 83.3%. The 818 metabolic markers annotated to HMDB database were used for random forest modeling and prediction, and the accuracy rate was 88.9%. According to the importance in the identification of cause of death, the most important metabolic markers that were significantly up-regulated in PBSI group were HMDB0038126 (genipinic acid, GA), HMDB0013272 (N-lauroylglycine), HMDB0005199 [(R)-salsolinol] and HMDB0013645 (N,N-dimethylsphingosine).

CONCLUSIONS:

GA, N-lauroylglycine, (R)-salsolinol and N,N-dimethylsphingosine are expected to be important metabolite indicators in the diagnosis of PBSI caused death, thus providing clues for forensic medicine practice.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Lesiones Encefálicas / Metabolómica Límite: Animals Idioma: En / Zh Año: 2023 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Lesiones Encefálicas / Metabolómica Límite: Animals Idioma: En / Zh Año: 2023 Tipo del documento: Article