23 results about "Metabolite profiling" patented technology

The invention discloses application of a stem cell exosome in treating depression-like behaviors caused by high-altitude hypoxia, and belongs to the technical field of biomedicine. The stem cell exosome is an exosome derived from human umbilical cord mesenchymal stem cells, and is used for drugs for preventing and / or treating depression-like behaviors caused by high-altitude hypoxia. The exosome can effectively improve depression-like core behavior symptoms caused by high-altitude hypoxia by regulating a brain-intestinal axis; the method starts from remodeling intestinal flora so as to influence the intracerebral metabolite spectrum, and finally plays a role in neuroprotection by regulating and controlling a 5-HT6 / cAMP-PKA key signal channel. In addition, the combination of the hUC-MSC-exo and probiotics shows synergistic interaction potential, and a potential treatment strategy which is novel in mechanism, remarkable in effect and high in safety is provided for the field.

The invention discloses a visual traceability processing method and system for food deterioration data, and relates to the technical field of data processing and big data resource services. According to food characteristics, a metabolite spectrum under a time sequence is constructed, a radio frequency tag of a food package is taken as a node, four-dimensional Internet of Things networking is carried out, and by coupling a space microenvironment and the metabolite spectrum, the food deterioration data is obtained. The method comprises the following steps: deploying an intelligent contract rule by taking a metamorphic nonlinear acceleration feature as a constraint, carrying out Internet-of-things network access operation through detection of a space microenvironment and a metabolite spectrum, triggering a coding environment cascade decision based on the intelligent contract rule, determining metamorphic data and carrying out uplink storage, and is used for solving the problem of lack of an integrated unified traceability mechanism in the prior art. The technical problem that full-process automatic identification and information uploading from full-period tracking, deterioration generation to responsibility identification are difficult to realize is solved, the food supply chain management and control level is improved, and the traceability and quality transparency of big data resource service are enhanced.

The invention relates to a urine metabolism combined marker for diagnosing bladder cancer and a screening method of the urine metabolism combined marker. The urine metabolism combined marker is prepared from 2-methyl furan, dimethyl disulfide, octylene and acetophenone. The method for screening the urine metabolism combined marker comprises the following steps: carrying out chromatographic separation and mass spectrometry detection on a urine sample on the basis of a headspace automatic sampler in combination with a chromatographic column and a photochemical ionization time-of-flight mass spectrometry, preprocessing mass spectrometry data, and extracting spectrum peak data of metabolites in urine; the urine metabolism combination marker for diagnosing bladder cancer is obtained through further statistical analysis. The screening method of the urine metabolism combined marker for diagnosing the bladder cancer has the advantages of non-invasiveness, high-throughput detection, simplicity in operation and accurate result, and has a wide application prospect.

A method and system for analyzing and / or estimating a metabolite profile of a subject. A digital image of a sample of feces of the subject is received by one or more processors. The digital image and / or one or more features extracted from the digital image is provided as input to a trained machine learning model which is configured to output a classification based on said input digital image and / or one or more features extracted from the digital image. Data indicative of one or more properties of the metabolome of the subject based on the output image classification is determined by the one or more processors.

The invention relates to the technical field of forest tree hybridization, and discloses a forest tree hybridization parent precise matching method based on multi-omics analysis, which comprises the following steps: S1, obtaining multi-omics data: performing genome sequencing, transcriptome analysis, proteomics analysis and metabonomics analysis on forest tree population individuals; a plurality of omics data such as genetic variation sites, gene expression quantity, protein expression abundance and metabolite spectrums are obtained. According to the forest tree cross parent accurate matching method based on multi-omics analysis, forest tree genetic characteristics are analyzed comprehensively through multi-omics data, genomics, transcriptomics, proteomics and metabonomics data are deeply fused, genetic factors closely associated with target traits are accurately identified, and the accuracy of forest tree cross parent matching is improved. According to the method, the scientificity of parent matching in forest tree cross breeding on the molecular level is remarkably improved, the fuzziness and uncertainty of traditional judgment only according to phenotype and experience are abandoned from the source, the parent matching accuracy is greatly improved, and the breeding work is more targeted and efficient.

This invention provides a method for predicting aging based on metabolite profiles using nonlocal variance enhancement, relating to the fields of medical data analysis and metabolomics. The method acquires LC-MS and / or GC-MS metabolite profile data and the actual age of the subject to be predicted. It performs missing data checks and intra-class normalization on the data, constructs a class-constrained nonlocal variance enhancement feature extraction model, and combines multi-kernel principal component analysis and linear multi-view fusion to obtain fused metabolite profile features. The fused features are then input into a support vector regression model to output predicted age values. The aging degree is determined based on the difference between the predicted and actual age values, which improves the feature expression ability and interpretability of metabolite profile age prediction.

The application discloses a kind of based on metabolic pathway analysis's Iresine heratensis fermentation quality regulation method, it is related to food biotechnology field, including, the sample of different varieties Iresine heratensis is obtained, carries out non-targeted metabolomics analysis, obtains metabolite spectrum data;The metabolite spectrum data carries out difference analysis, constructs key difference metabolite dataset;Metabolic pathway enrichment analysis is carried out to the key difference metabolite dataset, determines core metabolic pathway;Regulate the process parameter in Iresine heratensis fermentation process, obtain the fermentation product after regulation;The quality of the fermentation product after regulation is verified, feedback optimization the process parameter, form closed loop quality regulation method;The application is determined core metabolic pathway by constructing key difference metabolite dataset and enrichment analysis, realizes from traditional experience type fermentation process optimization to the change of accurate regulation based on metabolic mechanism, solves the problem that fermentation quality in prior art is greatly blind, target is not clear.

The present invention pertains to methods for survival prognosis, stratification, and assessment of disease progression or regression in Glioblastoma multiforme (GBM) patients. These methods are based on the detection and analysis of signature patterns of six specific metabolites in emotional tears samples. Additionally, the invention encompasses a preparation technique for emotional tears samples prior to liquid chromatography-mass spectrometry (LC-MS) analysis. This approach enables more accurate and reliable evaluation of GBM patient status and treatment efficacy through the examination of emotional tears metabolite profiles.

The present invention discloses a method and application for predicting and controlling metabolite profiles based on the structure of process microbiota, belonging to the field of biotechnology. The method inoculates microbiota at the beginning and during the process of Daqu fermentation by means of in-situ system microbiota enrichment, effectively changing the microbiota structure and succession pattern, making the fermentation process in deterministic assembly (MST < 0.5). At the same time, this enrichment method ensures the direct colonization and safety of in-situ microorganisms and effectively saves costs. Based on the artificially modified microbiota structure and the metabolites at the end of fermentation, the present invention constructs an elastic net model by training the above data, establishes the relationship between the microbiota data during the fermentation process and the metabolites at the end point through the elastic net model, and realizes good prediction of the metabolites at the end point (Spearman R > 0.3, P < 0.05) only according to the microbiota data during the fermentation process without fermentation environment parameters.

A computer-implemented method for producing a fermented comestible having a target physiological or functional effect is provided. The method involves receiving a target input corresponding to a desired physiological or functional effect, including mood modulation, cognitive focus, or sleep support. A trained machine-learning model identifies a candidate combination including at least one precursor substrate, at least one food-safe microbial strain or microbial consortium, and associated fermentation process parameters. The trained machine-learning model predicts production of two or more metabolites associated with the desired physiological or functional effect at concentrations within a target metabolite profile. The precursor substrate, microbial strain or consortium, and fermentation process parameters are selected based on the model output. The selected precursor substrate is fermented with the selected microbial strain or consortium under the selected fermentation process parameters to produce the fermented comestible having metabolites at concentrations corresponding to the target physiological or functional effect.

A vial adapted for preservation of a metabolite profile of a fecal sample during transport thereof is provided. The vial includes a body including a volume of preservative; and, a cap configured to engage with the body and to seal sample fluid including a mixture of the preservative and fecal sample within the vial during transport by a common carrier. A kit and a system are included.

The application provides a metabolite spectrum library construction method and system and a database, relates to the field of metabolite spectrum library construction, and realizes extraction of standard substance spectrum and establishment of a spectrum library. The overall library construction logic is unified, the route is reliable, the spectrum quality is high, the spectrum can be traced and long-term optimized, and the accuracy of identification can be improved during sample identification.

The invention provides application of parabacteroides dieldrii in preparation of a medicine for treating Parkinson's disease. The parabacteroides dieldrii can play a neuroprotective role through multiple mechanisms of regulating intestinal flora, reducing neuroinflammation, improving a blood-brain barrier function and the like. Animal experiments show that the active parabacteroides dielsii can significantly improve PD mouse dyskinesia, protect dopaminergic neurons, repair intestinal and central barriers, and remodel a metabolite spectrum related to nerve regulation. The intestinal symbiotic bacterium parabacteroides dieldrii provided by the invention has a good effect of treating the Parkinson's disease, and has a wide application prospect in the field of micro-ecological treatment of the Parkinson's disease.

This invention discloses a compound microbial preparation for controlling root-knot nematodes and promoting plant growth, as well as its preparation and application. Addressing the technical problems of existing biocontrol agents, such as lack of target optimization based on root-knot nematodes, unclear metabolite profiles, poor synergistic effects, and unstable insecticidal and growth-promoting properties, this invention screens *Polyspora pinki* and *Bacillus belye* for co-culture, and through inter-bacterial interactions, directionally enriches synergistic metabolites such as daidzein, isopentenyl adenine, and melatonin. When applied to the rhizosphere, this preparation reduces the root-knot index of cucumbers by ≥40% and increases the fresh weight of aboveground parts by ≥25%, significantly superior to single-strain or physical mixture systems. Furthermore, the product is flexible in form, possessing both viable bacterial colonization ability and metabolite stability; its activity retention rate is >80% after 90 days of storage at 35℃, making it suitable for various application methods such as root irrigation and seed coating, providing a highly efficient and stable technical solution for the green control of root-knot nematodes.

The application discloses a method for identifying and evaluating the origin and quality of red-ear sorghum, comprising the following steps: S1, collecting a red-ear sorghum sample to be tested; S2, determining the physicochemical indexes, volatile flavor component spectrum and non-target metabolite spectrum of the sample to obtain multi-dimensional quality data; and S3, comparing and analyzing the multi-dimensional quality data obtained in the step S2 with a standard quality database of red-ear sorghum from different origins to determine the origin and / or quality characteristics of the sample to be tested. It is found that the samples from the core production area in Guizhou are characterized by high tannin content, enrichment of flavonoids and tannin secondary metabolites; the samples from the surrounding production areas in Guizhou are characterized by high starch content, high protein content and active amino acid metabolism; and the samples from the production areas outside Guizhou are characterized by high thousand-grain weight, high ester flavor substances (especially methyl hexanoate) and enrichment of lipid oxidation products.

The present invention relates to a method for detecting from a urine sample whether a subject has or is predisposed to developing metastatic colorectal cancer, comprising obtaining a metabolite profile from said urine sample by measuring the concentration of the metabolites selected from trimethylamine N-oxide (TMAO), cis-aconitate and / or valine, or combinations thereof.

Methods and systems are for identifying the cell type of an unknown microorganism. The device includes: an apparatus for culturing unknown organism(s), a diagnostic data acquisition tool and a computer program. The method includes: incubation of the sample with a growth medium (with or without toxins), and an analysis of the metabolites detected in the sample. The computer system compares the results collected from the device to reference metabolite profiles.

The invention relates to the technical field of biological medicine, in particular to a serum metabolism marker composition for evaluating FTSGA and application of the serum metabolism marker composition. The serum metabolism marker composition is prepared from the following biomarkers: 1-methylpiperidine-2-carboxylic acid, FAHFA (8: 0 / 10: 0), L-arginine, carnitine C13: 1, vitex oil, His-Ser-Lys-Lys, His-Tyr-Glu-Asp and Val-Glu-Phe-Asp, and the serum metabolism marker composition is prepared from the following biomarkers: 1-methylpiperidine-2-carboxylic acid, FAHFA (8: 0 / 10: 0), L-arginine, carnitine C13: 1, vitex oil, His-Ser-Lys-Lys, His-Tyr- According to the invention, a liquid chromatography-mass spectrometry analysis method is adopted to analyze the metabolite spectrum of serum samples of FTSGA pregnant women and normal pregnant woman groups, and the FTSGA-related biomarkers are screened from endogenous differential metabolites by using expression quantity data of the metabolites between the two groups to carry out difference analysis and a recursion elimination method. And a basis is provided for subsequent theoretical research and clinical diagnosis of FTSGA.

The application provides application of glycochenodeoxycholic acid 7-sulfate in preparation of a detection product of diabetic cognitive dysfunction, and belongs to the technical field of biological medicine detection. Based on the metabolomics technology of liquid chromatography-mass spectrometry, metabolite spectrum changes of different populations are analyzed, metabolite relative abundance data are obtained, inter-group difference analysis is carried out, and candidate differential metabolites are screened. It is found that the change of metabolic phenotype is accompanied by the appearance of cognitive dysfunction in type 2 diabetes patients, the level of metabolite glycochenodeoxycholic acid 7-sulfate is significantly increased, and the metabolite glycochenodeoxycholic acid 7-sulfate has good diagnostic value, can be used as a serum metabolite marker of diabetic cognitive dysfunction, and the diagnostic threshold is 25.8 ng / mL, and a matching detection method is established.

The invention provides a metabolite spectrogram library construction method and system and a database, and relates to the field of metabolite spectrogram library construction, the method realizes extraction of a standard substance spectrogram and establishment of a spectrogram library, the overall library construction logic is unified, the route is reliable, the spectrogram quality is high, traceability and long-term optimization are realized, and the method is suitable for large-scale popularization and application. And the identification accuracy can be improved during sample identification.

The invention discloses a red-pulp kiwi fruit variety identification method, which comprises: carrying out sample preparation and metabolite data acquisition on a to-be-detected kiwi fruit sample to obtain metabolite spectrogram data of the to-be-detected kiwi fruit sample; comparing the metabolite spectrogram data of the to-be-detected sample with the metabolic marker combination or the content information of each marker in the metabolic marker combination to obtain relative abundance; and performing attribution judgment on the variety of the to-be-detected sample by constructing a judgment model or comparing the relative abundance of the specific marker in the sample with the relative abundance of the corresponding metabolic marker in the reference sample group. By adopting the technical scheme provided by the invention, the identification of the red-pulp kiwi fruit varieties' Donghong ',' Hongyang 'and' New Zealand small red fruit 'can be realized.

The application discloses a method for differentiating Late-Life Depression (LLD) and amnestic Mild Cognitive Impairment (aMCI) based on plasma metabolomics combined with neural cognitive evaluation. The application detects metabolite spectrum in the plasma of the subject by hydrogen proton magnetic resonance spectrum metabolomics technology, screens out Scyllo-inositol as a key differential marker, and combines with neural cognitive test scores (especially memory scale scores) to construct a joint diagnosis model. The method can effectively distinguish LLD, aMCI and healthy control groups, and has high diagnostic accuracy and specificity. In addition, the method further introduces two-way double-sample Mendelian Randomization (MR) analysis to verify the causal relationship between Scyllo-inositol and the risk of dementia from the genetic point of view, and to strengthen the reliability of Scyllo-inositol as a biomarker. The application provides a diagnosis method with both metabolomics and genetic evidence support for early identification, accurate typing and intervention of high-risk groups of senile dementia, and has important clinical application value.

The invention discloses a metabonomics method and a metabonomics device for detecting serum and kidney tissue of a hyperuricemia renal injury rat based on HPLC-MS. The metabonomics method comprises the following steps: (1) collecting and storing a sample; (2) sample pretreatment; (3) carrying out UHPLC-MS detection; (4) data analysis; according to the method, by optimizing a sample pretreatment process and detection conditions, metabolite spectrums of serum and kidney tissues of the hyperuricemia renal injury rats can be reserved to the greatest extent for subsequent data analysis, the extraction efficiency and detection sensitivity of the metabolite are improved, and meanwhile, by combining an advanced data analysis means, the detection sensitivity of the metabolite is improved. Metabolic characteristics of the hyperuricemia renal injury are comprehensively disclosed, and the method is stable and reproducible, so that the detection result has higher directivity.