39 results about "Plasma Metabolism" patented technology

This invention belongs to the field of diagnostic testing technology. Addressing the problem that existing gestational diabetes screening methods may miss the optimal time for early intervention, leading to the failure to promptly detect and manage some potentially hyperglycemic pregnant women, increasing the risk of adverse maternal and fetal outcomes, this invention proposes a plasma metabolic biomarker for the early diagnosis or prediction of gestational diabetes and its application. This plasma metabolic biomarker is at least one of L-lysine, propargylglycine, N6-acetyl-L-lysine, cyclic leucine, and glycine-deoxycholic acid. The five plasma metabolic biomarkers provided by this invention can be applied to the early diagnosis or prediction of gestational diabetes, and are of great significance for the prevention or treatment of gestational diabetes.

The application relates to the technical field of targeted drugs, in particular to an organic compound and application of the organic compound in preparation of a PET probe for targeting 18kDa translocation protein and in pharmacy. 18 Or F. Compared with a conventional PET probe, the organic compound has the targeting TSPO characteristic, can be used for preparing the PET probe for targeting TSPO, is stable in in-vivo plasma metabolism, has lower non-specific uptake, and can be used for diagnosis, scientific research and drug preparation of neuroinflammation.

The invention discloses application of a reagent for detecting a plasma metabolism marker combination in preparation of a product for diagnosing breast cancer, and belongs to the field of breast cancer markers. According to the application of the reagent for detecting the plasma metabolism marker combination in preparing the product for diagnosing the breast cancer, the plasma metabolism marker combination comprises dehydroepiandrosterone sulfate, acylcarnitine 12: 0 and taurodeoxycholic acid. The plasma metabolism marker combination has high specificity, high sensitivity and non-invasiveness when being used for breast cancer detection, so that a subject can conveniently and safely receive detection, and the plasma metabolism marker combination is suitable for screening and detection of large-scale common people and is beneficial to early screening, early diagnosis and early treatment of breast cancer.

The invention discloses a biomarker composition for predicting the biological age or aging state of an individual, and the biomarker composition comprises tetracosyl saturated fatty acid / tetracosyl monounsaturated fatty acid phosphatidylcholine (PC 24: 0 / 14: 1), cysteine-glycine-cysteine tripeptide (Cys-Gly-Cys), cysteine-glycine-cysteine tripeptide (Cys-Gly-Cys), cysteine-glycine-cysteine tripeptide (Cys-Gly-Cys), cysteine-glycine-cysteine tripeptide (Cys-Gly-Cys), cysteine-glycine-cysteine tripeptide (Cys-Gly-Cys), cysteine-glycine- The invention relates to a pharmaceutical composition for the preparation of a pharmaceutical composition, which comprises the following components in parts by weight: 20: 0 / 22: 5, 20 / docosapentaenoic acid monohexosyl sphingosine amide (HexCer 20: 0 / 22: 5), trigonelline, lithocholic acid glycine (Lithocholic acid glycine), and octadecanoic saturated fatty acid / octadecanoic monounsaturated fatty acid phosphatidylcholine (PC 18: 0 / 18: 1). The biomarker can be used for accurately characterizing the aging state of an organism, and provides a strategy for early screening, intervention evaluation, accurate health management and the like of related diseases of the elderly.

The present application relates to the field of medical informatics, and provides a paliperidone efficacy prediction method and system based on plasma metabolomics. The method comprises the following steps: collecting basic data of a target patient; performing feature selection on metabolites through the basic data to obtain relevant metabolites corresponding to paliperidone efficacy; training a neural network based on the relevant metabolites and baseline data in the basic data to obtain a prediction model; and performing paliperidone efficacy prediction on a patient to be predicted through the prediction model to obtain a prediction result. The present application can predict the probability of patient response to paliperidone, thereby more accurately evaluating the efficacy of paliperidone before medication, and providing an auxiliary decision for individualized medication.

The invention belongs to the field of marker detection, and particularly relates to a marker for diagnosing or identifying potential causes of migraine, application, a kit, a device and an evaluation model. The invention provides a marker for diagnosing or identifying potential causes of migraine. The marker comprises indoleacrylic acid and / or hypersensitive C-reactive protein. The invention reveals that PFO is combined with migraine metabonomics maps, and branched chain amino acid metabolism, neurosteroid metabolism and tryptophan metabolism disorder are core pathological mechanisms for the first time. On the basis of plasma metabonomics characteristics of a PFO-combined migraine patient and a healthy control, 211 metabolites with significant difference are screened out, it is found that various metabolites are significantly related to the symptom severity of migraine, and two factors [indoleacrylic acid and hypersensitive C-reactive protein (hs-CRP)] significantly related to migraine related to patent foramen ovale are confirmed. In addition, the concentration of indolecrylic acid (IA) is found to have a relatively good prediction value on symptom relief after occlusion.

The invention discloses a combined biomarker for drug-induced liver injury diagnosis, a diagnosis model and application, plasma metabolites with abundance difference are obtained on the basis of plasma metabolite data related to intestinal flora, and the metabolites show excellent differential diagnosis value for distinguishing drug-induced liver injury from healthy people and have good application prospects. A high-specificity and high-accuracy diagnosis model is constructed by combining the plasma metabolite and baseline clinical information, the model can effectively and accurately diagnose the drug-induced liver injury and healthy people, and an auxiliary decision-making tool with conversion application value is provided for objective evaluation of the drug-induced liver injury in clinical practice.

The invention relates to the technical field of biologic detection of metabolomics, in particular to an OSAHS metabolism marker, a diagnosis model and application of the OSAHS metabolism marker and the diagnosis model. The metabolic marker comprises a red blood cell metabolite and / or a plasma metabolite; erythrocyte metabolites comprise L-aspartic acid, D-ribose-5-diphosphoric acid, cadaverine, 12, 13-epoxy octadecenoic acid, L-arginine, N-acetylmethionine, 5-hydroxyeicosatetraenoic acid, 13-oxo-9Z, 9, 10-tetrahydroxy-9Z, 9, 10-tetrahydroxy-9Z, 9, 10-tetrahydroxy-9Z, 9, 10-tetrahydroxy-9Z, 9, 10-tetrahydroxy-9Z, 10-tetrahydroxy-9Z, the component A is one or more of 11E-octadecadienoic acid, inosine monophosphate, nicotinamide, alpha-lactose, sphingosine, hydroxy octadecadienoyl or glyceraldehyde-3-phosphoric acid; the plasma metabolite is prepared from one or more of fumaric acid, cadaverine, L-aspartic acid, malic acid, sphingosine-1-phosphoric acid, L-arginine, cysteine-S-sulfate, thiosulfate, gamma-glutamyl alanine, galacturonic acid or vitamin B2.

The invention relates to the technical field of biological medicines, and provides a plasma metabolite-based ALD non-invasive differential diagnosis model, which is characterized in that five key metabolites in plasma, namely vecuronium bromide, N-(3-amino-3-oxopropyl)-L-valine, geranyl citronellol, phenylpropanolamine and S-allylcysteine, are used as core diagnosis indexes; or by taking alanine aminotransferase as an auxiliary index at the same time, calculating the ALD disease probability through a logistic regression equation. The invention further provides a construction method and application of the model. The method has the advantages that by accurately capturing plasma metabolite differences and combining clinical indexes, efficient identification of healthy people and ALD patients is achieved, and a reliable tool is provided for early screening and clinical diagnosis of ALD.

The invention provides application of a group of plasma metabolism markers in preparation of a reagent product for pulmonary nodule diagnosis, and relates to the technical field of disease diagnosis reagents. The metabolic marker is prepared from trigonelline, 4-methylcatechol, 2, 4-dinitrophenol, 3, 4-dihydroxyphenylacetic acid, N-(2-furfuryl) glycine, mevalonic acid and formononetin, and the metabolic marker is prepared from the following raw materials: the trigonelline, the 4-methylcatechol, the 2, 4-dinitrophenol, the 3, 4-dihydroxyphenylacetic acid, the N-( According to the application, ultra-high performance liquid chromatography-high resolution mass spectrometry (UPLC / MS) is adopted to carry out metabonomics analysis on plasma samples before and after an early pulmonary nodule operation, and a group of plasma metabolites (trigonelline, 4-methylcatechol, 2, 4-dinitrophenol, 3, 4-dihydroxyphenylacetic acid, N-(2-furfuryl) glycine, mevalonic acid and formononetin) are identified; and the group of metabolites is utilized to form a diagnosis model, so that benign and malignant pulmonary nodules can be accurately diagnosed, and early-stage differential diagnosis and risk assessment are facilitated.

The invention relates to the technical field of metabonomics analysis, in particular to application of a metabolic marker in preparation of a head and neck squamous cell carcinoma diagnostic product, a kit, a screening method of the metabolic marker of the head and neck squamous cell carcinoma, a diagnostic model and a construction method and application of the diagnostic model. The metabolic marker comprises at least one of lactic acid, sphingosine, cadaverine, uridine diphosphate, allantoic acid, hydroxyproline, sphingosine-1-phosphoric acid, indole-3-acetaldehyde, pantothenic acid, fumaric acid, malic acid, prostaglandin or ornithine in red blood cells and / or plasma. The screened red blood cells and plasma metabolism markers can be used for predicting or diagnosing the head and neck squamous cell carcinoma respectively or independently, particularly, the red blood cell metabolism markers can provide more stable tumor microenvironment information, and the metabolism stability is high and is not easily influenced by diet and circadian rhythm.

The application discloses a plasma metabolic marker composition and a screening method and application thereof, and relates to the technical field of biomedicine. The plasma metabolic marker composition comprises glycyl-L-glutamic acid, phosphatidylcholine 38:7, pyrrole-2-formic acid and phosphatidylethanolamine 36:2p. The metabolic marker composition is significantly different in different cancer types, can distinguish different cancer types of cancer patients, and realizes the detection of pan-cancer. The metabolic marker composition is non-invasive and minimally invasive when used for the detection of pan-cancer, is more suitable for the screening and detection of a large number of ordinary people, has high sensitivity and specificity, is more suitable for the early screening of cancer, and is helpful for the early screening, early diagnosis and early treatment of cancer, and is helpful for improving the survival rate and cure rate of cancer patients.

The invention relates to the technical field of prediction models, and particularly discloses a pain and rheumatism syndrome biomarker, a diagnosis / prediction model and a construction method thereof. The pain and rheumatism syndrome biomarker comprises at least one of acetyl vanillin, cyclic adenosine monophosphate, methyl vanillate and uridine succinic acid; the pain and rheumatism syndrome biomarker comprises a pain and rheumatism syndrome diagnosis marker and a pain and rheumatism syndrome recurrence prediction marker; the pain and rheumatism syndrome diagnosis marker comprises at least one of acetyl vanillin, cyclic adenosine monophosphate and methyl vanillate; the pain and rheumatism syndrome recurrence prediction marker comprises cyclic adenosine monophosphate and uridine succinic acid. According to the application, a diagnosis model of the pain and rheumatism syndrome can be constructed on the basis of the expression levels of the three plasma metabolites, namely the acetylvanillin, the cyclic adenosine monophosphate and the methyl vanillate; based on the plasma expression level of cyclic adenosine monophosphate and uridine succinic acid, a gout recurrence prediction model can be developed, and high prediction accuracy is achieved.

Chronological age is an important predictor of morbidity and mortality, however it is unable to account for heterogeneity in the decline of physiological function and health with advancing age. Several attempts have been made to instead define a “biological age” using multiple physiological parameters in order to account for variation in the trajectory of human aging; however, these methods require technical expertise and are likely too time-intensive and costly to be implemented into clinical practice. Accordingly, a metabolomic signature of biological aging was developed that can predict changes in physiological function with the convenience of a blood sample. A weighted model of biological age was generated based on multiple clinical and physiological measures in a large group of healthy adults and was then applied to a cohort of healthy older adults who were tracked longitudinally over a 5-10 year timeframe. Plasma metabolomic signatures were identified that were associated with biological age, including some that could predict whether individuals would age at a faster or slower rate. These results not only have clinical implications by providing a simple blood-based assay of biological aging, but also provide insight into the molecular mechanisms underlying human healthspan.

The invention provides a metabolic marker composition for distinguishing health from breast cancer. Based on metabonomics data of blood plasma, a blood plasma metabolism marker combination for breast cancer diagnosis is screened, and a breast cancer diagnosis model with relatively high sensitivity and specificity is constructed in combination with a machine learning algorithm. The method has high sensitivity and specificity, samples are convenient to obtain, non-invasive performance is achieved, accurate screening of the breast cancer can be achieved, important help is provided for prevention of the breast cancer and reduction of the morbidity, and the method is suitable for large-scale population screening and long-term tracking in areas with shortage of medical resources.

The invention discloses a plasma metabolism biomarker combination for predicting and intervening propofol-induced respiratory depression, a detection method and application, and relates to the technical field of biological science. According to the method, non-target metabonomics detection is conducted on preoperative plasma of 298 subjects through UPLC-MS, 14-metabolite panels are obtained through machine learning supervised feature screening, a diagnosis model is established through verification of discovery and verification queues, and the prediction AUC of severe respiratory depression reaches 0.86-0.91 (discovery queue) and 0.86-0.91 (verification queue). Mouse in-vivo function verification proves that the key metabolite alpha-linolenic acid obviously improves the respiratory rate, the tidal volume and the minute ventilation volume under two administration paradigms of pretreatment and treatment of a mouse propofol-RD model, shortens the recovery time of righting reflex, has a good safety foundation, and has a good application prospect. Prompt can be converted into a foresight supplement or rapid treatment medication thought in a perioperative period, and a'prediction and intervention 'integrated clinical value is formed.

The invention relates to the technical field of biology, and particularly discloses application of hydroperoxide octadecadienoic acid in preparation of a medicine for treating or preventing colorectal cancer. A colorectal cancer relapse monitoring queue is established, a colorectal cancer MRD and relapse detection model is established through dynamic plasma metabolome detection, key metabolites closely related to CRC relapse are identified, the identified metabolite 9-HpODE and the effect and mechanism of downstream enzyme GPX3 in CRC malignant progression are verified, and the CRC malignant progression is determined. A new target spot is provided for detection and treatment of CRC recurrence; in addition, the hydrogen peroxide octadecadienoic acid can obviously inhibit the activity of colorectal cancer cells, and the effect of killing the colorectal cancer cells is achieved.

The application discloses a set of biomarkers for diagnosing Parkinson disease with rapid eye movement sleep behavior disorder and application thereof, and belongs to the technical field of biomarkers. The application studies changes in plasma metabolite levels of patients with Parkinson disease with rapid eye movement sleep behavior disorder (PD+RBD) and changes in inflammation-related proteins based on metabolomics and protein analysis methods, finds potential biomarkers, and can be applied to the diagnosis of PD+RBD, solves the current problem of diagnosing Parkinson disease with rapid eye movement sleep behavior disorder in the clinic, helps to establish a scientific treatment plan as early as possible, relieves the pain of patients, and has a very good application prospect.

The invention relates to the technical field of biological medicine, and provides a plasma metabolite-based ALC and AH noninvasive differential diagnosis model, which is characterized in that five plasma key metabolites are taken as core indexes, and a probability calculation model is constructed through a logistic regression algorithm; the five kinds of plasma key metabolites are respectively an Ab-fubinaca metabolite 3, isocitric acid, N-docosahexaenoyl cysteine, gluconic acid-delta-lactone, and 1-palmitoyl-2-linoleyl phosphatidylethanolamine. The invention further provides a construction method and application of the model. According to the method, five key plasma metabolites with remarkable difference between AH and ALC are screened, modeling is carried out in combination with a logistic regression algorithm, or a multi-parameter model is constructed by further integrating prothrombin time, so that accurate non-invasive efficient identification of AH and ALC is realized.

The present application relates to the biomedical technology field, and particularly to a blood biomarker for early coronary heart disease diagnosis, and a screening method and application thereof.The blood biomarker comprises blood clinical indicators and plasma metabolome characteristics, the blood clinical indicators comprise direct bilirubin, aspartate aminotransferase and high-density lipoprotein, and the plasma metabolome characteristics comprise pyroglutamic acid, N-formyl-L-methionine, hypoxanthine, pinobetsin and L-valyl-alanine.The present application not only uses plasma metabolome characteristics, but also incorporates blood clinical indicators through blood routine and blood biochemical detection, compared with other methods, more comprehensively reflects the physiological and pathological state of the body, reduces the possibility of result deviation, and therefore adopts multi-dimensional blood biomarkers, which can more accurately predict the risk of early coronary heart disease.In addition, the adoption of multi-dimensional blood biomarkers can also improve the specificity and sensitivity of screening and diagnosis of early coronary heart disease.

The invention belongs to the field of biological aging, and discloses a metabolic biology age construction method based on plasma metabonomics markers and a related system.The construction method comprises the following steps that S1, data sets including plasma metabonomics indexes, actual age data, death or not and death time are prepared, and a training set and a test set are obtained; s2, in the training set, screening specific plasma metabonomics indexes required for constructing metabolic biology ages to obtain 21 specific plasma metabonomics indexes in total; and S3, by utilizing the training set, taking the actual age and the specific plasma metabonomics index as independent variables, taking the death condition as a dependent variable, and obtaining a relational expression of the metabolic biological age through a Gompertz proportional risk model. On the basis of the plasma metabonomics marker, the metabolic biological age is constructed for predicting the death risk, and the aging condition of the metabolic function of an individual can be accurately reflected.

The invention discloses a plasma metabolism marker combination for distinguishing health from lower limb atherosclerotic obliterans and application of the plasma metabolism marker combination, and belongs to the field of lower limb atherosclerotic obliterans markers. The plasma metabolism marker combination for distinguishing health from lower limb atherosclerotic obliterans comprises dehydroepiandrosterone sulfate, iminodiacetic acid, pyruvic acid and phosphatidylcholine 36: 6. The plasma metabolism marker combination has high specificity and high sensitivity when being used for distinguishing health and lower limb atherosclerotic obliterans, is non-invasive, enables subjects to conveniently and safely receive detection, is suitable for screening and detection of large-scale common people, and has wide application prospects. The kit is beneficial to early screening, early diagnosis and early treatment of the lower limb atherosclerotic obliterans.

The invention provides a metabolic marker composition for distinguishing benign thyroid diseases from early thyroid cancer, and provides an auxiliary diagnosis means independent of invasive biopsy by constructing a diagnosis model based on the metabolic marker composition. The metabolic marker combination can realize functional early warning and diagnosis in an earlier stage of thyroid cancer occurrence, solves the problems of insufficient specificity and high false positive rate in distinguishing benign and malignant thyroid lesions in existing imaging examination, has high sensitivity and specificity, can provide a basis for clinical early intervention, and has a good application prospect. The method is suitable for large-scale population screening and repetitive dynamic monitoring of high-risk individuals.

The invention discloses a biomarker for identifying the severity of drug-induced liver injury, an identification model and application, plasma metabolites with abundance difference are obtained on the basis of plasma metabolite data related to intestinal flora, the metabolites show excellent identification and diagnosis values for severity grading of drug-induced liver injury, and the biomarker can be used for identifying the severity of drug-induced liver injury. A high-specificity and high-accuracy prediction model is constructed by combining plasma metabolites and baseline clinical information, the model can effectively realize accurate layering of the severity of a patient suffering from the drug-induced liver injury, and an auxiliary decision-making tool with transformation application value is provided for objective evaluation of the severity of diseases in clinical practice.

The present invention provides a metabolic aging identification model construction system, storage medium, and kit. Through machine learning of age and plasma metabolite biomarkers, a metabolic aging identification model is generated. The metabolic age calculated from this model demonstrates excellent sensitivity, specificity, and accuracy in predicting all-cause mortality risk, exceeding chronological age itself. Experiments have confirmed its ability to intuitively reflect accelerated metabolic aging through comparison with chronological age. The present invention requires only a small amount of plasma as a test sample, resulting in a simple and convenient evaluation process, low cost, and easy acceptance by subjects, making it suitable for large-scale dissemination. Furthermore, its high sensitivity and specificity make it suitable for screening anti-aging drugs.

The application belongs to the field of chemical drugs, and discloses a protein degrading agent with borneolamine as a hydrophobic group, a preparation method, a pharmaceutical composition and application thereof. The application provides a degradable AR protein degrading agent based on a hydrophobic label function of noradamantane as a hydrophobic group and an enzalutamide parent nucleus as a target protein ligand. The protein degrading agent prepared by the application can effectively induce degradation of AR and AR-V7 in a cancer cell line. Compared with a previously reported AR protein degrading agent based on adamantane as a hydrophobic label, the protein degrading agent has considerable improvement in plasma metabolic stability, can exhibit excellent in-vivo anti-prostate cancer effect in low-frequency drug administration, can be applied to preparation of an AR degrading agent, and can form a pharmaceutical composition, and is suitable for development of a cancer drug such as a prostate cancer drug.

A metabolically unhealthy obesity identification model construction system, a storage medium and a kit. The identification model construction system comprises a collection module, a feature selection module, a training module and an identification module. By means of performing machine learning on plasma metabolite biomarkers, early identification of metabolically unhealthy obese patients can be achieved before clinical symptoms manifest, thereby helping doctors and patients take intervention measures prior to an increase in cardiovascular risk. Compared with conventional physical examination procedures, the metabolically unhealthy obesity identification model construction system is simpler, and reduces the participation burden for patients.

The invention belongs to the technical field of biomedical detection, and particularly relates to a metabolite composition for curative effect prediction and progress risk dynamic monitoring of esophageal squamous cell carcinoma treatment-free combination therapy and application. According to the method, a targeted detection panel containing 1384 metabolites is constructed, and on the basis of plasma samples of 541 patients and an external verification queue, an esophageal squamous carcinoma treatment whole-course large-scale metabonomics dynamic database and a multi-model prediction system are established for the first time; a developed complete cycle scheme comprises a high-precision baseline prediction model (AUC 0.864-0.999) based on 23 markers, a curative effect prediction model (AUC 0.773-0.885) for identifying 25% of long-term benefit people, and a progress risk early warning model based on a sphingolipid and glycerophospholipid yin-yang balance mechanism. The multi-model integration system is verified by an independent queue, and a first non-invasive and accurate plasma metabonomics dynamic monitoring scheme is provided for ESCC individualized treatment.

The invention belongs to the technical field of biomedicine, and particularly relates to a plasma metabolism marker for kidney cancer diagnosis and application of the plasma metabolism marker. The application provides a group of brand new plasma metabolism marker combination, the combination can synergistically and accurately distinguish healthy people and early and late patients with kidney cancer, a new and reliable biomarker is provided for molecular diagnosis of kidney cancer, and by detecting the synergistic change of the 13 metabolites in the plasma, the clinical diagnosis of kidney cancer can be realized. Healthy people, early-stage kidney cancer patients and late-stage kidney cancer patients can be effectively distinguished. Wherein the occurrence and development of the kidney cancer are accompanied by deep reprogramming of a metabolic network of an organism, the metabolites relate to a plurality of key paths such as amino acid metabolism, glycometabolism and lipid metabolism, and the abundance change of the metabolites can sensitively reflect the proliferation state and invasiveness of the tumor and the systematic reaction of the organism to the tumor from the molecular level.

This invention provides a combination of metabolic biomarkers for distinguishing between healthy individuals and gynecological tumors. Based on metabolomics research, this invention analyzes and compares metabolites in the plasma of gynecological tumor patients and healthy individuals to screen for a set of metabolic biomarker combinations with significant differences. Based on the characteristics of plasma metabolites, this invention constructs a diagnostic model with high sensitivity and specificity. This method can be used for risk screening of three types of gynecological tumors: ovarian cancer, cervical cancer, and endometrial cancer. It is non-invasive, easy to obtain samples, and has high subject compliance, making it suitable for large-scale population screening, especially in remote areas with limited medical resources. It also avoids over-testing and reduces testing costs.