56 results about "Serum biomarkers" patented technology

The invention discloses an SERS (Surface Enhanced Raman Scattering) sensor for detecting diabetic retinopathy serum biomarkers miR-21 and miR-152 as well as a preparation method and application of the SERS sensor, and belongs to the technical field of biomedicine. The preparation method comprises the following steps: self-assembling AuNHs through an oil-water interface to obtain an AuNHs array, and modifying an aptamer chain cDNA on the surface of the array to obtain an SERS (Surface Enhanced Raman Scattering) substrate; the method comprises the following steps: modifying a Raman signal molecule 4-MBA and a nucleic acid aptamer complementary chain H1 to the surface of AuNTs to obtain an SERS probe 1; a Raman signal molecule DTNB and a nucleic acid aptamer complementary chain H2 are modified to the surface of AuNTs, and an SERS probe 2 is obtained; and combining the prepared SERS probe with a complementary basic group in the nucleic acid aptamer chain cDNA on the SERS substrate, and fixing on the AuNHs array to construct the SERS sensor.

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 relates to the technical field of medical systems, and particularly discloses a method for constructing a cancer malignant fluid prognosis composite index CCAR based on a creatinine and CAR combination, and the method comprises the following steps: S1, obtaining serum biomarker data of a cancer malignant fluid patient, including creatinine, C-reactive protein and albumin; s2, calculating the ratio CAR of the C reactive protein to the albumin, wherein CAR = CRP / Albumin; s3, taking the CAR and the Cr as input features, and constructing a nonlinear composite index CCAR through a random forest machine learning model; s4, developing a network calculator system; the CCAR index constructed by the invention can comprehensively reflect the inflammation burden, the metabolic state and the immune level of the cancer and malignant fluid patients, and compared with the traditional index, the CCAR index can provide more accurate survival prognosis prediction; creatinine, the ratio of C-reactive protein to albumin and other parameters contained in the CCAR index are all clinical common blood examination items, the obtaining approach is simple and convenient, and wide clinical application is facilitated.

The invention belongs to the technical field of animal model analysis, and particularly relates to a modeling method of a gout damp-heat syndrome subtype model, application of a biomarker group and a detection method. According to the invention, two subtype models of pain, rheumatism and severer heat and severer heat are constructed, a UPLC-MS (Ultra Performance Liquid Chromatography-Mass Spectrometry) detection method of serum biomarkers is established, and a biomarker group comprising lauroyl carnitine, taurocholic acid, palmitoyl-L-carnitine, L-leucine, leukotriene A4, 13-Oxo-9 and 11-tridecioic acid is obtained. Researches show that the biomarker group can simply and effectively distinguish models of pain and rheumatism heavier than heat and models of heat heavier than wet and judge whether modeling of two subtypes succeeds or not, so that the biomarker group can replace conventional characterization and biochemical pathology evaluation methods, and the objectivity and operability of judgment are improved.

The application discloses application of a product for detecting metabolite levels in serum in preparation of a preparation for diagnosing systemic lupus erythematosus (SLE) and SLE patients with different organ involvement phenotypes and a reagent. The application first uses specific serum biomarkers to assist in screening SLE, SLE with different disease activity and SLE patients with different organ phenotypes, including only kidney involvement systemic lupus erythematosus (KI), only skin involvement systemic lupus erythematosus (SI), only blood system involvement systemic lupus erythematosus (BI) and multi-system involvement systemic lupus erythematosus (MI), thereby opening up a new way for the diagnosis of the field and helping to more accurately diagnose and evaluate the disease progression of SLE. The blood specimen required is small, easy to carry out and has a good application prospect.

The invention discloses application of an FKBP9 detection agent in preparation of products for liver cancer diagnosis, auxiliary diagnosis, screening, auxiliary screening, prognosis evaluation, risk assessment and / or risk stratification. The content of the FKBP9 in blood of a healthy control person and a patient with advanced liver cancer is remarkably different, and the FKBP9 has high sensitivity and specificity and can be used as a biomarker for diagnosing liver cancer. The content of the serum FKBP9 in a healthy control person and a patient with the advanced liver cancer is detected by adopting an enzyme-linked immunosorbent assay, and after a large number of clinical samples are subjected to immunoassay, it is determined that the content of the serum FKBP9 in the healthy control person and the content of the serum FKBP9 in the patient with the advanced liver cancer have significant difference; and ROC curve analysis is adopted to further verify that the serum FKBP9 can be used as a serum biomarker for diagnosing liver cancer. Meanwhile, it is also verified that FKBP9 in blood can be used as a biomarker for prognosis evaluation of liver cancer.

A method for assessing a suicidal severity in a depressed patient according to an embodiment includes measuring a concentration of a suicidal behavior prediction biomarker contained in a biological sample of the depressed patient, wherein the suicidal behavior prediction biomarker is one or more markers selected from the group consisting of cortisol, interleukin-1 beta (IL-1β), homocysteine, total cholesterol, and folate, and determining a probability of an increased suicidal severity by comparing the measured concentration of the suicidal behavior prediction biomarker with a preset cutoff level thereof.

A method for detecting risk biomarkers for chronic neurodegenerative disorder of central nervous system called Alzheimer's disease (AD) is provided. AD is the most common cause of dementia, and it is characterized by extracellular senile plaques formed by the accumulation of amyloid β (Aβ) proteins and aggregation of tau proteins into intracellular neurofibrillary tangles in central nervous system. The present invention aims at developing a novel PoC based multiple AD biomarkers detection assay and method, such as Aβ1-40 / Aβ1-42, tau protein and BDNF using multi-colored highly luminescent, non-photo-bleaching quantum dots (QDs), which provides an affordable and accurate means for early diagnosis of MCI, AD or dementia at homes, hospitals or near the patient bedsides.

The application relates to application of phosphatidylinositol and phosphatidylglycerol as metabolic markers for predicting intracranial atherosclerosis progression, and belongs to the technical field of medical biological detection. In the scheme, single-sample MR analysis is carried out by combining RICAS cohort data with external database data, causal correlation between metabolites and ICAS is analyzed, and serum biomarkers phosphatidylinositol PI 18:0 / 22:6 and phosphatidylglycerol PG 18:1 / 18:2 for ICAS occurrence and development are screened out by combining characteristic metabolic changes of ICAS with causal correlation analysis. ICAS progression is collected through prospective follow-up of the RICAS cohort, the correlation between the serum biomarkers and the ICAS progression is established, and it is verified that the serum biomarkers have transformation application value and can be used as a new biomarker for early prediction of ICAS progression and derivation.

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 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 present invention relates to a method for detecting pathological bone activity in an equine subject using a serum biomarker, and use of the serum biomarker for diagnosing and / or preventing risk of equine bone fracture and / or lameness, particularly in race horses.

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 discloses application of Mindin as an early-stage biomarker in prediction or diagnosis of acute kidney injury, belongs to the technical field of biology, and relates to a method for early diagnosis of AKI (acute kidney injury), the early-stage serum biomarker of acute kidney injury is determined by analyzing the concentration of Mindin protein in serum of a normal person and a patient with acute kidney injury. The relationship between serum Mindin and acute kidney injury is analyzed by using an independence test, the occurrence of AKI (acute kidney injury) is predicted in an early stage, the purpose of preliminarily screening patients is achieved, and a kit with high specificity is applied to patients with increased screening for further definite diagnosis. The application method of the Mindin as the early biomarker in predicting or diagnosing the acute kidney injury has important significance in early diagnosis and prognosis of the acute kidney injury.

The present invention relates to methods and compositions for enabling the diagnosis and treatment of chronic kidney disease in felines. In one embodiment, a method for diagnosing chronic kidney disease (CKD) in a feline may include measuring a normalized relative abundance or absolute quantification of a first serum biomarker and determining whether the feline has CKD, early CKD, or late CKD based on the normalized relative abundance or absolute quantification.

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 present invention relates to a novel blood biomarker SH3PXD2B for diagnosing hepatitis B virus (HBV)-associated hepatocellular carcinoma and a use thereof. In order to identify a novel serum biomarker, transcriptome profiling including buffy coat WTS data was used to distinguish blood biomarkers specific to the caused of hepatocellular carcinoma Confirmation was made through validation using publicly available large-scale RNA-seq data, and qRT-PCR and AUC analyses were performed. Through this, it was confirmed that SH3PXD2B was upregulated exclusively in hepatitis B virus hepatocellular carcinoma in buffy coat transcripts, and qRT-PCR data demonstrated significant upregulation of SH3PXD2B in HBV-HCC. Therefore, SH3PXD2B is expected to be advantageously utilized as a blood biomarker for hepatitis B virus-associated hepatocellular carcinoma.

The present invention discloses serum metabolic markers and their application in evaluating the stability of intracranial artery plaques, belonging to the field of biomedical technology. The serum biomarkers are selected from one or a combination of phosphatidylcholine PC(15:0 / 15:1), phosphatidylcholine PC(14:0 / 18:2), phosphatidylethanol PE(18:2 / 18:3), phosphatidylethanol PE 18:1e / 18:1, 5α-dihydrotestosterone, D-fructose 6-phosphate, phosphatidylcholine PC(14:0 / 18:2), sphingomyelin SM(d28:0 / 14:1), phosphatidylcholine PC(20:3 / 22:6), and phosphatidylcholine PC(20:3 / 20:4), providing a new direction for the treatment or intervention of atherosclerosis.

The invention discloses a gastrointestinal tumor patient postoperative weakness risk correlation analysis method and system fused with a co-disease map, and belongs to the field of medical care informatics, and the method comprises the following steps: obtaining clinical pathological characteristics, co-disease assessment, serum biomarkers and social and psychological assessment scale data of gastrointestinal tumor patients; obtaining statistical effect values of homocysteine and health attainment, and executing isomorphic mapping; identifying a chained intermediary conduction path and generating a coupling strength factor, and performing weighted correction on the initialized weight matrix; inputting the multi-source data as a risk source excitation signal into the atlas, executing correction calculation, and outputting a risk prediction score; a maximum weight subgraph search is performed to generate risk attribution analysis data. According to the method, the multi-dimensional heterogeneous association map is constructed to fuse the multi-source data of the patient and the medical priori knowledge, and the risk conduction is quantified through the path topology analysis and the matrix correction operation, so that the accurate prediction of the postoperative weakness risk and the interpretable attribution of the key pathogenic path can be realized.

The present invention relates to the field of biomedical technology, and in particular to a SERS sensor for detecting serum biomarkers related to colorectal cancer and a preparation method thereof. The SERS sensor of the present invention is composed of a double-stranded nucleic acid structure formed by a hairpin structure DNA specifically binding to a relevant serum biomarker and a single-stranded DNA partially complementary to the hairpin structure DNA, which is modified on the surface of gold-coated ferroferric oxide magnetic nanoparticles with Raman signal molecules, and a single-stranded DNA partially complementary to the hairpin structure DNA. ‑1 and 1346cm ‑1 By measuring the position and intensity of the characteristic peaks, rapid, highly sensitive and synchronous detection of miR-21 and miR-122 in serum can be achieved; the SERS sensor of the present invention has good specificity, stability and reproducibility. When detecting clinical serum samples, the operation is simple and the detection of miR-21 and miR-122 can be completed in a short time.

The invention relates to the technical field of artificial intelligence and machine learning, discloses a method for establishing an identification model of bone tuberculosis (STB) and suppurative bone infection (PSI) based on deep learning, and provides a novel interpretable deep learning model based on a Transform architecture, and the STB and the PSI are distinguished by using a diagnostic tag confirmed by metagenome next-generation sequencing. The TabPFN model provided by the invention not only shows strong diagnosis accuracy, but also has interpretability of early-stage STB recognition by utilizing the diagnosis tag confirmed by mNGS and common serological biomarkers, and provides instant early-stage diagnosis decision support for clinicians. According to the method, the TabPFN model is applied to diagnosis of the spinal bone tuberculosis for the first time, an efficient and accurate tool is provided, and a solid foundation is laid for future artificial intelligence auxiliary clinical diagnosis research.

The invention discloses a serum metabolism marker histidine for screening heat-resistant mutton sheep as well as a screening method and application of the serum metabolism marker histidine, and belongs to the technical field of animal molecular breeding and serum biomarker screening. The serum metabolism marker is histidine; the critical value of histidine for distinguishing heat-resistant mutton sheep and heat-stress mutton sheep in a mutton sheep serum sample is 3.18 ug / ml; under the heat stress condition, if the histidine content in the mutton sheep serum sample is higher than 3.18 ug / ml, the mutton sheep is heat-resistant mutton sheep; if the content of histidine in the mutton sheep serum sample is lower than 3.18 ug / ml, the mutton sheep is heat-sensitive mutton sheep. According to the screening method, a liquid chromatography-mass spectrometry combined mode and an orthogonal-partial least square discriminant analysis method are adopted for analyzing a sample, and the serum metabolism marker histidine for screening the heat-resistant mutton sheep is screened out. The serum metabolism marker histidine disclosed by the invention has higher accuracy and stability, and can be used for accurately evaluating the heat resistance of the Hu sheep in the early stage.

The invention discloses a preparation method of a Raman enhanced substrate applied to serum biomarker detection. Dielectric microsphere cavities are densely laid on the surface of a flexible high polymer material through a mechanical assembly method to form a micro-lens array, and a nanopore array is formed in the micro-lens array through ultrafast laser irradiation. And then gold nanoparticles are deposited in the nanopores through ion sputtering to form the novel composite structure substrate. Finally, based on excellent sensitivity and uniformity of the composite structure substrate, effective enhancement of a serum Raman spectrum and specific recognition of a biomarker are realized. The gold nanoparticle modified nanopore array composite structure substrate obtained by the invention has trace sample collection capability and excellent Raman enhancement performance, successfully realizes specific detection of serum biomarkers, and provides a new scheme for construction of a substrate with low cost, high sensitivity, high stability and accurate quantitative analysis for practical application.

The invention relates to application of phosphatidylinositol and phosphatidylglycerol as metabolic markers for predicting the progress of intracranial atherosclerosis, and belongs to the technical field of medical biological detection. According to the scheme, RICAS queue data and external database data are combined for single-sample MR analysis, the causal association between metabolites and ICAS is analyzed, ICAS characteristic metabolic change and causal association analysis are combined, serum biomarkers of phosphatidylinositol PI 18: 0 / 22: 6 and phosphatidylglycerol PG 18: 1 / 18: 2 of ICAS occurrence and development are screened out, ICAS progress is collected through prospective follow-up visit of an RICAS queue, and the ICAS development rate is improved. The association between the gene and ICAS progress is established, and the gene has a transformation application value and can be used as a novel biomarker for early prediction of ICAS progress and development.

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.

This invention relates to ficolin-2, a serum biomarker for assessing carotid atherosclerotic plaques, and its applications, belonging to the field of biomedical technology. To address the urgent need for readily available and repeatable circulating biomarkers that reflect the unstable state of atherosclerotic plaques, particularly their hemorrhagic characteristics, this invention provides the application of ficolin-2 as a serum biomarker in the preparation of products for assessing the stability of atherosclerotic plaques and the prognosis of sonodynamic therapy for atherosclerosis. This invention is the first to use serum ficolin-2 protein as an efficacy monitoring indicator for SDT treatment of hemorrhagic carotid plaques, monitoring the level of complement activation-related inflammation within the plaque, enabling dynamic management of patient treatment, filling the gap in the quantification of efficacy in hemorrhagic plaque treatment, and providing a key tool for the clinical translation of emerging therapies such as sonodynamic therapy.

The invention provides an amyotrophic lateral sclerosis prediction and prognosis evaluation system and a construction method of a sALS prediction and prognosis evaluation model based on multiple proteins. The system comprises an input module, a prediction evaluation module and an output module. The input module is used for acquiring the content of multiple candidate ALS serum biomarkers. And the prediction and evaluation module is used for calculating and scoring according to the sALS prediction and prognosis evaluation model based on multiple proteins, judging that the patient suffers from amyotrophic lateral sclerosis when the scoring result is greater than or equal to a specific judgment threshold value, and judging that the patient does not suffer from amyotrophic lateral sclerosis when the scoring result is less than the specific judgment threshold value. And the output module is used for outputting the prediction evaluation result. After the technical scheme is adopted, amyotrophic lateral sclerosis can be simply, quickly and accurately predicted and evaluated.

The application provides serum biomarkers for diagnosing and evaluating the activity of aortitis and application, and relates to the field of disease activity evaluation. The serum biomarkers are serum proteins: HP and ORM1. HP and ORM1 are determined as two new serum biomarkers through comprehensive proteomics analysis; gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis are carried out using a DAVID database; a protein-protein interaction network is constructed using Metascape. A random forest model is trained using a caret package, and a nomogram is visualized using an rms package. Serum samples are obtained, and the serum levels of HP and ORM1 are determined using a commercial ELISA kit; they are significantly related to disease activity, and are involved in inflammation / immune processes in aortitis, so the markers have the potential to serve as an auxiliary tool for monitoring the activity of aortitis.

The invention relates to the technical field of biomedicine, in particular to a serological biomarker for diagnosing chronic obstructive pulmonary disease. The marker is selected from the group consisting of myristic acid, oleic acid, stearic acid, eicosapentaenoic acid, arachidic acid, docosahexaenoic acid, docosapentaenoic acid, docosatetraenoic acid, nervonic acid, wood tar acid, arginine and glutamine. The combined marker has relatively high sensitivity and specificity, can provide reliable diagnosis information in an early stage or an atypical stage of symptoms, and overcomes the limitations of dependence on lung function detection, insufficient sensitivity, complex operation and the like of the existing diagnosis means; therefore, a convenient and accurate detection tool capable of being popularized is provided for early screening, risk prediction and clinical diagnosis of the chronic obstructive pulmonary disease.

The application relates to the technical field of medical systems, and particularly discloses a construction method of a cancer cachexia prognosis composite index CCAR based on a combination of creatinine and CAR, which comprises the following steps: S1, obtaining serum biomarker data of a cancer cachexia patient, including creatinine, C-reactive protein and albumin; S2, calculating a C-reactive protein to albumin ratio CAR=CRP / Albumin; S3, taking CAR and Cr as input features, and constructing a nonlinear composite index CCAR through a random forest machine learning model; and S4, developing a network calculator system; the CCAR index constructed by the application can comprehensively reflect the inflammatory burden, metabolic state and immune level of a cancer cachexia patient, can provide more accurate survival prognosis prediction compared with traditional indexes, and the parameters, such as creatinine, C-reactive protein to albumin ratio, contained in the CCAR index are all common blood examination items in clinical practice, so that the CCAR index is convenient to obtain and can be widely applied in clinical practice.