173 results about "Proteomic Profiling" patented technology

The invention relates to a network prediction model for identifying a new cancer gene, a model construction method and application, and belongs to the field of biostatistics. According to the method, genomics, transcriptomics and proteomics data are integrated, a dynamic tensor twinning graph neural network is constructed, a hybrid multilayer random block model is utilized to perform tensor decomposition to extract global and local community features, causal and non-causal information is separated, causal feature mutual information is maximized, and non-causal interference is inhibited, so that the dynamic tensor twinning graph neural network is obtained. The model interpretation force is enhanced; through a dynamic community perception algorithm and a twin graph neural network, inter-layer graph dissimilarity is learned by using a graph similarity function, accurate detection of network structure mutation nodes in a high-frequency dynamic scene is realized, and an independent state transition and co-evolution mode is synchronously positioned. And the biological functions of the candidate genes are verified by combining KEGG pathway enrichment analysis and an independent database. Compared with an existing optimal algorithm, the mutation detection accuracy and the calculation efficiency are remarkably improved, and the AUROC and the AUPRC are both higher than those of an existing advanced recognition method.

The invention discloses an application of exosome nano-vesicles derived from artemisia annua cells and peroxidase A0A2U1N9S9 contained in the exosome nano-vesicles in preparation of anti-colorectal cancer drugs. According to the present invention, the anti-CRC activity analysis results show that the artemisia annua cell-derived exosome nano-vesicles can inhibit the growth of DLD-1 and HCT116 cell strains, and the active substance base of ACDENVs for inhibiting the growth of DLD-1 and HCT116 cell strains, namely peroxidase A0A2U1N9S9, is innovatively revealed for the first time based on the difference analysis of the proteomics technology; and a new research thought and a theoretical basis are provided for the research of anti-colorectal cancer drugs.

The invention discloses a radiotherapy sensitivity and toxic and side effect detection system based on multiple omics, and relates to the technical field of radiotherapy, and the system comprises the steps: obtaining genomics data, proteomics data and metabonomics data of a patient at different stages, and generating a patient multi-omics time series data set; based on the patient multi-omics time sequence data set, extracting patient multi-omics time sequence features, and constructing a patient radiotherapy sensitivity dynamic prediction model; combining clinical manifestation and treatment history in clinical data of the patient, generating an adversarial network by utilizing a cGAN condition, generating toxicity simulation data under different radiotherapy doses, and establishing a patient toxic and side effect risk prediction model; and according to the patient radiotherapy sensitivity dynamic prediction model and the toxic and side effect risk prediction model, obtaining an optimal radiotherapy dose interval of the patient, and generating a patient personalized radiotherapy digital twinning scheme. The method has the beneficial effects that the treatment safety and effect of patients are improved, and the method has higher clinical application value and personalized treatment potential.

The invention discloses a group of pyemia specific biomarkers and application thereof, and belongs to the field of medical detection.The biomarkers are formed by combination of alpha 1-microglobulin / Bikunin precursor protein and one or more of antithrombin III protein, apolipoprotein A1 or apolipoprotein H, according to the invention, seven core proteins, namely APOA1, APOH, AMBP, SERPINC1, F2, FGG and FGB, are screened from TMT proteomics of serum of a clinical hemorrhagic patient, and four more important proteins, namely APOA1, APOH, AMBP, SERPINC1, F2, FGG and FGB, are obtained by further screening the seven proteins in a hemorrhagic cell model and the clinical hemorrhagic patient. The four proteins are verified again in serum of a hemorrhagic mouse animal experiment and an expanded-scale clinical hemorrhagic patient, and the potential of the four core proteins as serum biomarkers for early diagnosis of hemorrhagic disease is determined.

The invention discloses a gastric cancer biomarker composition as well as a screening method and application thereof, and belongs to the technical field of bioinformatics. The screening method of the gastric cancer biomarker composition comprises the following steps: firstly, collecting blood specimens of a gastric cancer patient and a healthy control, carrying out high-throughput plasma proteomics detection, and screening out differential expression proteins; and verifying the candidate protein markers by using a prospective queue in the British biological sample library, and finally determining 17 core proteins to obtain the gastric cancer biomarker composition. A gastric cancer risk prediction model is constructed by combining clinical risk factors and protein expression levels of gastric cancer biomarkers. The gastric cancer risk prediction model constructed by the invention can realize accurate layering of gastric cancer risks, decision curve analysis shows that the net benefit of the model is obviously superior to that of a traditional screening method, and an efficient tool is provided for early warning and personalized intervention of gastric cancer.

The disclosed method and system pertain to stratifying a patient population for precision medicine in Inflammatory Bowel Disease (IBD) treatment. The method involves accessing a multi-omic dataset comprising genomic, transcriptomic, and proteomic profiles of patient data. Machine learning algorithms are employed to analyze the dataset and identify biomarkers associated with a response to a drug for treating IBD. The patient population is stratified into phenotypic groups based on the identified biomarkers using unsupervised clustering. A patient population predicted to respond to the drug is defined based on the stratification and further analysis of patient metadata. The system includes a data storage unit and a processor configured to perform the method. The method and system can be used to optimize therapeutic interventions in IBD management.

The present application provides methods and systems for single-run or minimally sequential detection and quantification of proteins, metabolites, and lipids using a unified instrumentation setup spanning a broad dynamic range (˜1 ng / L to 100 mg / L). In some embodiments, a two-phase database architecture transitions newly observed analytes from a discovery repository into a validated repository, enabling reproducible detection (CV<10%) with precise parameters (e.g., retention time, transitions). A machine-learning pipeline enhances automated peak selection by integrating large-scale manual curation with advanced feature extraction. The disclosed platform supports high-throughput multi-omics profiling of plasma or dried blood spot (DBS) samples and enables correlation with clinical factors such as age, BMI, or genetics. These systems maintain high sensitivity, scalability, and reproducibility, addressing long-standing limitations in clinical proteomics. As a result, the disclosed approach facilitates translational research, remote patient monitoring, and global healthcare implementation.

A biomarker diagnostic system includes a sensor to collect a sweat sample from a biological subject; a processor operatively connected to the sensor, wherein the processor is configured to perform metabolic and proteomic profiling of biomarkers in the sweat sample. The metabolic and proteomic profile is compared to a predetermined profile of the biomarkers and to determine a physiological status of the biomarkers. The system further includes a feedback unit operatively coupled to the sensor and the processor and configured to output physiological performance data based on the physiological status.

Affinity-based and activity-based probes (ABPs) described herein offer transformative resolutions to microbiome function. These ABPs target key metabolic pathways in the gut microbiome. The ABPs contain a binding group, a reactive group, and a. reporter group handle that allows for the addition of a "flexible" reporter group that can be easily swapped to enable multimodal fluorescence and proteomic measurements and isolation of live cells. The binding group, also called an affinity element or biorecognition element, mirrors monomeric and polymeric carbohydrates, sulfated and acetylated carbohydrates, and peptides to afford probe selectivity.

The invention relates to the technical field of biological medicines, in particular to application of panthenol-cytochrome c reductase core protein 2 lactic acid modification as a therapeutic target in preparation of drugs for treating sepsis cardiomyopathy. The invention discloses a key effect of Uqcrc2 K109 site lactic acid modification in SIC and application of the Uqcrc2 K109 site lactic acid modification as a therapeutic target. The Uqcrc2-K109 is found to be a therapeutic target of the SIC through molecular biology methods such as proteomics analysis, lactylation omics analysis and co-immunoprecipitation, and the function of a mitochondrial respiratory chain complex III is recovered in a targeted manner by regulating and controlling the lactylation level of the Uqcrc2-K109, so that a new strategy is provided for treatment of the SIC.

Provided herein are methods for diagnosing and treating a pulmonary disease, e.g., bronchopulmonary dysplasia (BPD) using proteomics analysis.

The present application relates to a kind of aerosol delivery system toxicology scoring method and storage medium, method includes: setting target group, the test biological of target group is in aerosol exposure environment;Blood physiological index and multi-omics data of control group are obtained, the test biological of control group is in air exposure environment;Blood physiological index of target group is measured, and first toxicology score is determined according to the difference of blood physiological index of target group and control group;Multi-omics data of target group is measured, and second toxicology score is determined according to the difference of multi-omics data of target group and control group;According to first toxicology score and second toxicology score, determine the comprehensive toxicology score of target group;Blood physiological index includes blood biochemistry, coagulation function and hematology index;Multi-omics data includes transcriptomics, proteomics and metabolomics data.The present application is evaluated from macroscopic physiological level and microscopic molecular level by the above setting, the toxicological effect of aerosol such as electronic cigarette liquid etc. is evaluated comprehensively.

The invention belongs to the field of biological medicine, and particularly relates to a protein marker for lung metastasis of primary liver cancer and application of the protein marker. The invention discloses a protein marker for lung metastasis of primary liver cancer. The protein marker is selected from at least one of rs2 protein, rcsd1 protein, rbbp9 protein, prl6a1 protein, ubxn4 protein and mospd2 protein. According to the invention, mass spectrometry is carried out by using an HPLC-MS / MS mass spectrometry system and an unlabeled quantitative proteomics method; and then searching mass spectrum data by utilizing a non-limited amino acid protein modification analysis method, and verifying whether differential protein substances have significance or not according to P-value, so as to finally obtain six proteins with significant difference, namely, the rs2 protein, the rcsd1 protein, the rbbp9 protein, the prl6a1 protein, the ubxn4 protein and the mospd2 protein. The protein marker provided by the invention has higher sensitivity and specificity, and the combination of the six proteins has better detection efficacy.

Despite the high prevalence and serious clinical implications of metabolic associated steatohepatitis (MASH) in patients with type 2 diabetes (T2D), MASH is usually overlooked in clinical practice, due to the lack of accurate biomarkers. The inventors evaluated the ability of plasma large extracellular vesicles (lEVs) to serve as noninvasive biomarkers for the diagnosis of MASH, in particular in T2D patients. Proteomic analysis identified Transferrinreceptor on lEVs (TFRC-lEVs) as associated with MASH. The inventors thus measured TFRC- lEVs on plasma samples from patients included in the derivation cohort. The proportion of patients with TFRC-lEVs concentration > 61 ng / mL was significantly higher in patients with MASH than in those with steatosis. TFRC-lEVs > 61 ng / mL remained associated with MASH after adjustment on either usual laboratory variables, or on the NASH-Test or on the Fibroscan FAST-score. When combining TFRC-lEVs with available methods the population suspected of having MASH was 32% and 29%, versus 22% and 16% for NASH-test or FAST score alone, without decreasing specificity. In conclusion, TFRC-lEVs, a marker of hepatocyte ballooning, is a promising biomarker for MASH. It could be used for patients' screening to enlarge recruitment of patients with MASH in clinical trials.

The application provides a marker combination for evaluating the degree of red blood cell contamination, the marker combination comprising PRPS1, ADD2, PA2G4, OLA1, ACLY, PIP4K2A, FLOT1, CFL1, ADD1, SNCA, STOM, RAP1B, PPIA, MPP1, EPB42, RAN, CD59, HSPA8, SLC4A1, HBD, EPB41, HBA1, BLVRB, GAPDH, PGK1, SPTB, HBB, SPTA1, CA1 and ANK1. Based on the marker combination, red blood cell contamination samples can be accurately identified and excluded, and biomarkers for evaluating diseases can be accurately screened through proteomics.

This disclosure describes how to treat and / or improve pancreatic cancer using a novel proteomic profile of the pancreatic cancer. In some embodiments, the treatment includes removing cancerous tissue after taking a biopsy from the patient. Optionally, the treatment includes administering anticancer agents to suppress the progression of pancreatic cancer. In further or alternative embodiments, the novel proteomic profile is used to monitor disease progression or remission.

The present application relates to the field of biological medicine, and particularly relates to biomarkers for migraine diagnosis, kits and application thereof. The biomarkers related to migraine disease according to the present application include COL4A2, and also include any one or a combination of multiple of MMP-14, LCAT, ADAMTS13 and CPXM2. The present application is based on proteomic data of clinical healthy controls and migraine patients, and uses bioinformatics analysis and machine learning methods to deeply mine protein combinations with the most early warning value for migraine disease diagnosis, and to verify in a clinical cohort with expanded samples, to provide a good prospect for clinical transformation of newly discovered biomarkers, and also to lay a foundation for subsequent mechanism research.

The present application relates to the technical field of pharmaceutical analysis, and in particular to a combined analysis method for exploring the effect of Huangkui capsules on improving chronic kidney injury of tacrolimus based on metabolomics and proteomics. The combined analysis method of the present application explores how Huangkui capsules improve the mechanism of chronic kidney injury of tacrolimus from multiple dimensions, which is beneficial to in-depth analysis of the specific effects of Huangkui capsules at the molecular, cellular and tissue levels, reveals the key path and regulation mechanism of kidney protection, and can also provide a scientific basis for optimizing clinical application, further promoting the modernization of traditional Chinese medicine and its wide application in the treatment of kidney disease.

The invention provides an application of ginseng oil body in preparation of a medicine for preventing and / or treating colitis, and belongs to the technical field of ginseng seed oil. The detection shows that the oil core structure of the ginseng oil body mainly consists of oleic acid and linoleic acid, and is different from the triglyceride core of the oil body obtained from the existing oil crops. Through lipidomics detection and analysis, the phospholipid monomolecular layer has 335 phospholipids (positive ion mode) and 269 phospholipids (negative ion mode). Through proteomics detection and analysis, 24 kinds of Panax proteins are identified. The condition that the oil core of the ginseng oil body contains protopanaxatriol (PPT) is identified through high performance liquid chromatography-high resolution mass spectrometry. In-vitro detection shows that the ginseng oil body has anti-inflammatory and anti-oxidation effects.

The invention relates to a spraying liquid for increasing the zinc and selenium content of wheat and a preparation method and a spraying method of the spraying liquid, and belongs to the field of optimization of the zinc and selenium content of the wheat. 0.0021% to 0.0024% of a selenium source substance; 0.02% of a surfactant; and the balance of water. By optimizing the concentration of Zn and Se sprayed on leaf surfaces, the antagonism between Zn and Se can be effectively compensated, and safe and synchronous enrichment of Zn and Se in wheat grains is realized; according to the present invention, the daily nutrition requirements of the human body can be met, and the Se content is controlled to be less than the Se toxicity threshold value; the proportion of organic Se is increased, and the related risk of inorganic Se is reduced, so that the food safety is improved; the Zn / Se response protein based on proteomics analysis provides a molecular marker for cultivating a wheat variety with synchronously enriched Zn-Se; the method can be compatible with traditional agricultural practice, extra large equipment or significant cost input is not needed, yield loss is avoided, and a feasible scheme is provided for solving the hidden hunger problem.

The present application relates to a kind of biomarkers related to osteoarthritis, which is Pin1 protein, by detecting whether the expression level of Pin1 protein is abnormal, osteoarthritis can be diagnosed or osteoarthritis prognosis can be predicted, by testing the influence of drug on the expression level of Pin1 protein, osteoarthritis treating drug can be screened or prepared, the quality and efficacy of osteoarthritis treating or relieving drug can be evaluated.The present application uses TMT proteomics technology to complete the screening of biomarkers related to osteoarthritis, and finds the common intersection of 6 proteins in the results of osteoarthritis disease target, TMT proteomics and protein targeting verification through way analysis, wherein Pin1 is consistent with the trend of TMT proteomics and protein targeting verification, i.e.Pin1 is decreased in osteoarthritis.New biomarker related to osteoarthritis-Pin1 protein is found, which has important biological significance and pharmaceutical significance.

The invention discloses a marker combination, a kit thereof and application of the marker combination to prediction of diabetic peripheral neuropathy, the marker combination comprises human angiotensin (ANG), human vascular endothelial cell adhesion molecule 1 (VCAM1) and human mannan binding lectin serine peptide 1 (MASP1), TMT quantitative proteomics and PRM targeted proteomics are adopted, and the marker combination is used for detecting diabetic peripheral neuropathy. A technology combination strategy of an ELISA experiment is adopted, and key differential expression proteins such as MASP1, VCAM1 and ANG in serum exosomes of patients with diabetic peripheral neuropathy are systematically screened and verified. By establishing a multi-model machine learning diagnosis system based on logistic regression, a support vector machine and a naive Bayes algorithm, simple and rapid identification of diabetic peripheral neuropathy patients is realized, and a new experimental basis is provided for clarification of a molecular regulation network of diabetic peripheral neuropathy; more importantly, a rapid screening tool with clinical application potential is developed.

The invention belongs to the technical field of biological medicine, and particularly relates to application of LRRFIP1 serving as a bilberry extract to improvement of a target spot of insulin resistance of gestational diabetes mellitus. The insulin resistance of gestational diabetes mellitus is improved by specifically regulating and controlling a PI3K-AKT signal channel and a GLUT4 glucose transporter function in skeletal muscle based on the bilberry extract. The bilberry extract used in the invention is a natural plant source, is high in safety, and is suitable for pregnancy. PI3K-AKT signal channels can be activated at the same time, and insulin resistance and lipid metabolism disorder can be synergistically improved. The action mechanism is systematically illuminated through technical means such as proteomics, network pharmacology, molecular docking and the like. Glucose tolerance, insulin sensitivity and blood fat level of GDM rats can be remarkably improved, and no remarkable side effect exists.

The invention relates to application of Nogo-B protein in preparation of a medicine serving as an anti-osteosarcoma target spot. The invention discloses an alpha-hydroxyl / alkoxy / amino-beta-amino derivative, a synthesis method thereof and application of Nogo-B protein serving as a new osteosarcoma target, an osteosarcoma diagnostic reagent and a small-molecule inhibitor 4v serving as an osteosarcoma targeting drug. A diazo compound, water / alcohol / amide and imine are used as raw materials, rhodium acetate is used as a catalyst, a product is obtained in an organic solvent through a one-step reaction with high selectivity and high yield, then the small-molecule inhibitor 4v is obtained through osteosarcoma cell surface screening, a new osteosarcoma target Nogo-B is proposed based on a proteomics analysis technology, and the new target Nogo-B is used for inhibiting osteosarcoma. The effects of inhibiting cell proliferation and migration and tumor growth can be achieved through experiments such as knockout of Nogo-B protein. The invention discloses a novel application of Nogo-B protein. The Nogo-B protein is used for preventing, relieving and treating osteosarcoma and osteosarcoma-related complications of individuals in need. Also disclosed are methods of diagnosing, treating, and monitoring osteosarcoma or methods of inhibiting osteosarcoma, and methods of screening for osteosarcoma inhibitors.

The application relates to the technical field of data detection, in particular to an intelligent proteomics data anomaly detection and analysis method, which comprises the following steps: acquiring a plurality of groups of proteomics data; determining discrete error data; determining the discrete type of the proteomics data; respectively determining a detection execution mode according to the discrete type, including adjusting the environmental temperature of a connection line interface of a mass spectrometer according to the Pearson correlation coefficient of the discrete error data of the proteomics data and the corresponding position of a detection voltage fluctuation value on a detection time sequence of the proteomics data, or adjusting a sampling frequency according to the deviation amplitude of a deviation trend; acquiring the peptide segment peak width of proteins detected according to the detection execution mode, and calculating the peak width standard deviation according to the peptide segment peak width; inserting a virtual sampling point at a position in the detection time sequence; and marking real abnormal data based on a re-contrast result. The application realizes the improvement of the data accuracy of anomaly detection.

The invention provides an ultramicro proteomics analysis method, which comprises the following steps: adding multi-channel Carrier samples into a sample to be detected, placing each Carrier sample in different TMT channels, and optimizing the number of channels of the multi-channel Carrier samples and the sample size of each channel, so as to obtain the ultramicro proteomics analysis result of the sample to be detected, wherein the Carrier sample of each channel only contains one single-cell sample, and each Carrier sample is placed in different TMT channels. After protein extraction, Trypsin / Lys-C double enzymolysis and TMT labeling, liquid chromatography-mass spectrometry analysis and detection are carried out, each Carrier sample can also be used as an internal reference, and semi-supervised clustering analysis of ultramicro samples is realized. The method is used for ultramicro proteomics analysis, so that the identification depth can be improved, and the quantitative accuracy of a single cell sample can be ensured.

The present invention discloses an application and a kit for differential histoproteins and / or metabolites of pigmented villonodular synovitis; the differential histoproteins include at least one of the following proteins: TNFSF11, cathepsin K, and adhesion G protein-coupled receptor E5; the differential metabolites include at least one of the following products: 13-hydroxyperoxylinoleic acid and octadecadienoic acid; the present invention provides a new screening marker for pigmented villonodular synovitis (PVNS) through proteomics and metabolomics, which can achieve effective screening for pigmented villonodular synovitis; the obtained screening marker is used in a screening kit, which only needs synovial fluid as a test sample, can quickly perform screening and detection, and has the advantages of high efficiency and simple detection.

The application belongs to the technical field of proteomics, and particularly relates to a one-step method for processing trace tissue samples. The method comprises the following steps: 1, preparing a sample; 2, obtaining a peptide segment: adding a Mix Buffer into an ep tube, non-contact ultrasonic incubation, finally adding an acid solution to terminate enzyme cutting, and centrifuging to obtain a supernatant; the Mix Buffer is composed of rapid trypsin, CAA, TECP, DDM and rapid trypsin buffer. The application combines all steps of lysis-reduction-alkylation-enzyme cutting into one step by optimizing the Mix buffer, the step is simple and the cost is low, the time for sample preparation is greatly saved, and a good proteomics test effect is achieved.

The present invention is applicable to the field of biomedical technologies and provides the use of EHD3 and ATP6V1E1 as markers for predicting the progression of periodontal disease and therapeutic targets. The present invention collects non-invasive and easily obtainable saliva samples from healthy individuals and patients clinically diagnosed with mild, moderate, and severe periodontitis, purifies the exosomes therein, and conducts proteomic sequencing studies. During the research process, the purified exosomes are identified, and then proteomic detection is carried out. By deeply analyzing the proteomic data, the present invention discovers two key proteins, namely EHD3 and ATP6V1E1, whose expression levels continuously increase as periodontitis progresses from mild to moderate to severe. These two proteins can accurately reflect the process of periodontal disease, and thus can be used as predictive and diagnostic biomarkers for the progression of different stages of periodontal disease, and also provide potential targets for the effective treatment of periodontal disease.