56 results about "Disease markers" patented technology

The invention provides a key node identification method for a disease marker expression regulation network, and belongs to the technical field of disease markers, and the method comprises the steps: firstly carrying out the preprocessing and quality control of original data, including batch effect removal, abnormal sample identification and the like; then identifying differential expression genes through multiple difference analysis and a pre-training model, and constructing a gene expression correlation network; and integrating multi-source regulation and control data to construct a multi-level weighted network, calculating network node features, and carrying out representation learning and module division. And based on multi-dimensional features such as network topology features, module contribution degree and biological importance, a neural network model is trained to carry out key node identification. And finally, optimizing the model through multi-layer verification such as pathway enrichment, disease gene overlapping, expression stability, time sequence change and network disturbance, and finally obtaining a verified key node set. The problem that in the prior art, the interaction relation between molecules is ignored, and consequently some key regulation and control nodes are possibly missed is solved.

A disease marker structure evolution characteristic change point determination method belongs to the field of disease markers, and comprises the steps of obtaining and preprocessing time sequence structure characteristic data of a disease marker, constructing a characteristic transformation image and calculating characteristic intensity distribution, establishing a structure characteristic fitting model and calculating a time evolution coefficient and an intensity evolution coefficient, determining a structural feature evolution trajectory and calculating a correlation index; establishing a structural feature piecewise function and identifying a feature mutation point; calculating a structural feature contribution value and generating a feature evolution matrix; calculating an evolution stability index and determining a structural feature change point; hierarchical clustering is carried out, main structural feature change points and secondary structural feature change points are determined, a feature weight distribution diagram is constructed, a change point time sequence table is generated, a time sequence corresponding relation is established, and a structural feature change point determination result is output; and fine analysis and accurate description of structural evolution characteristics of complex disease markers are realized.

The invention belongs to the technical field of medical detection, and discloses a blood disease high-throughput detection method based on a micro-fluidic chip and SERS (Surface Enhanced Raman Scattering) detection, which comprises the following steps: introducing a blood sample into the micro-fluidic chip with a micro-fluidic channel; the method comprises the following steps: performing SERS (Surface Enhanced Raman Scattering) detection on a blood sample through a nano-structure surface preset in a micro-fluidic channel; according to the present invention, with the microfluidic chip and the SERS technology, the high-throughput detection of the blood sample is achieved, the SERS spectrum data is collected and analyzed, the disease marker in the blood is identified, and the high-throughput detection of the blood sample is achieved through the combination of the microfluidic chip and the SERS technology. A plurality of micro-channels on the micro-fluidic chip can process a plurality of samples in parallel, the detection efficiency is remarkably improved, and the strength of Raman scattering signals can be remarkably improved by utilizing the surface enhancement effect of the SERS technology, so that high-sensitivity detection of blood disease markers is realized, and the detection cost is reduced. Accurate control and efficient separation of trace samples are achieved through the micro-fluidic chip, complex pretreatment steps are not needed, and the operation process is simplified.

The invention discloses a biosensor for detecting Alzheimer's disease markers as well as a construction method and application thereof, and relates to the technical field of biosensors. The construction method comprises the following steps: after a glassy carbon electrode is cleaned, dropwise adding a RuSi NPs (at) CS compound on the surface of the glassy carbon electrode to obtain a RuSi NPs (at) CS / GCE electrode; and immersing the RuSi NPs coated CS / GCE electrode into a PHL: S double-chain solution, and carrying out an incubation reaction to obtain the biosensor. The RuSi NPs (at) CS compound is obtained by carrying out mixed reaction on ruthenium-silicon nanoparticles and chitosan; pHL: S double chains contained in the PHL: S double-chain solution are obtained by carrying out incubation reaction on carboxyl-activated PHL chains and S chains. The biosensor has the advantages of high accuracy, strong sensitivity, good selectivity and the like, and can be applied to accurate detection of the Alzheimer's disease marker A beta oligomer.

A method for judging the structural stability of a disease marker belongs to the field of disease markers, and comprises the following steps: acquiring a disease marker sample, and collecting spectral data, fluorescence intensity data, surface charge data and protein content data of the disease marker as multi-dimensional data of the disease marker; performing spectral stability analysis on the multi-dimensional data of the disease marker to obtain a first judgment result, and if the first judgment result is in a stable state, performing molecular structure scoring analysis on the fluorescence intensity data of the disease marker through a pre-trained deep neural network model to obtain a second judgment result; and if the second judgment result is a stable state, performing structural integrity analysis based on the surface charge data of the disease marker and the protein content data of the disease marker to obtain a third judgment result, and outputting a final structural stability judgment result of the disease marker according to the third judgment result. The technical problem that the overall structure stability of the disease marker is difficult to accurately assess in the prior art can be solved.

The invention provides a light-excited lateral flow immunosensor reagent strip, a detection analysis method and application of the light-excited lateral flow immunosensor reagent strip. The reagent strip comprises an electrode substrate, a combination pad, a sample pad layer, a chromatography membrane and a water absorption pad, and the sample pad layer, the combination pad, the chromatography membrane and the water absorption pad of the reagent strip are connected end to end and are sequentially overlapped and pasted on the electrode substrate. The light-excited lateral flow immunosensor reagent strip is applied to quantitative analysis of various disease markers in biological fluids such as blood, saliva, cerebrospinal fluid or urine.

The invention provides a disease marker epitope prediction and antibody screening method, which belongs to the technical field of disease markers, and comprises the following steps: firstly, establishing a training data set containing a known antigen-antibody compound structure and disease tissue expression data; and obtaining target protein sequence information through liquid chromatography-mass spectrometry analysis and carrying out sequence comparison. And then a deep convolutional neural network is utilized to extract sequence features, and surface exposure sites are identified by combining secondary structure prediction and solvent accessibility analysis. After the features are integrated with sequence evolution conservative properties, a prediction model is constructed by using a random forest classifier. The method comprises the following steps: carrying out molecular dynamics simulation on a prediction result, screening first 10% of candidate sequences through a comprehensive scoring function and K-means clustering, and finally determining an antigen epitope sequence with the strongest binding activity through verification of an antigen chip and a fluorescence labeled antibody system, so that the technical problem that specific antigen epitopes are difficult to accurately predict and recognize in the prior art is solved.

The invention relates to the technical field of biosensors, in particular to a novel thin film material for a molecular diagnosis nascent biosensor, a thin film transistor and a preparation method and application of the novel thin film material and the thin film transistor. The thin film transistor is prepared from a novel thin film material, the thin film material covers the surface of a substrate of the transistor, and a source electrode and a drain electrode of the transistor are arranged between the substrate and a thin film in parallel. The concentration of the cancer marker is detected by monitoring the variable quantity of threshold voltage (Vth) in a transfer curve of the organic-inorganic hybrid thin film transistor, the responsivity and sensitivity to a disease marker are improved by optimizing a semiconductor material combination, efficient detection of the low-concentration cancer marker is realized, and the method has the characteristics of no label, rapidness, simplicity and convenience, and is suitable for popularization and application. The kit is suitable for early diagnosis of diseases.

The invention provides a disease marker subtype classification determination method, and belongs to the technical field of disease marker subtype classification, and the method comprises the following steps: firstly, extracting protein from a patient sample, separating through a dimensional electrophoresis technology, and obtaining a protein expression matrix through Coomassie brilliant blue dyeing and an image analysis system; differentially expressed proteins are determined, and sequence information is obtained through mass spectrum identification. And extracting network node features by using a graph neural network method, and carrying out function annotation analysis and path identification. Patient samples are grouped by using a multi-level clustering method, and feature extraction and optimization are realized by training an embedded neural network model. And inputting the extracted features into a pre-trained function enrichment analysis model, and determining the molecular pathway features of the disease subtype. And finally, the reliability of a classification result is confirmed through an immunoblotting technology and independent patient queue verification, subtype classification of the disease markers is completed, and the problem that in the prior art, the recognition degree of subtype classification of the disease markers is not high enough is solved.

The invention discloses a multi-nucleic acid probe rapid modification and multi-target detection device based on electric field regulation and control. An instrument main body in the device is integrated with an automatic liquid path system and a micro electrochemical workstation module, and is used for realizing programmable reagent and sample conveying and multi-channel electrochemical measurement. The detachable sensing chip is formed by bonding a microelectrode array chip and a micro-fluidic chip, and is switched through a PCB (Printed Circuit Board) to realize plug and play. According to the present invention, the positive suction-negative discharge strategy of the electric field is utilized, the preferred positive potential is applied to the target electrode to efficiently attract the probe, and the preferred negative potential is applied to the non-target electrode to inhibit the non-specific adsorption so as to achieve the spatial selective rapid fixation and the accurate density regulation and control of the multi-nucleic acid probe in the extremely short time; the device can be used for quantitatively detecting various body fluid disease markers in a plurality of urine samples at the same time in single operation, and an efficient, accurate and portable multi-marker comprehensive analysis solution is provided for early screening and instant diagnosis of tumors.

The present invention relates to the field of data processing technology, and in particular to a genomic data analysis method. The method comprises the following steps: preprocessing genomic data, performing intelligent compression processing on the preprocessed genomic data using an adaptive multidimensional space compression algorithm to obtain compressed genomic data; performing gene variation detection and filtering on the compressed genomic data to obtain variant genomic data, and performing functional annotation to obtain genomic data with annotation results; performing gene expression analysis on the genomic data with annotation results to obtain gene expression data; and performing association analysis based on the genomic data with annotation results and gene expression data using a gene variation-phenotype association analysis method to screen out potential disease markers. The method solves the technical problems of inaccurate genomic data processing, low computational efficiency, and low analysis accuracy in the genomic data analysis process of traditional genomic data storage and processing technologies.

The invention relates to the technical field of image processing, in particular to a retinopathy recognition method, system and device based on artificial intelligence and a medium, and the method comprises the steps: taking the eye of a detected pregnant woman as a target object, setting a preset position, scanning in the depth direction, obtaining point fault information, carrying out three-dimensional reconstruction, and generating a second image. Generating a third image, performing correction processing, presetting a straight line in the corrected image, obtaining pixel values corresponding to the positions where the first pixel and the second pixel are located, and obtaining the thickness data of each layer; taking the thickness data as a pixel value; setting the value of each layer of pixel to be equal to the thickness data of the layer; generating a fourth image; and establishing an image database, adding a disease marker to the reference image, training the image estimation model by using the reference image added with the disease marker, and outputting a disease corresponding to the fourth image. The method improves the prediction efficiency and accuracy of the eye fundus image.

The invention belongs to the technical field of mass spectrum ionization sources, provides a tandem mass spectrum ionization source for online detection of disease markers in tail end gas of expired gas of a human body, and aims to solve the problems of complex expired gas sampling, insufficient detection sensitivity and difficult real-time monitoring in the prior art. According to the ionization source device, real-time online expiration analysis of expired gas of a human body can be achieved, the expired gas end gas moment can be determined according to the change of the concentration of carbon dioxide in the expired gas, and therefore real-time analysis of disease markers in the expired gas end gas is achieved. According to the technical scheme, the design that double ionization sources are connected in series is adopted, and a funnel-shaped chemical ionization source is arranged at the front end and specially used for high-sensitivity detection of volatile organic compounds in exhaled air; the photoelectron ionization source is arranged at the rear end and used for monitoring the carbon dioxide concentration in real time and determining the end gas moment. According to the design, high sensitivity of organic matter detection and accurate monitoring of carbon dioxide concentration are both considered in a partitioned ionization mode. The efficient, sensitive and reliable mass spectrum ionization source device is provided for exhaled gas detection, automatic distinguishing of the front end and the tail end of exhaled gas and high-sensitivity analysis of disease markers in tail end gas can be achieved, the sampling process can be simplified, the detection efficiency can be improved, and the mass spectrum ionization source device has wide application prospects, can be suitable for various scenes and has wide application prospects. The method includes clinical disease early diagnosis, health monitoring and the like.

The present invention provides a light-activated lateral flow immunosensor reagent strip, a detection and analysis method, and its application. The reagent strip comprises an electrode substrate, a conjugate pad, a sample backing layer, a chromatographic membrane, and a water-absorbing pad. The sample backing layer, conjugate pad, chromatographic membrane, and water-absorbing pad are connected end to end and sequentially overlapped and affixed to the electrode substrate. The light-activated lateral flow immunosensor reagent strip is used for the quantitative analysis of various disease markers in biological fluids such as blood, saliva, cerebrospinal fluid, or urine.

The disclosure relates to systems and methods for evaluating markers of disease by using optical techniques A method includes analyzing data from an imaging of an eye of a patient with at least one processor, the data from the imaging including a plurality of pixels and including, for each pixel, spatial, spectral, and polarimetric data generated from the imaging of the eye. The method further includes based on the analyzing, classifying the patient into at least one category of a plurality of categories, each category of the plurality of categories indicating a status with respect to a neurodegenerative disease that affects a central nervous system. The method further includes generating an output of one or more of the at least one category indicating the status of the patient with respect to the neurodegenerative disease that affects a central nervous system.

A method for capturing distinct higher order interactions of a dataset relevant to biological inferences includes deriving Hamiltonian parameters for the dataset, wherein the dataset includes data responsive to a phenotype of interest. The method further includes generating a partition function responsive to the Hamiltonian parameters; calculating cumulant moments of the partition function and deriving higher order cumulants using the Hamiltonian parameters, wherein the higher order cumulants are responsive to the phenotype of interest.

The invention provides a human body expired gas detection method and device based on deep learning and a semiconductor sensor array, and belongs to the technical field of gas detection. The problem that an existing semiconductor gas sensor is low in mixed gas recognition accuracy is solved. Comprising the following steps: acquiring expired gas R-t response curve data of a patient as sample data; constructing mixed gas, and obtaining and storing a mixed gas R-t response curve group; the collected patient exhaled gas R-t response curve data and the mixed gas R-t response curve set are preprocessed, and a patient exhaled gas gas response curve data set is constructed through the preprocessed patient exhaled gas R-t response curve set; constructing an expired gas disease marker fitting data set by utilizing the preprocessed mixed gas R-t response curve group; fitting a data set by using the exhaled air disease markers to construct an evaluation system; constructing a recognition model, and performing learning training on the recognition model by using the data set; the method is applied to human body exhaled air detection.

To provide renal disease markers for accurately evaluating and determining a wide range of renal diseases in subjects.SOLUTION: A method of assisting in evaluation of renal disease is provided, the method involving the use of a ratio of reabsorption and excretion of D-serine and / or D-asparagine in the kidney of a subject as an indicator.SELECTED DRAWING: None

The present invention relates to the technical field of crop disease and insect pest monitoring, and in particular provides an intelligent crop disease and insect pest monitoring system based on image recognition and machine learning. The system comprises: a benchmark model generation subsystem for acquiring a continuous state stream within the crop growth cycle through a multispectral imaging array; a micro-field comparison of the real-time image of the continuous state stream with an ideal morphological benchmark vector, outputting a biological stress difference map; a mutually exclusive association engine subsystem for purifying the pathological response map, inputting it into a pathological feature dissociation network, and outputting a set of environmental immune disease markers; and a pathogen source trajectory deduction subsystem for analyzing the initial infection focus coordinates, combined with a real-time wind direction vector field and a plant density topology map, through a biological diffusion dynamic chain. The present invention solves the problem of high false positive rates in complex farmland scenarios through a dual denoising mechanism of growth rhythm decoupling and environmental interference stripping, ultimately achieving a leap in monitoring dimensions from single-point identification to group prevention and control.

The present invention relates to a green fluorescent nanosphere, a preparation method and uses thereof, comprising: respectively preparing solutions of green fluorescent gold nanoclusters AuNCs, dendritic mesoporous silica nanoparticles DMSN and polyetherimide PEI; adding the PEI solution to the DMSN solution, mixing evenly and carrying out an ultrasonic reaction to obtain a mixed solution of the DMSN solution coated with PEI; centrifuging and redissolving the obtained mixed solution, adding the AuNCs solution, mixing evenly and carrying out an ultrasonic reaction, and obtaining the green fluorescent nanosphere DMSN@AuNCs after multiple centrifugal purifications. The operation of the present invention is simple, the cost is low, the reaction conditions are mild, and it is green and environmentally friendly. The prepared green fluorescent nanosphere DMSN@AuNCs can be used as a fluorescent labeling material in the field of immunological detection; according to the different antibodies and antigens labeled on the DMSN@AuNCs, a variety of fluorescent detection probes can be formed to detect different targets, and it has high popularization and application value in aspects such as high-throughput detection of disease markers.

The present invention relates to a method for analyzing mucin types and glycan structures of O-linked glycopeptides. More specifically, the present invention relates to a method for identifying types of O-linked glycopeptides using a high-resolution mass spectrometer, a method for analyzing glycan binding positions of O-linked glycopeptides, and a computer-readable recording medium recording a program applied to perform the methods. The methods according to the present invention can efficiently and accurately analyze various mucin types and glycan structures of O-linked glycopeptides, and can be usefully employed to easily discover disease markers from samples for prediction or diagnosis or to analyze the structures of O-linked glycopeptides in glycoprotein drugs by using a high-resolution mass spectrometer.

The invention relates to a multi-channel paper-based micro-fluidic chip for sequential detection of disease markers, the multi-channel paper-based micro-fluidic chip sequentially comprises an inner-ring chip, a reagent ring and a peripheral chip from inside to outside, and the reagent ring can rotate independently; a buffer solution storage area and a plurality of buffer solution runners communicating the buffer solution storage area to the outer edge of the inner ring chip are arranged in the inner ring chip; a plurality of reagent runner groups distributed at intervals are arranged in the reagent ring, and each reagent runner group comprises a marking runner, a developing runner and a flushing runner; a plurality of test areas and a waste liquid storage area communicated with the test areas are arranged in the peripheral chip; the buffer solution flow channels, the reagent flow channel groups and the test areas are in one-to-one correspondence. The multichannel paper-based micro-fluidic core designed by the invention is ingenious in internal flow channel layout, and sequential detection of disease markers can be conveniently and quickly completed by rotating the reagent ring.

The invention relates to a method for screening Alzheimer's disease markers by using a bioinformatics technology and application of the Alzheimer's disease markers. Serum microRNA samples of Alzheimer's disease normal subjects (NC), mild cognitive impairment (MCI) and Alzheimer's disease (AD) patients in an NCBI public database are analyzed, and a bioinformatics analysis method is adopted to systematically identify common miRNA which commonly presents differential expression at the early stage and the late stage of the disease, so that target mRNA of the common miRNA is predicted, and a regulatory network is constructed; and finally screening out specific miRNA closely associated with the key mRNA as a disease monitoring marker. The method breaks through the high-cost limitation of traditional dependence on high-throughput sequencing, realizes efficient and reliable screening and identification of disease molecular markers by deeply mining public data resources, provides a new molecular target and detection means for early diagnosis, illness monitoring and intervention treatment of Alzheimer's disease, and has a wide application prospect. The important clinical application value is realized.

The invention provides a ratio fluorescence biosensor based on photonic crystal signal amplification and application thereof. The biosensor comprises a fluorescent magnetic bead modified by a capture antibody, a detection antibody modified by biotin, avidin modified by fluorescein and a substrate layer, wherein a photonic crystal layer is deposited on the substrate layer. The detection method provided by the invention has the advantages of small sample dosage, convenience in detection, rapidness, high efficiency, high sensitivity and low cost, and has the potential to be applied to various scenes requiring detection of low-abundance markers, such as drug research and development, early diagnosis of nerve diseases, tumor recurrence monitoring, discovery and confirmation of disease markers and the like.

The present invention relates to an Alzheimer's disease marker and risk prediction model based on intestinal flora. The Alzheimer's disease marker based on intestinal flora includes intestinal flora, and the intestinal flora includes anaerobic Lawsonia sibiricum, Escherichia coli, Streptococcus salivarius, Asahidiella pseudomallei, Kluyveromyces pedis, intestinal Roseburia, Roseburia faecalis, Roseburia hominis, Firmicutes CAG424 bacteria and Clostridium CAG58 bacteria. The present invention mines Alzheimer's disease markers based on intestinal flora and further constructs an Alzheimer's disease risk prediction model based on intestinal flora, which has the characteristics of high detection accuracy, convenience, speed, safety and non-invasiveness, and has important clinical guidance significance for assisting the diagnosis of Alzheimer's disease-related indicators.

The invention discloses a kit for detecting Alzheimer's disease markers and a preparation method thereof, and relates to the technical field of immunodetection. The kit for detecting the Alzheimer's disease marker comprises a shell and a test strip, wherein the test strip comprises a bottom plate extending along a first direction as well as a sample pad, a combination pad, a coating pad and absorbent paper which are arranged on the bottom plate at intervals along the first direction; the combination pad is coated with an A beta 1-40 labeled primary antibody, an A beta 1-42 labeled primary antibody and a quality control labeled primary antibody; the coating pad is sequentially provided with a first detection piece, a second detection piece and a quality control piece along a first direction, the first detection piece is coated with one of an A beta 1-40 secondary antibody and an A beta 1-42 secondary antibody, the second detection piece is coated with the other of the A beta 1-40 secondary antibody and the A beta 1-42 secondary antibody, and the quality control piece is coated with a quality control secondary antibody. According to the technical scheme, A beta 1-40 and A beta 1-42 can be detected at the same time.

The application discloses a radial multilayer microneedle sensing system and method for single follicular unit microenvironment analysis, and belongs to the field of skin appendage detection and biological micro-electro-mechanical systems. The system comprises a positioning hole integrated with an optical collimation unit, concentric ring microneedles with a length ratio within a certain range, ring compression protruding structures between the microneedles, a differential negative pressure sampling module and a detection electrode modified with an aptamer probe, which can accurately position the follicle, perform layered puncture, construct a hydraulic barrier to prevent cross-flow, isolate and extract interstitial fluid between deep and shallow layers and target detection of disease markers such as androgen alopecia. The method comprises the steps of optical targeting positioning, compression sealing puncture, differential sampling, in-situ detection and gradient index calculation. The application can improve the spatial detection resolution of the markers, solve the problems of signal dilution and subcutaneous cross-flow, and provide a quantitative tool for follicular pathological research and drug non-diagnostic screening.

The present invention discloses a biosensor for detecting Alzheimer's disease markers, as well as a construction method and application thereof, relating to the field of biosensor technology. The construction method comprises the following steps: after cleaning a glassy carbon electrode, dripping a RuSi NPs@CS complex onto the surface of the cleaned glassy carbon electrode to obtain a RuSi NPs@CS / GCE electrode; immersing the RuSi NPs@CS / GCE electrode in a PHL:S double-strand solution for incubation to obtain the biosensor; the RuSi NPs@CS complex is obtained by mixing ruthenium silicon nanoparticles and chitosan; the PHL:S double-strand solution contains PHL chains activated by carboxyl groups and S chains through an incubation reaction. The biosensor has the advantages of high accuracy, strong sensitivity, and good selectivity, and can be used to accurately detect Aβ oligomers, a marker of Alzheimer's disease.

The invention relates to the technical field of immunofluorescence detection, in particular to an immunofluorescence detection reagent for HCR signal amplification. According to the invention, a double-stranded DNA product carrying a large amount of biotin is formed by utilizing linear hybridization chain reaction, and is linked with fluorescent dye labeled streptavidin, so that a constant fluorescent signal is amplified, the detection sensitivity is greatly improved, and high-sensitivity detection of disease markers such as proteins and small molecules is realized.

The present application provides a kind of road disease distribution visualization method and system, including data acquisition module through mobile detection vehicle equipped with industrial camera, three-dimensional laser scanner and combined navigation device, real-time collection disease data, and carry out space-time synchronization and pretreatment;Disease analysis module realizes the space alignment of multi-source data based on position data, identifies the disease boundary in image through semantic segmentation and maps to scanning data, extracts disease point set and calculates disease area, depth and volume parameters;Decision module integrates historical and real-time disease parameters, weather data, generates disease evolution trend by combining time series prediction model, and marks high-risk areas by risk index;Visualization module dynamically renders disease markers on three-dimensional map, supports interactive display of historical changes on time axis, and dynamically renders high-risk areas on heat map.The present application realizes multi-dimensional dynamic monitoring and risk warning of road disease, and improves road maintenance decision efficiency.