125 results about "Metabolomics" patented technology

Methods systems and computer readable media for manipulating and displaying sparse data contained within very large datasets. Particular applications includes those to proteomics and metabolomics. Navigation of the large data sets from a global perspective may be practically implemented, so that a user is not confined to a narrow slice of data at a time, thus providing context of the whole dataset while viewing data within the dataset.

The small molecule profiles of cells are compared to identify small molecules which are modulated in altered states. Cellular small molecule libraries, methods of identifying tissue sources, methods for treating genetic and non-genetic diseases, and methods for predicting the efficacy of drugs are also discussed.

The small molecule profiles of cells are compared to identify small molecules which are modulated in altered states. Cellular small molecule libraries, methods of identifying tissue sources, methods for treating genetic and non-genetic diseases, and methods for predicting the efficacy of drugs are also discussed.

The present invention provides a novel NMR metabolomics method for observing an animal by labeling the animal itself. A labeled animal labeled with a stable isotope is produced by feeding an animal a labeled food labeled with the stable isotope. Metabolism of a biological substance in the animal is analyzed based on nuclear magnetic resonance data of the biological substance containing the stable isotope. The nuclear magnetic resonance data is acquired by performing NMR measurement on a body of the labeled animal, a portion of the body, or an extract.

The present invention provides diacylhydrazine ligands and chiral diacylhydrazine ligands for use with ecdysone receptor-based inducible gene expression systems. Thus, the present invention is useful for applications such as gene therapy, large scale production of proteins and antibodies, cell-based screening assays, functional genomics, proteomics, metabolomics, and regulation of traits in transgenic organisms, where control of gene expression levels is desirable. An advantage of the present invention is that it provides a means to regulate gene expression and to tailor expression levels to suit the user's requirements.

The present invention relates to a reliable and statistically significant method for predicting the likelihood of an onset of an inflammation associated organ failure from a biological sample of a mammalian subject in vitro, by means of a subject's quantitative metabolomics profile comprising a plurality of endogenous metabolites, and comparing it with a quantitative reference metabolomics profile of a plurality of endogenous organ failure predictive target metabolites in order to predict whether the subject is likely or unlikely to develop an organ failure. Furthermore, the invention relates to the usefulness of endogenous organ failure predictive target metabolites in such a method.

Acetyl-L-carnitine (ALCAR) and L-carntine (LCAR) are nutraceuticals with indications in treating a variety of mental health disorders. A metabolomics-guided bioprocess method is presented to enhance ALCAR and LCAR formation in germinating plant seeds. Metabolic fluxes are manipulated by germination in bioreactors to increase oxygen availability as well as provide an aseptic environment to alter carbohydrate consumption and feedback repress gluconeogenesis. Large shifts in sunflower seed fatty acid, phospholipid and high-energy metabolism change the germination environment and these metabolic changes lead to an approximate 1000-fold increase in natural LCAR and ALCAR production by the seeds. The resulting LCAR and ALCAR products from the seeds are used for treating mental health disorders including Alzheimer's disease, geriatric depression, non-geriatric depression and schizophrenia.

The disclosed methods address the identification of myocardial ischemia and myocardial infarction using metabolomics, as well as the identification of metabolic products whose differential expression over time is indicative of myocardial ischemia and/or myocardial infarction.

The present disclosure provides boron-containing diacylhydrazines having Formula I:

Relative quantitative information about components of chemical or biological samples can be obtained from mass spectra by normalizing the spectra to yield peak intensity values that accurately reflect concentrations of the responsible species. A normalization factor is computed from peak intensities of those inherent components whose concentration remains constant across a series of samples. Relative concentrations of a component occurring in different samples can be estimated from the normalized peak intensities. Unlike conventional methods, internal standards or additional reagents are not required. The methods are particularly useful for differential phenotyping in proteomics and metabolomics research, in which molecules varying in concentration across samples are identified. These identified species may serve as biological markers for disease or response to therapy.

A shotgun metabolomics approach using MALDI-tandem mass spectrometry was developed for the rapid analysis of cellular metabolites. Through the use of neutral organic solvents to inactivate endogenous enzyme activities (i.e., methanol/chloroform/H₂O extraction), multiplexed extraction conditions and combinatorial alterations in matrix stereoelectronic composition and analyte interactions, multiple suites of metabolites were directly ionized and quantitated directly from biologic extracts without the need for prior chromatographic separation. Through combinatorial alterations in 9-aminoacridine charge, aromaticity and stacking, a set of multiplexed conditions was developed that allowed identification of many hundreds of peaks corresponding to metabolites from mouse heart extracts. Identification of metabolite peaks was based on mass accuracy and isomeric species were assigned based on diagnostic fragment ions present during tandem mass spectrometry for many of the identified metabolite peaks.

The invention discloses a method for analyzing serum metabolomics on the basis of an LC-MS (liquid chromatogram-mass spectrograph) serum metabolomics technology. According to the method, serum is analyzed by constructing a serum metabolomics analysis model, and a model construction method comprises the following steps: collecting a healthy serum sample and an infected serum sample; performing LC-MS detection on the samples to obtain an original metabolic fingerprint; and preprocessing the fingerprint, sequentially performing PCA (principal component analysis) and PLS-DA (partial least squares-discriminant analysis) on an obtained two-dimensional matrix to obtain a PLS-DA model, verifying the obtained model, and if the model does not have over-fitting risks, finishing the model construction. By adopting the method disclosed by the invention, a serum metabolomics analytic technology is applied to early screening of esophageal cancer for the first time, high risk populations of the esophageal cancer can be quickly and conveniently screened out, the screening range of the esophageal cancer is reduced, the efficiency of screening the whole population of a high incidence area of the esophageal cancer is effectively improved, the screening cost is greatly reduced, and the pain caused by invasive endoscopy to part of the population is effectively avoided, so that the method has important economic and social benefits and is convenient to popularize and apply.

The invention discloses a pancreatic ductal adenocarcinoma marker and a screening method thereof, which belongs to the field of clinical test and diagnosis. In view of the problem that the detection sensitivity and the detection specificity of the existing pancreatic ductal adenocarcinoma diagnosis marker are poor, serum of an early patient of early stage pancreatic duct adenocarcinoma is subjected to a trace metabolomics analysis by the high-performance liquid chromatography-tandem mass spectrometry technology, and different metabolites between normal human and the early stage pancreatic ductal adenocarcinoma patients are found. The different metabolites between normal human and pancreatic ductal adenocarcinoma patients are further analyzed by this technique to find the specific differentmetabolites C10:1 acyl carnitine and lysophosphatidyl choline LysoPC (14:0) of pancreatic ductal adenocarcinoma patients caused by cancer, i.e., the diagnostic molecules of the pancreatic ductal adenocarcinoma. According to the pancreatic ductal adenocarcinoma marker and the screening method thereof, the method can assist CA19-9 in diagnosing pancreatic duct adenocarcinoma patients, and can improve the diagnosis rate for the CA19-9 negative patients by 85%. The method is suitable for the screening of tumor markers.

A method of identifying a patient in need of therapy to treat a malignant glioma comprising measuring a panel of polar metabolite levels in a biological sample taken from the patient and implementing a therapy to treat the malignant glioma in the patient.

The invention belongs to the technical field of model establishment and evaluation methods with an aim to solve problems about low flexibility and poor accuracy in an existing model establishment andevaluation method for the nephritic syndrome rats and provides a model establishment and evaluation method for the nephritic syndrome rats. Changes of endogenous metabolites in the urine of terminal products are analyzed by metabolomics, all 1 HNMR spectra are processed through MestReNova software to acquire integral data, statistical analysis of the content of 12 biomarkers is combined with, thechanges of the mean value of the 12 biomarkers in the urine before and after modeling reflect the change trend of the urine metabolite content in the nephritic syndrome rats, and models of nephritic syndrome are evaluated in a targeted manner. Dynamic body contours before and after modeling is reflected comprehensively, flexibly and systematically, rationality and scientific nature of model reproduction are comprehensively reflected, the reliable evaluation method for new drug development and pharmacological research is provided, and the method is efficient, rapid, noninvasive and high in specificity.

A method of obtaining pure component mass spectra or pure peak elution profiles from mass spectra of a mixture of components involves estimating number of components in the mixture, filtering noise, and extracting individual component mass spectra or pure peak elution profiles using blind entropy minimization with direct optimization (e.g. downhill simplex minimization). The method may be applied to deconvolution of pure GC/MS spectra of overlapping or partially overlapping isotopologues or other compounds, separation of overlapping or partially overlapping compounds in proteomics or metabolomics mass spectrometry applications, peptide sequencing using high voltage fragmentation followed by deconvolution of the obtained mixture mass spectra, deconvolution of MALDI mass spectra in the separation of multiple components present in a single solution, and specific compound monitoring in security and/or environmentally sensitive areas.

The invention discloses streptococcus thermophilus 210202 capable of high yielding of gamma-aminobutyric acids and application thereof. The streptococcus thermophilus 210202 is preserved in the ChinaGeneral Microbiological Culture Collection Center (CGMCC) on October 15, 2018, and the preservation number is CGMCC No.16585. The streptococcus thermophilus strain 210202 capable of metabolism in cowmilk and high yielding of GABAs can be screened out from natural fermented food, and the streptococcus thermophilus strain 210202 is identified as a streptococcus thermophilus strain through the identification in morphology, metabolomics and genetics. The bacterial strain is capable of high yielding of the GABAs in a fermenting system containing sodium glutamate and dried skim milk reconstituted milk, and the content of the GABAs is 0.3273 g/L. The streptococcus thermophilus 210202 provides a new way for preparing a yoghurt starter or milk products rich in the gamma-aminobutyric acids, and hasgreat significance for industrial production of yogurt products with high-content GABAs.

A variational autoencoder (VAE) has been developed to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. The VAE has been extended to include a chemical property decoder, trained as a multitask network, to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, focused on properties that are obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involves a cascade of transfer learning iterations. First, molecular representation is learned from a large dataset of structures with m/z labels. Next, in silico property values are used to continue training. Finally, the network is further refined by being trained with the experimental data. The trained network is used to predict chemical properties directly from structure and generate candidate structures with desired chemical properties. The network is extensible to other training data and molecular representations, and for use with other analytical platforms, for both chemical property and feature prediction as well as molecular structure generation.