227 results about "Proteomic Profiling" patented technology

The invention relates to the field of chronic disease early warning, and discloses a chronic disease early warning intervention system based on artificial intelligence, and the system comprises a data collection module which is used for collecting multi-modal data, and the multi-modal data comprises gene detection data, proteomics data, microbiomics data, environment data and social economic data; the pre-processing module is used for pre-processing the multi-modal data to obtain basic data, and the pre-processing comprises noise removal, abnormal value and missing value processing and targeted data cleaning and feature extraction operation on the data of different modals; and a conversion fusion module. Through cooperative operation of all the modules, a complete and automatic chronic disease early warning intervention process is formed, an organic whole is formed from data acquisition, risk assessment and intervention measure formulation and implementation, and the chronic disease risk whole process management and control are achieved, so that the chronic disease prevention and treatment efficiency and effect are improved.

The invention relates to the technical field of image analysis, in particular to a multi-modal data fusion IDH wild type glioblastoma typing method and system, and the method comprises the steps: obtaining a data set of a GBM patient; based on the data set, radiomics characteristics, pathomics characteristics, whole exon sequencing data, transcriptome sequencing data and proteomics characteristics are obtained; performing multi-modal data fusion on the image omics characteristics, the pathological omics characteristics, the whole exon sequencing data, the transcriptome sequencing data and the proteomics characteristics, and constructing a multi-modal fusion typing framework to identify the MOFS subtype; and based on image omics features, constructing a deep neural network classifier for predicting the MOFS subtype, and verifying the identified MOFS subtype. Three different subtypes in IDH wild-type adult glioma are determined and verified using an artificial intelligence method by integrating multi-mode data from MRI derived radiomics, WSI derived pathomics, WES, RNA sequencing, and MS-based proteomics.

The invention relates to the field of breeding technical equipment, and particularly provides protein expression profile screening and application of anxiety disorder of Alzheimer's disease. Specifically, for Alzheimer's disease anxiety disorder caused by amyloid beta protein accumulation, proteomics analysis finds that inhibition of aBLA-vCA1 pathway functions can effectively relieve social avoidance behaviors caused by amyloid beta protein accumulation; and the differential functional proteins of the anxiety disorder of the Alzheimer's disease, such as RPS6, UBQLN1, Eras2, Rpl30, SERBP1, CLU, APOE, C4B, C1QB, C1QA, C1QC, MAPK1, MAPK3, RHOG, PTK2B, ASPA, GAD1, GAD2, H3f3a, H4C8 and TSPYL4, are screened out. The different functional proteins can be used as biomarkers for detecting Alzheimer's disease anxiety disorder caused by accumulation of pulverulent beta protein, and can be used as potential therapeutic targets.

The invention relates to a molecular model established based on proteomics detection serum specimens and used for assisting in evaluating the renal injury progress of diabetic nephropathy as well as a construction method and application of the molecular model, new disease diagnosis markers can be found, and an existing disease diagnosis system can be optimized; according to the technical scheme, a proteomics technology is combined with various medical diagnosis technologies such as experiments and images through research thinking logic of evidence-based medicine and clinical epidemiology so as to diagnose whether a patient suffers from diseases or not and identify different diseases, and the molecular model is formed by combining proteins P23396 + P0CG47 + Q9NYQ8 + P63241, according to the present invention, the protein composition in the sample is detected by using the high-throughput mass spectrometry means, the difference of the diabetic nephropathy disease group and the non-disease group on the proteomics level is compared, the biomarker for the disease progress auxiliary evaluation model construction is screened, and the new scheme is provided for the disease progress auxiliary evaluation of the diabetic nephropathy.

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 preparation method comprises the following steps: by taking alkynol (compound II) as a raw material, reacting with methoxycarbonyl methylene triphenyl phosphorus under the action of an oxidizing agent to obtain a methyl ester compound (compound III), hydrolyzing under the action of alkali to obtain acid (compound IV), finally reacting with alkali, and filtering to obtain the crotonyl acylation detection probe. And freeze-drying to obtain the probe molecule (compound I). According to the present invention, a chemical probe (compound I) having an alkynyl group is applied in a cell, the alkynyl group is selectively loaded on a target protein at a specific modification site through a metabolic marker, and a fluorescent dye can be orthogonally linked to the modification site through a bio-orthogonal chemical reaction, therefore, the probe is used for performing fluorescence imaging on crotonyl protein in cells and identifying crotonyl modified substrates and sites, and the crotonyl sites regulated and controlled by histone deacetylase (HDACs) can be determined by utilizing the probe through quantitative chemical proteomics.

The invention provides a marker combination, a combined reagent and a kit for early risk assessment of caries of young children and application of the marker combination, the combined reagent and the kit in construction of an early risk assessment model of caries of young children, and belongs to the technical field of oral preventive medicine and oral public health. Saliva samples of the young children are researched by adopting proteomics and metabonomics, and 2-oxoadipic acid, enoyl coenzyme A hydratase and kynurenase can be used as a marker combination for evaluating early caries risk of the young children. According to the abundance reference indexes of 2-oxoadipic acid, enoyl coenzyme A hydratase and kynureninase, an early risk assessment model for the caries of the low-age children is constructed. The early risk assessment model for the caries of the young children, which is constructed by the method disclosed by the invention, has relatively good stability, sensitivity and specificity, and lays a solid foundation for taking effective intervention measures to realize comprehensive prevention and control of caries.

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.

The invention belongs to the field of proteomics analysis, and particularly discloses a crop proteome sample pretreatment method based on a solid phase enrichment technology, and the method comprises the following steps: carrying out pyrolysis denaturation and reductive alkylation on protein in a crop tissue sample; carrying out solid-phase enrichment on crop protein by utilizing nano magnetic beads; carrying out protein enzymolysis, and carrying out elution and low-temperature drying on peptide fragments; and performing mass spectrometry on the peptide fragment sample. According to the method, various secondary metabolites such as chlorophyll in the plant tissues can be effectively removed, proteins are effectively purified, interference of impurities on subsequent mass spectrometry is reduced, the identification number and data quality stability of proteomes in the plant tissues rich in pigments are effectively improved, the sample treatment steps are simplified, and the detection efficiency is improved. The method does not need an additional reductive alkylation step and a peptide fragment desalting step, is efficient and rapid, reduces experimental operation errors, and is suitable for an automatic sample pretreatment process.

The invention discloses a novel protein marker for judging muscle injury degree as well as an obtaining method and application of the novel protein marker. The novel protein marker comprises at least one of 36 proteins; the invention discloses a novel protein marker obtaining method. The method specifically comprises the following steps: establishing experimental rat models with different muscle injury degrees, collecting plasma of each group of rats, performing proteomics detection, comparing differences to obtain candidate protein markers, verifying the candidate protein markers by using blood of patients with different clinical muscle injury degrees, and determining a diagnosis reference interval range of the candidate protein markers; according to the application of the proteomic marker, the novel protein marker is used for preparing a kit or detecting and judging the muscle injury degree through a mass spectrum method or any other method. According to the method, a new basis is provided for judging the muscle injury degree, and the muscle injury degree can be quantified conveniently.

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.

The present invention proposes the application of KLHL23 in the treatment of colorectal cancer. The present invention has found that KLHL23 is overexpressed in primary CRC patient specimens and is associated with a poor prognosis, suggesting that the gene may be associated with the malignant phenotype of CRC from a clinical level. Through molecular biological means, interfering with KLHL23 can significantly inhibit the proliferation, cloning and migration of CRC, thereby showing that KLHL23 has an important function in the occurrence and development of colorectal cancer. In a mouse subcutaneous tumor model, silencing KLHL23 can significantly inhibit tumor growth. In addition, the present invention also pioneered the discovery that the key cell proliferation protein CCND1 is regulated by KLHL23 through proteomic sequencing, indicating that KLHL23 can promote the proliferation and growth of tumors, and its expression level can be used as a marker gene for the prognosis of colorectal cancer, which has important clinical significance.

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 relates to the technical field of multi-omics, in particular to a cancer risk assessment system based on multi-omics data, which comprises a proteomics acquisition module, a transcriptomics acquisition module, a genomics acquisition module, a radiomics acquisition module and a central processing module, according to the method, the medical image noise layer simulates the noise according to different irradiation parameters, imaging parameters and imaging technology categories through the medical image noise parameter adjustment dictionary, so that the dynamic convolution layer and the convolution encoder learn how to extract key features of the medical image as much as possible while removing the noise; a multi-modal second-order auto-encoder is constructed through a transfer learning technology to perform multi-modal fusion and feature extraction on multi-omics data of different modals, and finally the multi-modal second-order auto-encoder is transformed into a multi-omics cancer risk assessment neural network model. According to the method, multi-omics, multi-mode and practical cancer risk assessment is realized through a transfer learning technology, a multi-mode fusion technology and a noise simulation technology.

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.

To provide methods of identifying and using glycopeptides as biomarkers for diagnosis and treatment monitoring.SOLUTION: Provided herein are methods of identifying new biomarkers for various diseases using proteomics, peptidomics, metabolics, proteoglycomics, glycomics, mass spectrometry and machine learning. The present disclosure also provides glycopeptides as biomarkers for various diseases such as cancer and autoimmune diseases.SELECTED DRAWING: Figure 1

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 invention provides application of a protein molecular marker in preparation of a product for predicting the effectiveness of neoadjuvant immunotherapy of a lung squamous cell carcinoma patient, and belongs to the technical field of biomedicine. Proteomics is utilized to identify pathologic remission biomarkers of lung squamous cell carcinoma patients and establish a prediction model, 20 protein molecules related to pathologic remission are found, the 20 protein molecules comprise 16 neoadjuvant immunotherapy poor prognosis molecules KRT3, KRT5, KRT6A, KRT6B, KRT6C, KRT16, KRT17, KRT20, KRT75, JUP, MAL2, PKP1, SPRR1A, SPRR1B, SPRR3 and TACSTD2 and 4 treatment good prognosis molecules CD74, HLA-DPA1, ICAM1 and VAV1, the effectiveness of neoadjuvant immunotherapy of the lung squamous cell carcinoma patients can be evaluated, and the pathologic remission of the lung squamous cell carcinoma patients can be evaluated. And the predictive ability of the model may be expanded to immunotherapy of advanced lung cancer patients.

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.