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Serum metabolomic profiling of acute complete spinal cord transection in macaques / 中国组织工程研究
Chinese Journal of Tissue Engineering Research ; (53): 5818-5823, 2017.
Article in Chinese | WPRIM | ID: wpr-698317
ABSTRACT

BACKGROUND:

Non-targeted metabolomic profiling is used to uncover metabolic changes and to identify relationships between metabolites and spinal cord injury,which contributes to further understanding the pathophysiological process and mechanisms of secondary spinal cord injury.

OBJECTIVE:

To detect and analyze the serum metabolite changes after complete spinal cord transaction in macaques,explore its relationship with the pathophysiological progress of spinal cord injury,and screen the potential biomarkers.

METHODS:

Five adult macaques were selected,in which the models of complete spinal cord transaction were established.The serum metabolic features were detected using a non-targeted metabolic profiling strategy based on gas chromatography time-of-flight mass spectrometry at 1 day before modeling,3 hours (superacute phase) and 3 days (acute phase) after modeling.After compared with the spectrometry profiling,recognizing the metabolites,searching for differential metabolites and the related metabolic pathways,the pathophysiological process and mechanisms were analyzed.RESULTS AND

CONCLUSION:

Three hundred and fifty-eight chromatographic peaks were obtained for subsequent data analysis.Fourteen metabolites,including low-molecular-weight organic acid,amino acids,fatty acid and carbohydrate,were identified as differential metabolites.To conclude,the acute phase of complete spinal cord transection is closely related to some metabolic pathways,such as amino acid metabolism,tricarboxylic acid cycle and pyruvate metabolism.
Full text: Available Index: WPRIM (Western Pacific) Language: Chinese Journal: Chinese Journal of Tissue Engineering Research Year: 2017 Type: Article

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Full text: Available Index: WPRIM (Western Pacific) Language: Chinese Journal: Chinese Journal of Tissue Engineering Research Year: 2017 Type: Article