145 results about "Targeted metabolomics" patented technology

The invention discloses a plant lactobacillus. The plant lactobacillus has the characteristic of efficiently tolerating 30% (w / v) cistanche substrate. The invention also discloses a medicinal and edible composition consisting of 85 parts of cistanche deserticola, 5 parts of rehmannia, 5 parts of fructus alpiniae oxyphyllae and 5 parts of Chinese yam, the composition is fermented by using plant lactobacillus, and the cistanche deserticola fermentation liquor is prepared by adopting a multi-stage temperature fermentation process to ferment for 16 hours. A non-targeted metabonomics result shows that the phytobacterium plantarum activates an upstream channel for synthesis of phenylethanoid glycoside substances, and the abundance of forsythiaside B is increased by 133 times compared with that before fermentation. The fermentation liquor can achieve the functions of reproductive anti-aging, kidney protection and body endurance improvement by down-regulating COX-2a and TGF-beta-1 gene expression.

The invention belongs to the technical field of biological medicine, and particularly relates to application of a metabolic marker combination in diagnosis of gastric cancer. According to the method, specific metabolites in plasma or tissue of a gastric cancer patient are accurately quantified and analyzed through a targeted metabonomics technology, an optimal metabolic marker combination is developed through machine learning, and a novel non-invasive, efficient and accurate gastric cancer detection method is provided.

The invention relates to the technical field of diagnosis markers and diagnosis methods for mental diseases of children and adolescents, in particular to a plasma metabolism diagnosis marker and diagnosis system for depression of children and adolescents. The plasma metabolism diagnosis marker comprises at least one of cyclic adenosine monophosphate, methylpyridine, unsaturated undecylenyl carnitine, histidine, prolyl hydroxyproline, tryptophan and glucuronic acid. According to the scheme, non-target metabonomics data is analyzed, and seven candidate diagnostic markers for depression of children and adolescents are screened and determined from common differential metabolites. Then, through targeted metabonomics research, diagnostic markers and combinations thereof with ideal diagnostic effects are determined, and then a diagnostic model is constructed. And a sex factor is also incorporated into the diagnosis model, so that the diagnosis effect is further improved. According to the technical scheme, the technical problem that in the prior art, reliability and popularization potential of depression plasma metabolism biomarkers of children and adolescents are not ideal can be solved, and the depression plasma metabolism biomarkers have ideal popularization and application prospects.

The invention relates to the field of strong wine detection, and discloses a strong wine visual non-target metabonomics origin traceability method, which comprises the following steps: firstly, collecting strong wine original data, digitizing and outputting the collected strong wine original data to obtain strong wine output data; performing preliminary analysis by using a clustering analysis method to obtain first analysis data; performing secondary analysis on the first analysis data by using a principal component analysis mode to obtain second analysis data; and finally analyzing the second analysis data by partial least square discriminant analysis or orthogonal partial least square discriminant analysis, and screening, comparing and identifying the characteristic compounds to obtain the non-target metabonomics origin traceability place of the strong wine. According to the method, the problems that a non-target metabonomics traceability technology easy to visually display does not exist in the prior art, and a standardized operation instruction integrating efficient pretreatment, non-target screening, rapid traceability, accurate confirmation and visual display does not exist are solved.

The invention discloses a method for predicting concentration dependence of nano-plastics on crop toxicity. The method comprises the following steps: S1, obtaining a crop sample polluted by nano-plastics, and carrying out sample treatment to obtain a prediction sample; s2, carrying out scanning electron microscope, laser confocal, Raman spectrum and Fourier infrared spectrum comprehensive experiments on the prediction sample at the same time, and collecting a scanning electron microscope spectrum, a laser confocal spectrum, a Raman spectrum and a Fourier infrared spectrum; s3, performing comprehensive operation based on the scanning electron microscope spectrum, the laser confocal spectrum, the Raman spectrum and the Fourier infrared spectrum to obtain a comprehensive spectrum; and S4, predicting the concentration dependence of the nano plastic on the crop toxicity based on machine learning and targeted metabonomics of the comprehensive spectrum. The invention further discloses a corresponding system, electronic equipment and a computer readable storage medium.

The invention belongs to the technical field of biological medicine, and particularly relates to application of phenylalanine in evaluating Kawasaki disease and treating effect thereof and application of a phenylalanine inhibitor in treating Kawasaki disease. According to serum non-targeted metabonomics analysis and verification of KD children and IVIG treated KD children, phenylalanine is highly expressed in KD children, and phenylalanine is remarkably reduced after IVIG treatment of KD and tends to healthy children. The phenylalanine can be used as a serological marker for evaluating the Kawasaki disease treatment effect and diagnosing IVIG second-line treatment, and is high in specificity and sensitivity. After the phenylalanine inhibitor is administered to a KD model mouse, the content of phenylalanine in serum is remarkably reduced and is consistent with the trend in serum of the KD mouse treated by IVIG, myrica tongues are remarkably relieved, arterial dilatation is slowed down, and expression of inflammatory factors is reduced. The phenylalanine inhibitor has the effects of treating Kawasaki disease, improving myrica rubra tongue and slowing down arterial dilatation.

The invention belongs to the technical field of biological medicine, and particularly relates to a screening method of tumor metabolism markers. According to the screening method, wide targeted metabonomics data and targeted metabonomics data are utilized and combined with a biomarker recognition and optimization algorithm (BIO-FIRE) based on function and feature importance recursion enhancement to screen tumor metabolic markers. According to the screening method, a stable metabolic marker combination can be identified in large-scale and multi-center research data, so that a set of efficient, accurate and generalizable gastric cancer metabonomics diagnosis scheme is constructed.

The invention discloses a research method for researching an influence mechanism of high-fat diet on old-age health by using a mouse model, which comprises the following steps: S1, constructing a high-fat diet-induced mouse model: from normal diet feeding of mice to an old mouse stage, dividing the mice into two groups, and respectively performing high-fat diet feeding and normal diet feeding; s2, detecting physiological and functional indexes of the mice in the high-fat diet feeding group and the normal diet feeding group; s3, microbial diversity analysis: performing 16S rRNA sequencing to analyze the community structure of the intestinal microbiota of the mouse; s4, carrying out non-targeted metabonomics analysis: S4.1, carrying out intestinal tract non-targeted metabonomics sequencing; s4.2, carrying out non-targeted metabonomics sequencing on the liver tissues; s5, carrying out transcriptomics analysis; s6, performing conjoint analysis: performing the conjoint analysis on every two results of transcriptomics analysis, non-targeted metabonomics analysis and microbial diversity analysis; and S7, evaluating the influence of the high-fat diet on the health of the old rats by using the data obtained through analysis.

The invention discloses a metabolic reaction pair prediction method based on deep learning and non-targeted metabonomics, and belongs to the technical field of bioinformatics, and the method comprises the steps: (1) enabling reactants and products to form a metabolite reaction pair, and converting the metabolite reaction pair into a molecular fingerprint pair, thereby constructing a metabolic reaction pair data set; (2) establishing a deep learning model of which the input is molecular fingerprints and the output is a metabolic reaction pair prediction result, and training the model by utilizing the established metabolic reaction pair data set to realize accurate modeling; and (3) inputting a molecular fingerprint pair of a compound to be predicted or a metabolic characteristic into the trained model to generate a prediction result of whether a reaction pair is formed or not. The method can predict whether a metabolic reaction pair is formed between a compound pair or a metabolic characteristic pair represented by non-targeted metabonomics secondary mass spectrometry data. According to the method, the information contained in the non-targeted metabonomics data can be fully mined, the utilization efficiency of the metabonomics data is remarkably improved, and the prediction precision is high.

The invention discloses application of beta-alanine in preparation of a medicine for preventing and / or treating uric acid metabolic disorder related diseases. The invention provides a theoretical source of beta-alanine for reducing the uric acid level. In the earlier stage, the applicant of the invention adopts non-target metabolome and targeted metabolome data to construct a hyperuricemia prediction model, and Rmcorr correlation analysis shows that high beta-alanine level and low uric acid level have strong correlation. On the other hand, beta-alanine acid is adopted to intervene in a hyperuricemia mouse, it is proved that beta-alanine can reduce the blood uric acid level of the hyperuricemia mouse and promote urine uric acid excretion, and beta-alanine can be used for preventing and / or treating uric acid metabolic disorder related diseases.

The invention relates to the technical field of analysis and detection, in particular to a screening method of pitaya anthocyanin metabolite based on full-targeted metabonomics and application thereof.The method comprises the steps that pericarp and pulp of four pitaya varieties in the green ripe stage, the coloring stage and the mature stage are selected as raw materials, and samples are obtained after treatment; the method comprises the following steps: respectively extracting effective components from samples, and carrying out liquid chromatography-mass spectrometry detection on an extracting solution; detecting the obtained original data, performing anthocyanin substance difference analysis by using an OPLS-DA method, and screening out Plt; 0.05, VIPgt; according to the method, on the basis of a full-targeting metabonomics method, firstly, six types of 23 anthocyanin substances are identified through liquid chromatography-mass spectrometry, then the anthocyanin differential metabolites are obtained through an OPLS-DA method, the relative content of the differential metabolites has significant difference among the four varieties, the dragon fruit varieties can be effectively identified, and the identification accuracy of the dragon fruit varieties is improved. And the screening of the anthocyanidin differential metabolites of different colored pitaya varieties provides an important basis for a molecular mechanism of anthocyanidin formation.

The invention discloses a screening method of mango pulp processing mode markers and an identification method of mango pulp processing modes, and relates to the technical field of analytical chemistry. According to the method, non-targeted metabonomics is combined with quasi-targeted metabonomics and targeted quantitative analysis, so that the characteristic markers capable of accurately distinguishing mango pulp in different processing modes are systematically screened and verified for the first time. The feature marker screened by the method can be used for constructing a discrimination model of the mango pulp processing mode, high-precision and high-reliability processing mode discrimination is realized based on the established multi-model fusion discrimination method, the discrimination efficiency and accuracy of the mango pulp processing mode are greatly improved, and the method is suitable for popularization and application. And a new technical means and theoretical basis are provided for product quality control and market supervision.

The invention relates to an oxidized fatty acid large-scale qualitative method based on non-targeted metabonomics. The method comprises the following steps: firstly, enriching oxidized fatty acid in a biological sample to obtain non-targeted metabonomics data; secondly, constructing a self-built database which comprises an oxidized fatty acid mass spectrum library, an oxidized fatty acid structure library and an in silico oxidized fatty acid virtual structure library; and then, carrying out self-established virtual database search on data of an oxidized fatty acid extraction sample, and carrying out large-scale identification on the oxidized fatty acid by comparing a first-level ion mass-to-charge ratio, a second-level mass spectrum and / or second-level ion characteristic fragments, retention time and an in-source cracking mode. And finally, co-constructing a molecular network by the secondary mass spectra recorded in all the databases, the identified secondary mass spectra in the samples and the secondary mass spectra of all the unknown metabolites in the samples based on secondary similarity, and carrying out expansion annotation on the unknown oxidized fatty acid through a metabolic reaction relationship.

The invention relates to a depression disease management system based on targeted metabonomics and a machine learning model, and the system is characterized in that the system comprises an intelligent diagnosis model module, a differential diagnosis model module, a grading diagnosis model module, an accompanying diagnosis model module, a treatment endpoint outcome prediction model module, a recurrence prediction model module, and a comprehensive judgment module. Through the system provided by the invention, the problems in the fields of current clinical depression diagnosis, treatment outcome, relapse and the like can be solved, accurate diagnosis and timely treatment are realized, and the mental health level of the whole people is improved.

The invention discloses a method for analyzing component difference between Maotai-flavor yeast and yeast-making wheat by using non-targeted metabonomics. The method comprises the following steps: S1, pre-treating a sample; s2, detecting each sample by adopting the combination of an ultra-high performance liquid chromatograph and a quadrupole time-of-flight mass spectrometer; s3, acquiring original data, performing peak extraction, peak alignment and normalization by adopting metabonomics data analysis software, and evaluating the overall difference and intra-group variation among sample groups by adopting principal component analysis; constructing a grouping discrimination model through discriminant analysis of an orthogonal partial least square method; determining differential metabolites of the two groups of samples by taking variable projection importance (VIP) greater than 1 and statistical significance P less than 0.01 as a primary screening standard and combining a secondary screening standard that the difference multiple log2FC greater than 2 is up-regulated and the difference multiple log2FC less than-2 is down-regulated; s4, performing compound structure and category annotation on the screened differential metabolites through a metabolome database; and performing pathway enrichment analysis by adopting a statistical test method, and determining a core metabolic pathway enriched by the differential metabolites.

The invention discloses an application of astaxanthin in preparation of a regulator for improving the gentisic acid level. On the basis of non-targeted metabonomics analysis, it is found for the first time that in the SAP pathogenesis process, the level of a flora metabolite, namely gentisic acid (GA), in intestinal contents is remarkably reduced, and in-vivo gentisic acid level can be remarkably callback through astaxanthin intervention. Further research proves that exogenous supplement of gentisic acid can effectively simulate the protection effect of astaxanthin, including improvement of pancreatic tissue pathological damage, reduction of serum lipase and amylase activity, and inhibition of systemic inflammatory response (down-regulation of IL-1beta, IL-6 and TNF-alpha and up-regulation of IL-10). The invention not only discloses a new mechanism that astaxanthin plays a therapeutic role through an'intestinal flora-metabolite 'axis, but also determines that gentisic acid is used as a novel drug target for treating SAP, and constructs a new method for screening drugs for treating SAP according to the novel drug target.

The invention belongs to the technical field of compound analysis and identification, and discloses application of a metabonomics analysis technology to identification of lycium seed oil. According to the metabonomics analysis technology, the characteristic components of the lycium seed oil are identified through Log2 Fold Channel and VIP values, and the screening conditions for identifying the characteristic components of the lycium seed oil are | Log2 Fold Channel | > 1.5, and VIP < gt >; the method specifically comprises the steps of sample preparation, non-targeted metabonomics analysis, extraction and identification of characteristic peaks, similarity analysis, difference analysis and screening of differential markers. According to the method, a non-targeted metabonomics analysis method is adopted, various metabolic components in the lycium barbarum seed oil can be undiscriminately covered, the limitation that a traditional identification technology only aims at a few indexes is broken through, the unique characteristic components of the lycium barbarum seed oil can be accurately screened out, and the accuracy and specificity of an identification result are greatly improved.

The invention relates to the field of biomarkers, and particularly provides a polycystic ovarian syndrome pregnancy outcome diagnosis marker and application thereof. According to the present invention, the endometrial targeting metabonomics research is adopted to screen the disease-related potential metabolic markers such as N-Acetylaspartic acid, Suberic acid and isoallolithocholic acid, and the three metabolites are subjected to quantitative detection so as to accurately predict the pregnancy outcome of the polycystic ovarian syndrome patient, such that the pregnancy outcome of the polycystic ovarian syndrome patient can be accurately predicted, and the prediction of the pregnancy outcome of the polycystic ovarian syndrome patient can be easily achieved, such that the disease-related potential metabolic markers such as N-Acetylaspartic acid, suberic acid and isoallolithocholic acid can be provided. A new path is provided for early intervention of the disease state to improve the pregnancy outcome, and a new drug target is provided for evaluation of the endometrial function of the polycystic ovarian syndrome.

The invention belongs to the technical field of drug detection, and particularly relates to a method for detecting lycium barbarum glycopeptide metabolites in rat plasma and tissues based on non-targeted metabonomics. According to the method, after rat lycium barbarum glycopeptides are administered through intragastric administration, blood, liver, intestine, lung and brain tissue samples after administration are collected. A UPLC-Q-TOF-MS (Ultra Performance Liquid Chromatography-Quadrupole Time of Flight Mass Spectrometry) analysis method is combined with a statistical analysis method comprising principal component analysis (PCA) and orthogonal-partial least squares discriminant analysis (OPLS-DA) to carry out in-vivo metabonomics analysis on the wolfberry glycopeptide. Then, based on a human metabolome database (HMDB), lycium barbarum glycopeptide differential metabolite is searched, so that the pharmacological action mechanism of the lycium barbarum glycopeptide is clarified from the perspective of metabolism, and a foundation is laid for deep research and development of the medicinal value of the lycium barbarum glycopeptide in future.

The present invention relates to a depression disease management system based on targeted metabolomics and machine learning models. The system comprises an intelligent diagnostic model module, a differential diagnostic model module, a hierarchical diagnostic model module, a companion diagnostic model module, a treatment endpoint outcome prediction model module, a relapse prediction model module, and a comprehensive judgment module. The system provided by the present invention can address current clinical challenges in depression diagnosis, treatment outcomes, and relapse, enabling accurate diagnosis, timely treatment, and improving the mental health of the general population.

The invention discloses an excrement metabolism marker for predicting spontaneous premature delivery based on UHPLC-MS (Ultra High Performance Liquid Chromatography-Mass Spectrometry) and application, and belongs to the technical field of biological medicines. According to the premature pregnant woman excrement metabolite specific biomarker and the application thereof, the premature pregnant woman excrement metabolite specific biomarker is screened out through a non-targeted metabonomics technology, and the marker is 1-hydroxyl vitamine d2, Mirtazapine n-oxide, Tetraethene glycol or a combination of the 1-hydroxyl vitamine d2, the Mirtazapine n-oxide and the Tetraethene glycol. A premature delivery prediction model established by using the marker is high in sensitivity and good in specificity, and a reference basis is provided for early prediction and accurate prevention of spontaneous premature delivery.

The present invention discloses a method for tracing the origin of dried ginger based on characteristic metabolites. The present invention is based on non-targeted metabolomics analysis technology, and uses ultra-high performance liquid chromatography tandem mass spectrometry to detect metabolites in dried ginger from different origins, obtains overall metabolite data of dried ginger from different origins, and further applies chemometrics and machine learning algorithms to construct a classification model for tracing the origin of dried ginger, and screens characteristic metabolites that affect the distinction of dried ginger from different origins, so as to achieve accurate tracing of the origin of dried ginger. The method for tracing the origin of dried ginger based on characteristic metabolites of the present invention has the advantages of accurate and reliable experimental results, stable tracing models, etc., and can provide a new idea for identifying the origin of dried ginger.

The application discloses a plant lactobacillus. The plant lactobacillus has the characteristics of high tolerance to 30% (w / v) cistanche matrix. The application also discloses a homologous medicinal and edible formula which is composed of 85 parts of cistanche, 5 parts of rehmannia root, 5 parts of yizhi kernel and 5 parts of yam. The formula is fermented by the plant lactobacillus, and a cistanche and yam fermented liquid is prepared by adopting a multi-stage temperature fermentation process for 16 hours. Non-targeted metabolomics results show that the plant lactobacillus activates the upstream pathway of phenylethanoid glycoside synthesis, and the abundance of forsythoside B is up-regulated by 133 times compared with that before fermentation. The fermented liquid can play the functions of reproductive anti-aging, kidney protection and body resistance improvement by down-regulating the expressions of COX-2a and TGF-beta-1 genes.

The invention provides application of acetylcholine and 1-carboxyl-6-hydroxy-3, 4-dihydro-beta-carboline (the code in the attached drawing of the abstract is CHEMBL461889) in early pregnancy diagnosis, non-targeted metabonomics detection and analysis are carried out on the urine of pregnant and non-pregnant sows by virtue of a liquid chromatography-mass spectrometry technology; the two metabolic markers capable of being used for early pregnancy diagnosis of the sows are screened out by introducing two deep learning models for joint analysis, the AUC values of ROC curves of the two metabolic markers are 1, the accuracy is very high, and the pregnancy state of the sows can be accurately judged only 18 days after the sows are bred. The invention provides a novel effective sow early pregnancy diagnosis method for the pig breeding industry, and has important significance for reducing the breeding cost.

The present invention discloses a biological age prediction method and device based on non-targeted metabolomics, which relates to the field of biological data processing, including: constructing a biological age prediction model based on stacking integration and training it to obtain a trained biological age prediction model; processing and feature selection of mass spectrometry data of each metabolite in the blood sample of the person to be analyzed to obtain key metabolic features; inputting the key metabolic features corresponding to the person to be tested into the trained biological age prediction model, first passing through each base model in the base model unit to obtain a number of first ages, forming a first age vector with the first ages and inputting them into each secondary learner in the secondary learner unit respectively to obtain a number of second ages, forming a second age vector with the second ages and inputting them into the final learner to obtain the predicted age. The present invention solves the problems of low accuracy, lack of comprehensiveness, complexity and high cost of existing biological age assessment.

The invention discloses a drug collaborative screening system based on artificial intelligence prediction and metabonomics verification, and belongs to the technical field of artificial intelligence and bioinformatics crossing. Comprising a multi-source data acquisition and feature fusion module, an artificial intelligence prediction module, a drug screening and visualization module, a metabonomics verification module and a feedback optimization and self-learning module. According to the invention, through multi-modal data integration and feature coding, unified modeling of a drug-disease-molecule network is realized; drug combination synergistic effect prediction is realized through an artificial intelligence algorithm; a prediction result is verified by using non-targeted metabonomics data, and the mechanism interpretability of the model is enhanced; a technical system capable of realizing closed-loop optimization is constructed, and an algorithm and experiment dual-verification platform is provided for drug discovery and precision medicine.

The invention provides an application of metabolites in sow vaginal secretion in early pregnancy diagnosis, and finds that three metabolic molecules can be used as markers for the early pregnancy diagnosis of sows for the first time on the basis of non-targeted metabonomics and machine learning conjoint analysis. According to the present invention, the metabolic markers are respectively INDOLEPROPIONIC ACID (indolepropionic acid), cis-Amonitate (cis-Amonitate) and 4-p-Coumaroylquinic acid (4-p-coumaroylquinic acid), the ROC curve AUC values of the metabolic markers are respectively 0.88, 0.92 and 0.77, and the metabolic markers have excellent diagnosis performance during the combination diagnosis. The pregnancy diagnosis is carried out by adopting the vaginal secretions of the sows 18 days after hybridization, the effective pregnancy diagnosis time is advanced by 5-8 days on the basis of a wave diagnosis method, and the method has important significance for reducing cost and increasing ultrasonic efficiency in live pig feeding.

The invention discloses a euphorbia helioscopia seed extract as well as a preparation method and application thereof in preparation of an anti-lung cancer medicine, and belongs to the field of biological medicines. The preparation method comprises the following steps: performing vortex extraction, centrifugation and concentration on a methanol extracting solution to prepare a euphorbia helioscopia seed extract; wide targeted metabonomics research results show that chemical components of the euphorbia helioscopia seed extract and chemical components of the euphorbia helioscopia whole herb extract are remarkably different. Experimental results show that the euphorbia helioscopia seed extract provided by the invention has strong toxicity to lung cancer A549 cells, has a remarkable inhibition effect on EMT generation and invasion and metastasis capacities in the A549 cells induced by TGF-beta1, and can be used for preparing medicines for resisting lung cancer. The invention provides technical support for development of the euphorbia helioscopia seed extract as a novel anti-lung cancer drug, lays a foundation for full utilization of euphorbia helioscopia seed resources, and has a wide application prospect.

The invention relates to the technical field of food identification, in particular to a method for identifying sheep milk and milk based on metabonomics, and aims to solve the industrial pain point that cheap milk is doped into current high-end sheep milk products and is difficult to detect through conventional physicochemical indexes, and the problems that existing protein, DNA and other identification methods are insufficient in sensitivity, complex in operation and the like. According to the invention, a non-targeted metabonomics analysis strategy is innovatively adopted. The saliency difference metabolites between the sheep milk and the milk are systematically screened from the omics level, and rapid, high-sensitivity and high-specificity identification on the authenticity of the sheep milk and whether the sheep milk is mixed with the milk is realized. According to the invention, a key technical basis and a material core are provided for developing corresponding detection products and market supervision methods.

The present invention belongs to the field of bioinformatics, and specifically relates to a mass spectrometry acquisition method, device, equipment and computer program product, the method comprising: performing biological sample processing on a provided biological sample; performing liquid chromatography separation on the treated biological sample; performing mass spectrometry analysis on the biological sample after liquid chromatography separation; after the mass spectrometry analysis is completed, using the AIE-MS automatic exclusion method to identify and exclude interference signals in the mass spectrometry data, and iteratively acquiring secondary mass spectrometry data. The depth of metabolite identification in the prior art is still at a relatively low level compared to other omics. The mass spectrometry acquisition method, device, equipment and computer program product provided by the present invention can significantly increase the number of compound identifications, aiming to solve the core problem of low metabolite identification depth in existing LC-MS non-targeted metabolomics analysis, and has great potential in improving LC-MS metabolomics identification flux and improving data quality.