49 results about "Targeted metabolomics" patented technology

The invention discloses a GC-MS-based plant non-targeted metabolomics sample pretreatment method. The method comprises the steps of 1) mixing up a plant sample, an internal standard substance and a hydrophilic organic solvent, wherein the hydrophilic organic solvent is pre-cooled to be -20 to 4 DEG C; cooling the obtained mixture to be -80 to -10 DEG C, grinding, crushing and conducting the supersonic extraction in the ice-water bath; 2) then respectively adding a lipophilic organic solvent and water, uniformly mixing up, conducting the supersonic extraction in the ice-water bath, conducting the high-speed centrifugation at the temperature of 0 to 16 DEG C, obtaining the aqueous phase and evaporating; 3) adding an oximation reagent, and conducting the oximation reaction at the temperature of 30 to 45 DEG C; 4) finally adding a derivatization reagent and n-hexane, and conducting the derivatization reaction at the temperature of 60 to 80 DEG C. According to the technical scheme of the invention, based on the above pretreatment method, the primary metabolites of a plant sample can be fully extracted, so that the abundant metabolite spectrum data information can be obtained. Meanwhile, both the change of metabolites during the extraction process, and the pollution of liposoluble substances to a gas chromatographic column can be avoided as much as possible. Therefore, the better sample reproducibility is realized.

The invention discloses a compound used in the diagnosis of polycystic ovary syndrome. The compound is one of androsterone glucuronide or sphingomyelin (d17:1 / 24:1), or the compound is Combination of androstone glucuronide with sphingomyelin (d17:1 / 24:1). The beneficial effect of the present invention is that the non-target metabolomics research of polycystic ovary syndrome urine is carried out by ultra-high pressure liquid phase tandem time-of-flight mass spectrometry, and androsterone glucuronide, sphingomyelin lipid (d17:1 / 24: 1) and their combination have better diagnostic value for polycystic ovary syndrome. The purpose of simple and rapid diagnosis of polycystic ovary syndrome is achieved, and the detection cost is reduced at the same time.

The method of studying the anti-insomnia mechanism of Sugemule based on amino acid metabolomics is to use GC-MS method to analyze the amino acid metabolome in the brain tissues of PCPA insomnia model rats before and after Sugemule gavage. Source compounds, using PCA and PLS-DA modes to analyze changes in metabolite profiles in various parts of the brain and identify related metabolic markers; use LC-MS to carry out targeted metabolomics research, and investigate 5-Hydroxytryptamine, GABA in brain tissue , Glu and other neurotransmitters and related metabolite content changes; by looking for differential metabolites and their metabolic pathways related to the curative effect of Sugemule in clinical rat brain tissue samples, the mechanism and location of the anti-insomnia effect of Sugemule were clarified point; the present invention applies metabolomics research means for the first time, and preliminarily proves the mechanism of action of Sugemule regulating sleep-wake rhythm through the neurotransmitter system.

The present invention is a pseudo-target metabolomics analysis method based on liquid chromatography / mass spectrometry. The samples to be analyzed are respectively prepared into combined samples according to the groups, and the ultra-high performance liquid chromatography / quadrupole-time-of-flight mass spectrometry is used for the analysis. The data-dependent acquisition mode automatically collects the secondary mass spectrometry of the metabolites contained in each combined sample, and uses qualitative analysis software to extract the retention time, parent ion and product ion information of the measured metabolites, and screen out the metabolites according to the response intensity of the product ions. The characteristic transition information is added, and the characteristic transition information obtained from each combined sample is summed. Then, in the actual sample, UHPLC / QQQ MS is used to scan the obtained metabolite characteristic ion pairs through dynamic multiple reaction monitoring mode to obtain the corresponding spectrum, and then integrated through quantitative analysis software to obtain the metabolite contained in the sample to be tested. information on the substance and its content.