57 results about "Serum markers" patented technology

The invention relates to the field of biological detection, in particular to application of IGSF1 protein as a cardiac function recovery marker. The invention provides application of protein as a marker in preparation of a product for detecting and / or diagnosing heart failure with improved ejection fraction. The protein comprises one or more of IGSF1, LIMS1, MPP1, PTX3, SLC2A3, FERMT3, ARPC1B, IGHG1, PTGS1, ACTN1 and RTN4, and is characterized in that the protein comprises one or more of the IGSF1, the LIMS1, the MPP1, the PTX3, the SLC2A3, the FERMT3, the ARPC1B, the IGHG1 The invention provides a novel serum marker, the expression level of the novel serum marker can predict heart failure patients with reduced ejection fraction and heart function recovery potential with high precision, and the change of the novel serum marker is earlier than the change of ejection fraction. According to the invention, a non-invasive standardized detection process based on a serum sample is established, all levels of medical institutions are adapted, and early and non-invasive prediction of the heart function recovery potential of the heart failure patient is realized.

The invention discloses a silent pituitary ACTH cytoma identification method. The method comprises the following steps: determining a pituitary neuroendocrine tumor through imaging examination; a serum marker SAA (serum amyloid protein A) index is obtained through blood detection; constructing a silent pituitary ACTH cytoma recognition model by combining serum marker SAA indexes, age, gender, clinical symptoms and various hormone variable characteristics of pituitary; and identifying and judging the silent pituitary ACTH cytoma according to the identification model. The serum SAA is used as an index for identifying the silent pituitary ACTH cytoma, and the serum SAA is used as a mature serum detection index in clinical application, so that a doctor can accurately identify a high-risk subtype of the silent pituitary ACTH cytoma which is a pituitary neuroendocrine tumor, and can adjust an operation strategy according to factors such as invasiveness and recurrence risk of the tumor; therefore, the operation effect is improved, and patient prognosis is improved.

The invention discloses a colorectal cancer diagnostic marker group based on expiration volatile organic compounds and application of the colorectal cancer diagnostic marker group. The colorectal cancer diagnostic marker group consists of six expiration volatile organic compounds, namely furfural, 2-methyl butane, valeric acid, methyl allyl sulfide, hexane and octanal. According to the method, expiration samples of 182 subjects are analyzed through TD-GC-MS / MS, machine learning is combined, the six expiration volatile organic compounds are screened out, the diagnosis AUC of the combination in a verification queue reaches 0.907, the accuracy rate is 0.806, and the combination is superior to a serum marker combination (AUC = 0.861). The invention further provides the BRS and a colorectal cancer noninvasive diagnosis and risk stratification system, and the BRS and the colorectal cancer noninvasive diagnosis and risk stratification system are used for achieving efficient prediction and risk stratification of the colorectal cancer. Compared with a traditional risk scoring model depending on clinical variables such as age, family history and lifestyle, the BRS provides unique metabonomics information, and the prediction accuracy is remarkably improved.

The invention belongs to the technical field of biology, and particularly relates to a detection method of a tumor serological marker. The application of the reagent for detecting the concentration of the sGRP78 protein in the serum of the breast cancer patient in preparation of a product for evaluating the curative effect of the breast cancer patient after neoadjuvant therapy is characterized in that the number of neoadjuvant therapy is four. In order to overcome the defects of dynamic monitoring deficiency, invasiveness, high cost and insufficient serum marker sensitivity in the existing neoadjuvant therapy metaphase evaluation of the breast cancer, the invention provides a detection kit, namely, the serum marker sGRP78 is monitored in the neoadjuvant therapy metaphase evaluation of the breast cancer, and the sensitivity of the serum marker sGRP78 in the neoadjuvant therapy metaphase evaluation of the breast cancer is improved. Therefore, a noninvasive, low-cost, high-sensitivity and high-specificity neoadjuvant therapy metaphase evaluation index is achieved.

The present invention relates to a set of molecular markers for assessing preoperative perfusion impairment in acute aortic dissection type A, said serum markers being: L-Kynurenine (Kyn), or L-Tryptophan (Try), or the ratio Kyn / Try (KTR).

The application discloses application of a reagent for detecting a biomarker in preparation of a product for predicting ulcerative colitis disease activity or a product for diagnosing ulcerative colitis disease, and relates to the technical field of serological markers of inflammatory bowel disease. The serum CD105 of a UC patient is significantly higher than that of a healthy control group, and CD105 has a very high effect of diagnosing ulcerative colitis disease, and CD105 can distinguish disease activity from remission. Therefore, the application provides a novel serum marker which is convenient to sample, has good repeatability and can objectively reflect the colon mucosa inflammation load, and is expected to provide more accurate marker selection for UC disease activity prediction and UC diagnosis.

This invention discloses the application of the serum biomarker miR-206 in the preparation of Alzheimer's disease diagnostic products. This invention is the first to use the serum biomarker miR-206 in the effective and auxiliary diagnosis of Alzheimer's disease, demonstrating extremely high diagnostic performance. This invention lays the technical and data foundation for the development of stable and reliable Alzheimer's disease point-of-care testing kits or automated chemiluminescence detection systems and other in vitro diagnostic products. It has significant clinical application prospects in promoting the transformation of early screening and auxiliary diagnosis of Alzheimer's disease from reliance on complex imaging and cerebrospinal fluid examinations to convenient, non-invasive blood tests.

This invention discloses a method for constructing a cancer assessment model, as well as a cancer assessment system, media, and electronic device. The assessment model developed in this invention significantly improves the diagnostic efficacy of hepatitis B virus-related liver cancer, especially demonstrating excellent predictive performance in early-stage liver cancer and patients with negative serum biomarkers. Compared with single biomarkers and existing scoring models, the cancer assessment model constructed in this invention has significant advantages in comprehensive indicators such as AUC, sensitivity, and specificity. Although GP73, introduced in the model, has a negative coefficient, its synergistic effect with other biomarkers effectively improves diagnostic performance. The assessment model constructed in this invention provides a reliable tool for non-invasive prediction and early screening of liver cancer, and has significant clinical application value.

The invention discloses a pancreatic cancer diagnosis marker and application thereof, and belongs to the technical field of biological medicines, a serological marker for pancreatic cancer diagnosis is thymosin beta-10, namely Tbeta10, a biomarker for pancreatic cancer diagnosis is a gene TMSB10 for coding the Tbeta10, the gene is 9168 in an NCBI database, a qRT-PCR upstream primer of the TMSB10 is shown as SEQ ID NO.1, a qRT-PCR downstream primer of the TMSB10 is shown as SEQ ID NO.2, the qRT-PCR downstream primer of the TMSB10 is shown as SEQ ID NO.1, the qRT-PCR upstream primer of the TMSB10 is shown as SEQ ID NO.1, and the qRT-PCR downstream primer of the TMSB10 is shown as SEQ ID NO.2. Tbeta10 protein or TMSB10 gene high expression serves as a biomarker for diagnosis and poor prognosis of a pancreatic cancer patient, it is determined that the content of Tbeta10 in serum of the pancreatic cancer patient is remarkably increased, the Tbeta10 protein or TMSB10 gene high expression serves as a serum marker for diagnosis and curative effect and prognosis monitoring of the pancreatic cancer, and the total survival rate of the pancreatic cancer patient is reduced; the TMSB10 is used as a biomarker for poor prognosis of pancreatic cancer patients.

The present application relates to the technical field of medical data fusion processing, and discloses a method and system for predicting adverse pregnancy outcomes of preeclampsia by fusing placental microenvironment image features and serum biomarkers, wherein the method comprises: obtaining digital whole field image of placental tissue section and serum biomarker detection data at the same period; constructing a multi-modal sample data set; extracting multi-scale structure features of placental microenvironment; constructing serum biomarker discriminant features; dynamically fusing placental image features and serum marker features; constructing a prediction model for adverse pregnancy outcomes of preeclampsia and outputting a prediction probability. Compared with the prior art which only relies on single image information or single serum biomarker for risk assessment, it is difficult to realize stable prediction of the coordinated changes of placental microenvironment abnormalities and biochemical abnormalities. Due to the multi-scale image structure analysis, the model generalization ability for predicting adverse pregnancy outcomes of preeclampsia is improved.

The invention relates to the technical field of laboratory medicine, in particular to application of IGSF3 as a serum marker in preparation of medicines for breast cancer diagnosis. The invention discovers that the expression level of serum IGSF3 in a metastatic breast cancer patient is obviously increased for the first time. The IGSF3 can be used as a serum marker for diagnosing breast cancer metastasis, the expression level of the serum IGSF3 in a metastatic breast cancer patient is remarkably improved compared with a healthy control person and a non-metastatic breast cancer patient, and ROC curve analysis shows that AUC is 0.799. The non-invasive auxiliary diagnosis of the metastatic breast cancer is carried out by using a small amount of serum samples, so that large-scale breast cancer screening is facilitated, and early discovery, early intervention and early treatment of the breast cancer are facilitated.

The invention discloses an oral lichen planus molecular subtype based on a serum protein marker. The molecular typing is based on a multi-center clinical queue, a new molecular typing is obtained by using transcriptome sequencing data through a Seed-Kmeans semi-supervised clustering algorithm, and a serum marker is further screened out through multiple omics data including the transcriptome sequencing data, proteome sequencing data and serum enzyme-linked immunosorbent assay experimental data. The method can be used for further judging molecular typing. The molecular typing can obviously improve the curative effect of the oral lichen planus medicine, and is beneficial to the realization of OLP individualized precision medical treatment.

This invention relates to the field of medical diagnostic technology and discloses a non-invasive early DCM analysis method based on multi-radiomics and serum biomarkers. The method involves acquiring image data using multimodal medical imaging equipment and serum biomarker data using blood testing equipment; generating radiomics feature sets and serum biomarker time-series feature sets using multi-scale feature extraction algorithms and temporal analysis models, respectively; fusing these features using a dynamic weighted fusion strategy to generate a joint feature matrix; inputting this matrix into a pre-trained multi-task deep learning model to predict the probability of DCM risk; and finally, optimizing the diagnostic decision tree based on a genetic algorithm to output the early DCM diagnostic result. This method is non-invasive, accurate, and can effectively improve the accuracy of early DCM diagnosis.

The invention relates to a serum marker for predicting glycolipid metabolism and a fat function. The serum marker is FBLN7. As a novel serological marker, the FBLN7 can specifically reflect changes of glycolipid metabolism and fat functions, changes of the serum level of the FBLN7 are closely related to occurrence and development of glycolipid metabolism disorder, and an important basis is provided for early diagnosis of diseases.

The application belongs to the technical field of molecular diagnosis, and particularly relates to a group of extracellular vesicle proteins as biomarkers for diagnosing abdominal aortic aneurysm and application. The application early diagnoses abdominal aortic aneurysm through determination of circulating extracellular vesicle protein markers, and provides an effective tool for diagnosis of abdominal aortic aneurysm. The extracellular vesicle protein marker is any one or two or more molecular marker combinations of interleukin-4 (IL-4), interleukin-6 (IL-6), monocyte chemotactic protein-1 (MCP-1), nerve growth factor (Neurturin) and tumor suppressor M (Oncostatin-M). The application uses proteins in extracellular vesicles as specific markers for early diagnosis of abdominal aortic aneurysm, which is a less applied field in current early diagnosis of AAA. The specific proteins in EVs may be directly related to the pathological process of AAA, and are different from conventional serum markers, and have higher specificity and sensitivity.

To provide a new serum marker whose expression is restricted to the latent form of toxoplasmosis (bradyzoites / cysts). Also provided is an antibody that specifically binds to the new protein.SOLUTION: The present invention relates to a new Toxoplasma gondii protein, hereinafter referred to as BCLA, a new serum marker whose expression is restricted to the latent form of toxoplasmosis. This specific protein and its antigenic fragments can be used to detect autoantibodies in the serum of patients for the diagnosis of latent forms of toxoplasmosis. The invention also relates to derived antibodies generated by BCLA immunization that specifically bind to this new protein.SELECTED DRAWING: None

The present invention provides a biomarker for the detection of glioma microenvironment (TME), comprising a serum marker selected from any one or more of annexin A1 (ANXA1), annexin A2 (ANXA2), matrix metalloproteinase 14 (MMP14) and potential transforming growth factor beta binding protein 2 (LTBP2), and a pathological marker selected from any one or more of Annexin A1 (ANXA1), Annexin A2 (ANXA2), matrix metalloproteinase 14 (MMP14) and potential transforming growth factor beta binding protein 2 (LTBP2); the pathological marker is selected from one or more of periostin (POSTN), lactoferrin (LTF) and a phospholipase A2 group IIA (PLA2G2A), and the pathological marker is selected from one or more of periostin (POSTN), lactoferrin (LTF) and phospholipase A2 group IIA (PLA2G2A). The invention also provides an application of the biomarker in diagnosis of glioma. The invention provides an innovative idea and a biomarker for non-invasive diagnosis of glioma, and can be used for prognosis and malignant diagnosis of glioma.

This invention discloses an early ovarian cancer auxiliary diagnostic system based on multimodal fusion, belonging to the field of early ovarian cancer auxiliary diagnostic technology. It includes a data acquisition module, an image feature extraction module, a non-image feature extraction module, a multimodal fusion module, and a diagnostic output module. The data acquisition module acquires raw ultrasound images, serum biomarker test values, and clinical variable data; the image feature extraction module extracts image depth features; the non-image feature extraction module extracts non-image features; the multimodal fusion module dynamically calculates the attention weights of the two modalities and generates fused features; and the diagnostic output module outputs risk probability values. This invention overcomes the limitations of single-modal diagnosis by dynamically and adaptively fusing multimodal data such as ultrasound images, serum biomarkers, and clinical variables, significantly improving the accuracy of early ovarian cancer diagnosis, while also possessing interpretability and clinical deployment feasibility.

The invention provides a serum marker combination and an analysis system for predicting significant fibrosis of MASLD. Through an advanced targeted metabonomics technology and a machine learning algorithm, a serum metabolite diagnosis model (Inflammation Panel) specially used for diagnosing MASLD related significant fibrosis is developed and verified for the first time. The result analysis shows that the predicted AUC reaches 0.928 (95% CI: 0.835-0.978), the performance is obviously superior to the prediction efficiency of the current non-invasive diagnosis method, and the blank and the defect of the prior art in the aspect of non-invasive diagnosis of significant fibrosis (greater than or equal to F2) of a metabolic dysfunction related fatty liver disease (MASLD) patient are filled.

The present application relates to the field of biotechnology, in particular to the clinical application of SHOX2, PTGER4 methylation and serum marker cyfra21-1 combined detection in the diagnosis of malignant pleural mesothelioma. The present application first found that the combined detection of SHOX2, PTGER4 DNA methylation and serum marker cyfra21-1 can significantly improve the diagnosis rate of malignant pleural mesothelioma, reduce misdiagnosis, improve diagnostic accuracy, and improve patient survival rate.

The invention provides a serum marker combination and an analysis system for predicting significant liver lobular inflammation caused by MASLD. Through an advanced targeted metabonomics technology and a machine learning algorithm, a serum metabolite diagnosis model (Inflammation Panel) specially used for diagnosing MASLD-related significant intralobular inflammation (greater than or equal to I2) is developed and verified for the first time. The result analysis shows that the predicted AUC reaches 0.884 (95% CI: 0.791-0.957), the performance is obviously superior to the prediction efficiency of the traditional serum transaminase (AST / ALT), and the blank and deficiency in the aspect of noninvasive diagnosis of the significant liver lobular inflammation (greater than or equal to the I2 stage) of the metabolic dysfunction related fatty liver disease (MASLD) patient in the prior art are filled.

The invention discloses application of a novel intestinal cancer detection kit of RBM15, and relates to the technical field of biological detection, the application of the RBM15 as the intestinal cancer detection kit in CRC molecular markers is characterized in that 36 pairs of CRC tissues and para-carcinoma samples are detected through qRT-PCR, expression data of RBM15 and corresponding serum tumor clinical indexes in 36 cases of CRC clinical tissues are analyzed through Spearman correlation, and the RBM15 and the expression data of serum tumor clinical indexes in the CRC clinical tissues are analyzed. Based on CRC in a TCGA database and expression data of a normal sample, an ROC curve of RBM15 and other tumor related indexes is drawn, an AUC value is calculated, and it is shown that the diagnosis efficiency is superior to that of part of traditional serum markers. As a novel CRC molecular marker, the RBM15 provided by the invention has remarkable advantages in the aspect of constructing a high-sensitivity and high-specificity detection kit, can be used for early diagnosis and prognosis evaluation, provides a new target for developing a treatment strategy of targeting m6A modification, and has wide clinical application and transformation prospects.

The invention discloses a snake wound classification and recognition model construction method based on serum marker electrochemical detection and deep learning, and belongs to the technical field of biosensing and intelligent diagnosis. The method comprises the following steps: firstly, screening based on proteomics to obtain multiple serum protein biomarkers related to snake bite types, constructing a multi-electrode electrochemical sensing platform, and collecting electrochemical signals through cyclic voltammetry (CV) to obtain electrochemical fingerprint characteristics of a serum sample; and after the obtained signal is subjected to normalization and data enhancement processing, the signal is input into a one-dimensional convolutional neural network (1D-CNN) model for feature learning and classification identification. Through combination of multi-index detection of biomarkers and an end-to-end deep learning algorithm, the method realizes rapid and accurate identification of three different snake wound types of agkistrodon acutus, cobra and trimeresurus remeresurus. The invention constructs a high-sensitivity and rapid diagnosis method for snake wounds, and the method has good popularization and clinical transformation potential.

The invention discloses application of uridine as a serum marker in prognosis of hepatitis B related chronic-acute hepatic failure, the uridine content is an independent predictive factor of 28-day mortality of patients with hepatitis B related chronic-acute hepatic failure, and when the mortality of the patients is increased, the uridine expression quantity is reduced, so that the content of the uridine in the prognosis of hepatitis B related chronic-acute hepatic failure is reduced. Uridine can be used for constructing a hepatitis B related chronic-acute liver failure prognosis prediction model. The serum marker uridine provided by the invention can be used for identifying high-death-risk patients in the early stage of hepatitis B-related chronic-acute hepatic failure, provides important reference for clinical doctors to accurately judge the severity of illness, and can identify the high-death-risk patients in the early stage and take corresponding positive treatment measures.

The invention relates to an S12micro model for cancer auxiliary diagnosis and prognosis evaluation and application of the S12micro model. According to the method, miRNA expression profiles of more than 20000 cases of samples of 8 common data sets are analyzed, 113 combinations of 12 machine learning algorithms are adopted for modeling and verification, 12 key miRNAs are screened out, and an S12micro model is constructed. The average AUC of the model in a large-scale verification queue is up to 0.987 and is obviously superior to that of the existing 42 serum miRNA marker combinations, and the excellent performance of the serum miRNA marker combination is further verified through clinical samples. The S12micro model is based on a serum sample, does not need invasive operation, has the detection cost lower than that of imaging examination, has the advantages of high sensitivity, high specificity and noninvasiveness, is suitable for early screening and auxiliary diagnosis of various cancers, is higher in universality, can effectively fill the blank of accurate diagnosis and prognosis evaluation of the cancers, and has a wide application prospect. And the method has important practical significance, and has extremely high social value and market application prospect.

The invention relates to the field of animal nutrition and metabolism, and discloses a screening method and application of broiler chicken bone abnormality serum marker metabolites, and the screening method comprises the following steps: performing preliminary grouping based on a serum calcium-phosphorus product value, performing multi-dimensional phenotypic verification in combination with bone geometric parameters, bone mineralization indexes and serum bone metabolism markers, constructing a bone abnormality model, and performing screening on the bone abnormality model; differential metabolites related to bone abnormality are screened and determined through non-targeted and targeted metabonomics technologies, classification performance of the markers is evaluated by utilizing a machine learning model, and fumaric acid and malic acid are determined to serve as serum markers for evaluating bone health of broilers through the method. The screening method provided by the invention is systematic and reliable, the obtained biomarker is closely related to an energy metabolism disorder mechanism of skeleton abnormality, the classification performance is high, the biomarker is applied to a feed additive, the skeletal development and mineralization level of broiler chickens can be effectively improved, and the screening method has important application value.

The invention relates to the technical field of gestational diabetes mellitus detection, and discloses a gestational diabetes mellitus serum marker detection data analysis system. The system comprises a serum biochemical data acquisition module, a detection device is used for acquiring a serum sample of a pregnant woman, and a serum biochemical data set containing blood sugar concentration, insulin level and glycosylated hemoglobin indexes is constructed; a multi-dimensional index integration module combines a historical detection time sequence and the physiological parameter information of the current detection time period to generate an integrated data set containing time dimension correlation features; the abnormal risk analysis module processes the time dimension correlation features in the integrated data set, and calculates the risk deviation value of gestational diabetes in the current detection period; when the risk deviation value is abnormal, the risk grading module generates a plurality of candidate intervention classification schemes based on the serum biochemical data set, and outputs the intervention influence degree of each candidate scheme; and an intervention scheme generation module screens the candidate scheme with the lowest intervention influence degree and generates an optimal intervention classification scheme in the current detection time period.

The present invention provides liver cancer-associated serum microRNA markers and a new method for diagnosing liver cancer, wherein the liver cancer-associated serum microRNA markers consist of hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27 and hsa-miR-4499; relative expression level of the liver cancer-associated serum microRNA markers in serum is tested using Real-time PCR method, and reagents for the testing contains primers of the liver cancer-associated serum microRNA markers. The present invention can be used to diagnose hepatocellular carcinoma, in particular early hepatocellular carcinoma or to distinguish the serum of at least one patient with hepatocellular carcinoma from the serum of at least one healthy individual, the serum of at least one patient with chronic hepatitis B or the serum of at least one patient with liver cirrhosis.

The invention belongs to the technical field of medicine, and discloses an application method of CLIC3 as a serological molecular marker in prediction of lung cancer bone metastasis, CLIC3 is significantly increased in serum of patients with lung cancer bone metastasis, and the total lifetime and non-metastasis lifetime of lung cancer patients with high expression of CLIC3 are significantly shorter than those of patients with low expression. In addition, bone metastasis in a mouse model constructed by CLIC3 high-expression lung cancer cells is remarkably enhanced. Based on detection and analysis of clinical specimens, it is successfully identified that CLIC3 can serve as a brand new serum marker and has a key indication effect in the aspect of early warning of lung cancer bone metastasis. In the research process, multi-dimensional verification is carried out by comprehensively applying a bioinformatics technology, molecular biology detection, a mouse animal model and other methods, and the serum CLIC3 has outstanding potential early warning efficiency and is expected to serve as a novel biological marker with innovativeness, scientificity and clinical application prospects in the early warning category of lung cancer bone metastasis.

The invention belongs to the technical field of disease prognosis and molecular biology, and relates to a flora marker for colorectal cancer liver metastasis diagnosis and liver metastasis risk assessment and application of the flora marker. The marker comprises the following three strains: bifidobacterium bifidum, bacteroides fragilis and bacteroides heparinidolyticus. The method comprises the following steps: acquiring information of microorganisms in excrement of a patient through a high-resolution metagenomics detection technology; a random forest algorithm is utilized to construct a colorectal cancer liver metastasis classification model based on the flora markers, the model can distinguish people without colorectal cancer metastasis and people with liver metastasis and accurately predict high-risk patients with colorectal cancer liver metastasis, and the effect of the model is superior to that of a clinical conventional tumor serum marker. The kit has the advantages of noninvasive detection and high accuracy, and can provide a reliable tool for liver metastasis diagnosis and individualized prognosis management of colorectal cancer patients.