402 results about "Assay technique" patented technology

The invention discloses a clinical examination and detection item correlation analysis method based on multi-agent cooperation, and relates to the technical field of correlation analysis, and the method comprises the steps: obtaining the name and basic parameters of an examination item, and determining the technical field of the examination item through a classification system and an algorithm; related field parameters are extracted from the knowledge management module, and configuration parameters of the intelligent agent are set; generating a task instruction based on the basic parameters, transmitting the instruction through a structured message transmission mechanism, and obtaining an agent processing result; and performing verification analysis on the processing result by using a hypothesis production and verification engine to obtain a correlation analysis result of the project. According to the method, the hypothesis content can be evaluated from multiple angles, it is ensured that the obtained correlation analysis result has high scientificity and credibility, and powerful support and basis are provided for research and application of clinical examination and detection items.

The invention relates to the technical field of prognosis analysis, in particular to a pancreatic cancer prognosis analysis system based on big data mining, which comprises a jump trend extraction module, an index grade mapping module, a sample stability screening module, a path node offset identification module and a prognosis stage classification module. According to the method, a change rate difference value and a third-order response sequence are constructed for continuous three-stage pancreatic cancer biological index sequences, jump trend feature points are extracted, and a risk grade transition sequence is formed in combination with grade span and direction judgment; multi-dimensional normalized parameters of a variable coefficient, a survival score difference value and an organ function score standard deviation are introduced to perform stable sample screening, interference of data disturbance on a path judgment result is reduced, and path mutation node positions are screened in combination with a path stability threshold value; precise classification and marking of high-risk variation stages in prognosis paths of pancreatic cancer patients are realized, stage reference is provided for individualized intervention strategies, and risk identification sensitivity and layered intervention scientificity are improved.

The invention belongs to the technical field of biological detection, and particularly relates to a disease marker fluorescence instant detection method based on protein scaffold DNA-Cu2 + functional nanospheres, which comprises the following steps: combining an insulin-like growth factor 1 (IGF-1) aptamer or cortisol aptamer, protein-DNA and DNA-metal ions; the protein scaffold DNA-Cu < 2 + > multifunctional integrated nanospheres are synthesized by a one-step method at room temperature. Based on the function integration of the nanosphere, one-step detection of IGF-1 or cortisol is realized, a plurality of analysis modes including fluorescence detection and smartphone-RGB are provided by combining a test strip and smartphone red, green and blue (RGB) analysis technologies, and a rapid and sensitive disease marker instant detection method is constructed. Finally, rapid and convenient detection of multiple markers in hundreds of clinical samples of multiple diseases is realized, and a new solution is provided for paper-based fluorescence POCT.

The invention discloses a method, a sensor and a system for testing an analyte in a biological sample, and belongs to the technical field of biological sample analysis. The method comprises the following steps: contacting a first working electrode and a second working electrode with a biological sample, and inputting a first excitation signal and a second excitation signal to the first working electrode; inputting an excitation signal sequence to the second working electrode between the first excitation signal and the second excitation signal; measuring at least one first output signal responsive to the first excitation signal, at least one second output signal responsive to the second excitation signal, and at least one sequence of output signals responsive to the sequence of excitation signals on the first working electrode; and obtaining the concentration of the analyte in the biological sample according to the first output signal, the second output signal and the output signal sequence. The first output signal, the second output signal and the output signal sequence are used as parameters for calculating the concentration of the analyte in the biological sample, so that the influence of interferents can be eliminated, and the detection accuracy of the test method is improved.

The invention relates to the technical field of biomedical data analysis, and discloses a data analysis method and system for genetic disease gene detection and a storage medium, and the method comprises the following steps: collecting a patient sample, and carrying out high-throughput sequencing to obtain original data; performing quality control and comparison processing on the data to generate variation detection data, and calculating variation information amount; suspicious variation sites are automatically screened, variation information amount is analyzed based on information entropy, and key variation is screened according to a preset threshold; performing Bayesian inference analysis on the key variation, calculating pathogenicity probability, and performing pathogenicity judgment based on an ACMG standard; calculating the matching degree of the key variation and the phenotype in combination with the phenotype information of the patient, and screening the variation conforming to phenotype characteristics; submitting the screening result to a doctor for auditing, and generating a final screening result; and generating a standardized gene detection report according to a final result. According to the method, pathogenic variation is efficiently and accurately recognized, and the automation level and clinical application value of genetic disease gene detection are improved.

The present invention discloses a disease prediction method and system based on adaptive fusion of microbiome hierarchical features, which relates to the technical field of microbiome data analysis. A trained disease prediction model is used to predict diseases based on microbiome data. The disease prediction model performs multi-level feature extraction on feature data of five classification levels through a hierarchical feature extraction module, and integrates the results into multi-level fusion features. The multi-scale feature fusion module processes the multi-level fusion features through three parallel channels of pattern flow, context flow and content flow to achieve an all-round representation of microbiome data. The dynamic sampling module is introduced in the training process, and based on the class balance mechanism and the uncertainty evaluation strategy, the sample sampling weights are dynamically adjusted. Finally, a classifier is used to predict diseases for the multi-scale fusion features. The present invention makes full use of microbiome classification level information, realizes multi-dimensional feature representation, effectively addresses the problem of sample imbalance, and improves the accuracy of disease prediction.

The invention provides a breast cancer HER2 immunohistochemical digital slice automatic interpretation method, device and equipment and a storage medium. Relates to the technical field of digital pathological image analysis. Acquiring a full-slice image, automatically identifying an external contrast area and a tissue area through a multi-task head real-time instance segmentation model, and segmenting an ROI (Region of Interest) area of tumor cell aggregation; extracting image blocks by adopting a fixed sliding window based on the ROI region, and constructing a double-branch multi-task feature extraction model containing a backbone network: realizing HER2 expression pattern classification by an image block classification head, and outputting a continuous protein expression intensity quantized value by a dyeing intensity numerical head through regression analysis; and establishing a slice-level interpretation model, integrating the classification results and the intensity quantized values of all the image blocks, and outputting an automatic HER2 interpretation result. According to the invention, end-to-end analysis from cell-level characteristic quantification to slice-level diagnosis is realized, and an objective and quantifiable intelligent interpretation scheme is provided for HER2 immunohistochemical evaluation.

The invention relates to the technical field of Escherichia coli drug resistance analysis, in particular to an Escherichia coli antibiotic drug resistance detection method based on SERS and machine learning. The invention provides an Escherichia coli antibiotic resistance detection method based on SERS (Surface Enhanced Raman Scattering) and machine learning so as to overcome the defect that high-precision and rapid diagnosis cannot be realized in the prior art. The invention aims to realize rapid and high-precision identification of drug-resistant bacteria through a machine learning algorithm by utilizing a core-shell nanostructure SERS substrate of silver-coated gold nanoparticles and combining SERS spectrum differences of drug-resistant escherichia coli with different phenotypes. Through the method, the detection time can be remarkably shortened, the detection precision can be improved, an accurate drug treatment scheme is provided for clinic, and the method has a wide application prospect.

The invention provides a method for detecting a virus inactivation effect by an ELISA (Enzyme-Linked Immunosorbent Assay) method, which comprises the following steps: acquiring a to-be-detected inactivated virus sample, extracting an antigen component from the sample, and detecting an antigen structure change characteristic value through a high-throughput mass spectrometry analysis technology to obtain antigen degradation degree data; obtaining a sample subjected to preliminary inactivation, and detecting the antigen content change trend through an enzyme-linked immunosorbent assay to obtain content change curve data; detecting the residual quantity of viral nucleic acid by a real-time fluorescent quantitative PCR (Polymerase Chain Reaction) technology aiming at the immunogenicity retained sample to obtain nucleic acid degradation degree data; acquiring nucleic acid degradation degree data, classifying the relationship between nucleic acid residues and infectivity by adopting a support vector machine algorithm, and judging that the inactivation effect is completely completed if the classification result shows that the infectivity is lost; according to the antigen content change trend, the immunogenicity index and the nucleic acid degradation degree data, a weighted fusion algorithm is adopted to integrate multi-dimensional detection results, and a comprehensive inactivation effect score is obtained.

The invention relates to the technical field of fluorescence immunoassay, and discloses a quantum resonance fluorescence immunoassay medical detection method, which comprises the following steps: 1, carrying out fixation and permeation treatment on cells, respectively incubating the cells by using labeled different quantum dot-antibody compounds, specifically binding antibodies with corresponding target proteins in the cells, and detecting the target proteins in the cells; a labeled cell sample is obtained; 2, under a confocal microscope, laser with a specific wavelength is adopted to excite the marked cell sample, and fluorescence lifetime image data of different quantum dot emission wavelengths are collected; according to the method, the fluorescence lifetime image data of different quantum dot emission wavelengths are acquired through the confocal microscope, the position and content information of different target proteins can be clearly distinguished, simultaneous analysis and accurate positioning of multiple target proteins are realized, and an efficient means is provided for multi-protein research in a complex biological system.

The invention discloses a urine sampling device for the endocrinology department and a sampling method, and belongs to the technical field of medical analysis. Comprising the following modules: an automatic sampling control module for realizing automatic control of the whole urine sampling process, ensuring that sampling accords with a standard process, and supporting automatic sub-packaging and storage management of samples; the sample quality control module is used for monitoring the physical characteristics of the samples in real time, automatically evaluating the effectiveness of the samples in combination with environmental parameter monitoring, and identifying and marking abnormal samples; the data acquisition and processing module is used for comprehensively acquiring physiological data, nutrition metabolism data, operation process data and sample tracing information, and performing cleaning, standardization and fusion processing to generate a standardized data set; the intelligent diagnosis and analysis module is used for intelligently detecting abnormal physiological indexes and carrying out disease risk prediction and early warning; and the man-machine interaction module provides a visual operation interface and integrates multi-level user authority management, automatic report generation and remote monitoring.

The invention discloses a digital single-molecule enzyme multi-activity characteristic cross-scale heterogeneity dynamic evaluation method, which comprises the following steps: diluting a pre-combined enzyme-template-primer ternary complex to a single-molecule level so as to load the enzyme-template-primer ternary complex into a reaction chamber in a micropore array chip for rolling circle amplification, a molecular beacon is combined with an amplification product to release a fluorescence signal; monitoring and acquiring the fluorescence signal in real time to generate a time-resolved fluorescence intensity curve; extracting a slope parameter of the fluorescence intensity curve so as to obtain a catalytic synthesis rate through conversion calculation for evaluating enzyme activity; the distribution of catalytic synthesis rate values is fitted using an n-fold Gaussian mixture model to obtain a standard deviation for assessing population homogeneity and a coefficient of variation for assessing stability. According to the invention, cross-scale analysis of single-molecule resolution, multi-parameter dynamic association and environmental interference processing can be realized at the same time, and resolution limitation of a traditional analysis technology is broken through by integrating single-molecule fluorescence tracking and population statistics.

The invention belongs to the technical field of tumor clustering analysis, and particularly relates to a tumor clustering analysis system and method based on big data. Through deep fusion and space-time alignment processing of multi-dimensional biological data, the limitation of single-dimension or simple data integration in a traditional method is overcome, complex biological characteristics and space-time evolution laws in the tumor can be captured more comprehensively, and the tumor can be rapidly and accurately captured by constructing a low-dimensional association and independent characteristic space and combining a nonlinear dimension reduction technology. According to the method, low-dimensional feature vectors with biomarker features are effectively extracted, a high-quality data basis is provided for subsequent clustering analysis, meanwhile, based on sliding window density estimation and determination of a dynamic neighborhood radius, accurate spatial adjacency relation construction of the low-dimensional feature vectors is achieved, and the accuracy of the spatial adjacency relation is improved. And the potential clustering center of the tumor subtype is extracted through the multi-scale characteristic spectrum, so that the generated classification label can more accurately reflect the real distribution of the tumor subtype.

The invention relates to the technical field of medical data analysis, in particular to a lung adenocarcinoma osimertinib drug resistance prediction model construction method and system based on machine learning. Clinical data of lung adenocarcinoma patients and mRNA (messenger ribonucleic acid) data of a public database are integrated, after Mann-Wh itney U inspection, FPKM standardization and other preprocessing are carried out, XGBoost is utilized to construct a basic model driven by clinical indexes, a prognosis model driven by gene characteristics is constructed through R packet Mime, and TYMS and UAP1L1 are screened out to serve as key genes. The performance of the model is optimized through residual correction and stacking integration, and finally the AUC is improved to 0.924. Serum RNA (Ribonucleic Acid) detection and verification show that TYMS and UAP1L1 are remarkably and highly expressed in a drug-resistant group. According to the method, traditional biopsy is replaced with noninvasive serum detection, the detection cost is reduced, the model generalization ability is verified through an independent queue, and an efficient and accurate solution is provided for early warning and personalized treatment of osimertinib drug resistance of the lung adenocarcinoma patient.

The invention discloses a component detection system for unmanned automatic production of an extracting solution, and relates to the technical field of automatic production and quality control. The system integrates a plurality of detection technologies, including infrared spectroscopic analysis, OD value detection, pH value monitoring and absorbance detection, so as to realize real-time monitoring and automatic production management of the components of the extracting solution. By establishing a standard product fingerprint spectrum database, the system can automatically compare real-time detection data with a standard spectrum, and immediately triggers an alarm or stops production once an abnormality is found, so that the stability of product quality and the high efficiency of a production process are ensured. The system not only improves the automation degree of a production line, but also predicts and optimizes bioactive components through a whole genome nutrition group analysis technology in combination with species genome, transcriptome data and metabolome information, realizes precise formula and efficient production, and provides powerful support for an unattended automatic production system.

Systems and methods are disclosed for analyzing data from oncology treatments such as immune checkpoint inhibitor or radiotherapy therapies, including predicting adverse events of the oncology therapies, predicting objective response of the oncology therapies, predicting symptoms from the oncology therapies, and use of such predictions by technological implementations to achieve improved system and medical outcomes. An example technique for generating a predicted treatment outcome includes: receiving patient data for a human subject, which provides patient-reported outcomes collected from the human subject relating to a particular oncology treatment; processing the patient data with a trained artificial intelligence (AI) prediction model, which receives the patient data as input and produces a prediction of a treatment outcome as output; and outputting data to modify a treatment workflow of an oncology treatment for the human subject, based on the prediction of the treatment outcome.

The invention belongs to the technical field of synthetic biology and biological detection and analysis, and particularly relates to a trimethylamine N-oxide biosensor, a genetically engineered bacterium and application of the genetically engineered bacterium. Specifically, a TMAO response type biosensor based on a TorRST atypical two-component system (TCS) is developed in escherichia coli, and the TMAO response type biosensor is optimized. Meanwhile, a TMAO degradation pathway regulated and controlled by a TMAO biosensor is introduced into escherichia coli EcN, so that an engineering probiotic capable of sensitively detecting, regulating and controlling degradation and balance of TMAO is successfully developed. Therefore, the compound can be used for preventing and treating diseases caused by TMAO, and has good practical application value and prospect.

The invention relates to the technical field of online detection and analysis, and discloses a bone joint synovial fluid detection and analysis system based on microfluidics, which specifically comprises the following steps: acquiring synovial fluid rheological data and synovial fluid molecular activity data in a bone joint movement process and cartilage surface morphological data in a joint buckling process through a biosensor; constructing a synovial fluid dynamic anomaly analysis model, and performing synovial fluid function anomaly analysis through the synovial fluid rheological data and the molecular activity data; a cartilage stress anomaly analysis model is constructed, and cartilage stress anomaly analysis is carried out through cartilage surface morphological change data; obtaining a synovial fluid function abnormality analysis result and a cartilage stress abnormality analysis result to carry out bone joint function adaptability analysis; and acquiring a bone joint function adaptability analysis result and carrying out synovial fluid function abnormality analysis early warning. The problem that in the prior art, due to the fact that the bionic degree of a detection strategy is insufficient and bone joint dynamic parameters are deficient, the association degree of a static synovial fluid detection result and the actual joint rehabilitation condition of a patient is poor is solved.

The invention discloses a multi-technology combined medication analysis platform of an oligonucleotide drug, which relates to the technical field of drug analysis and is technically characterized by comprising an oligonucleotide drug delivery technology module, a liposome, a polymer nanoparticle or an exosome is selected as a delivery carrier, the surface of the delivery carrier is modified with hydrophilic polyethylene glycol or a targeting ligand, and the oligonucleotide drug delivery technology module is connected with the oligonucleotide drug delivery module. The performance of the material is represented by dynamic light scattering and a transmission electron microscope; the multi-technology platform analysis module integrates RT-qPCR, LC-FL, LC-MS / MS and LC-HRMS technologies, is respectively used for target gene expression quantification, drug distribution tracking, drug principal component quantification and metabolite structure analysis, establishes a cross validation rule, and requires a correlation coefficient R2gt of an LC-MS / MS quantitative result and RT-qPCR expression data; 0.95%, 0.95%; the pharmacokinetic evaluation module constructs a PBPK model to predict human pharmacokinetic parameters, and draws a drug metabolism network diagram in combination with an LC-HRMS metabolite identification result; by integrating various advanced analysis technologies, the problems of low sensitivity, poor specificity and difficult metabolite analysis in the prior art are solved.

The invention belongs to the technical field of bioinformatics and medical data analysis, and discloses a neurodegenerative disease comprehensive analysis platform based on multi-source data fusion, and the platform comprises a data integration module; a gene name conversion module; a core analysis module; a network analysis module; a drug screening module; the omics visualization module systematically catalogs data of genes related to at least 10 main neurodegenerative diseases, 486 drug-derived 3, 957 bioactive components and 18 lifestyle factors through the data integration module, and the data source is wide; according to the method, artificially sorted literature evidence, reanalyzed batch transcriptome data, single-cell RNA sequencing data and standardized data from a public database are covered, new disease targets and potential treatment strategies can be found easily, and the neurodegenerative disease research efficiency and comprehensiveness are improved.

The invention belongs to the technical field of detection and analysis, and provides a method for detecting the average coupling rate of a high-hydrophilicity small-molecule high-glycosylation antibody coupling drug and application of the method for detecting the average coupling rate of the high-hydrophilicity small-molecule high-glycosylation antibody coupling drug. The method comprises the following steps: firstly, carrying out desugaring treatment on a high-glycosylation coupling medicine, then coupling desugarized high-hydrophilicity toxin molecules with an antibody medicine, analyzing by using hydrophilic interaction chromatography conditions, and calculating to obtain the accurate average coupling rate of the antibody coupling medicine. According to the method, through desugaring treatment and hydrophilic interaction chromatography detection in the analysis process, the defect that the separation degree of high-hydrophilicity micromolecules with different coupling ratios is low due to hydrophobicity difference in a traditional method is overcome, so that the measured average coupling rate result is more accurate and real. The method is suitable for coupling a high-hydrophilicity payload drug with a high-glycosylation antibody drug, and is especially suitable for an antibody coupling drug which is high in micromolecule hydrophilicity and has obvious influence on the separation degree of a traditional DAR liquid phase analysis method.

The invention discloses a total homocysteine (Hcy) electrochemical detection method based on metallothionein. The invention relates to the technical field of electrochemical detection. Firstly, metallothionein combined with metal ions is prepared, free Hcy reacts with the metallothionein combined with the metal ions, and the metal ions are released into a solution. And detecting an electrochemical response signal of the metal ions by utilizing an electrochemical analysis technology, making a standard curve of the Hcy concentration and the electrochemical response signal of the metal ions, repeating the operation on a sample to be detected to obtain an electrochemical response signal, and calculating the concentration of the Hcy in the sample according to the standard curve. Only metallothionein needs to be prepared, the metallothionein is small in molecular weight, stable in structure, easy to prepare and low in cost, the metal ion concentration is detected through the electrochemical analysis technology, the method has the advantages of being short in detection time and high in sensitivity, and rapid, accurate, high-sensitivity, simple and low-cost detection of clinical sample total homocysteine is achieved.

The invention belongs to the technical field of tumor molecular diagnosis and biological information analysis, and relates to a high-throughput sequencing method and system for monitoring acute lymphocytic leukemia (MRD). Through targeted sequencing with a unique molecular identifier and / or a double-chain tag, error modeling based on a background noise spectrum and statistics / machine learning pseudo variation filtering, ultra-deep accurate detection of IG / TCR cloning and related gene low-frequency variation is realized. And an artificial intelligence recurrence risk prediction model is established by combining a time sequence MRD index, cloning diversity and clinical information, and a structured clinical report is output and docked with LIS / HIS. According to the method, the sensitivity and the specificity of ALL minimal residual disease detection can be remarkably improved, dynamic evaluation on leukemia cloning evolution and recurrence risks is realized, and a reliable basis is provided for individualized treatment decision and long-term follow-up visit.

The invention belongs to the technical field of biological drug analysis, and particularly relates to a method and a system for quantitatively detecting antibody drugs by using a dimethyl-labeled modified peptide fragment as an internal standard. Sequentially carrying out dimethyl labeling on the enzymolysis peptide fragments of the antibody drugs to obtain a plurality of dimethyl-labeled modified peptide fragments which are used as internal labels; the method comprises the following steps: enriching antibody drugs in a biological sample, and carrying out enzyme digestion to obtain a plurality of unlabeled modified peptide fragments; and mixing the unlabeled modified peptide fragment with an internal standard, enriching the modified peptide fragment, and then carrying out quantitative detection on the modified peptide fragment by adopting liquid chromatography-tandem mass spectrometry. A one-to-one structural analogue internal standard is provided for the modified peptide fragment, and the matrix effect is eliminated. Meanwhile, the method is combined with a high-pH reversed-phase grading equal peptide fragment enrichment method, so that the detection sensitivity of the modified peptide fragments in the matrix is improved. The technology provides a sensitive and reliable quantitative analysis method for the modified peptide fragment, and can be used for quantitative analysis of antibody drugs in biological samples.

The present invention relates to the technical field of tumor immune microenvironment detection and analysis, and discloses an apparatus and method for detecting and analyzing the immune microenvironment of solid tumors. The apparatus for detecting and analyzing the immune microenvironment of solid tumors includes: a detection mechanism: used to contact with a tumor sample or puncture a part of the tumor sample and upload data for operators to analyze; a transportation and cleaning mechanism integrating transportation, cleaning and adjustment functions: cleaning both sides of the sample synchronously during transportation and adapting to different sample sizes; a clamping and turning mechanism: clamping the sample and turning it to position to expose the cleaning surface; a collection component: detachably recovering the shed impurities; a wiping component: used to clean the detection area of the detection mechanism after detection to prevent residual substances from affecting subsequent detection results. The transportation and cleaning mechanism of the present invention can automatically clean the impurities outside the sample while transporting the tumor sample, and can also adjust according to samples of different sizes, improving the accuracy of subsequent detection.

The invention discloses an intelligent typing method and system for M protein in an immunofixation electrophoresis image, and belongs to the technical field of immunofixation electrophoresis image analysis. According to the intelligent typing method and system for M protein in the immunofixation electrophoresis image, an expert knowledge-driven labeling specification of a double-blind labeling process and a labeling verification algorithm embedded into electrophoresis mobility physical constraint is established; according to the method, the image is subjected to standardization processing through the Vahadane dyeing normalization and strip ROI dynamic extraction technology, intelligent noise suppression processing is carried out on image data, the data quality is improved, standardized interpretation can be carried out on the electrophoresis image through a hybrid neural network deep learning algorithm and an AI model, the influence of human factors is reduced, and the method is suitable for being applied to the field of electrophoresis. And the model can reproduce the specificity and accuracy of the expert level. The problems that traditional naked eye interpretation is high in subjectivity, low in accuracy rate, low in efficiency of searching for expert consultation for complex and difficult cases and the like are solved.

The invention discloses a virus gene sequence host prediction method and system, and belongs to the technical field of biological information analysis, and the method comprises the steps: dividing a virus gene sequence into ordered k-mer word sequences, and carrying out the vectorization of the k-mer word sequences through a pre-trained BERT language model, and obtaining an embedded vector; mapping the embedded vector of the high-dimensional space into a low-dimensional space with a fixed dimension by adopting an average pooling method, and reducing the embedded dimension to obtain a feature vector representing an original gene sequence; the feature vectors are input into a pre-trained classification network model to predict the likelihood that the sequence is infected with a particular host. According to the method, the complete virus gene sequence is directly used as input, so that information loss possibly caused by dependence on statistical characteristics is avoided, and the accuracy of characteristic extraction is remarkably improved.

The invention discloses an adversity high-photosynthetic-efficiency transcription factor screening method based on deep learning, and relates to the technical field of biological information analys.The method comprises the steps that rice multi-modal stress response data is obtained and preprocessed, and preprocessed gene expression data is obtained; carrying out differential expression gene screening and co-expression network analysis on the preprocessed gene expression data, extracting multi-modal features, and fusing the multi-modal features to generate a multi-modal input feature matrix; constructing a double-layer deep learning model, training the double-layer deep learning model by using the multi-modal input feature matrix, and respectively outputting a regulation and control relationship matrix of transcription factors and target genes and a regulation and control relationship matrix of transcription factors and target pathways; and according to an output result, calculating a comprehensive score of each transcription factor through a multi-dimensional scoring system, and screening out the stress high-photosynthetic-efficiency transcription factor according to a predetermined screening standard.

The invention relates to the technical field of immunochromatography analysis, and discloses a signal enhancer of a colloidal gold immunochromatography rapid detection card and a preparation method thereof, the signal enhancer comprises a silver ion solution, a thiourea-ascorbic acid synergistic reducing agent, a pH buffer system and a surfactant stabilizing system, the signal amplification is realized by catalyzing and depositing silver nanoparticles on the surface of colloidal gold, the operation is simple and convenient, one-time addition is realized, and the reaction is completed within 5-10 minutes; the sensitivity is improved by 5-10 times, and the specificity is higher than 98%; the stability is excellent, and the storage time is 12 months at 4 DEG C; various colloidal gold rapid detection cards are universally compatible; compared with the prior art, the colloidal gold immunochromatography rapid detection card has the advantages that the cost is controllable, the single detection is increased by 0.3-0.5 yuan, and the colloidal gold immunochromatography rapid detection card is particularly suitable for ultra-sensitive detection of small molecular substances in the field of food safety, for example, the detection limit of malachite green reaches 0.1 microgram per liter, the detection limit of fipronil reaches 0.5 microgram per liter, and the application range of the colloidal gold immunochromatography rapid detection card is obviously expanded.

The invention relates to the technical field of medical data analysis, in particular to a hepatic fibrosis outcome prediction and intervention recommendation method based on dynamic data, which comprises the following steps: collecting biological samples and clinical index data from a target individual at a plurality of continuous time points, and constructing a multi-modal time sequence data set; processing the data set to extract dynamic features reflecting a change process, wherein the dynamic features comprise trend features and event features; inputting the baseline features and the dynamic features of the target individual into a pre-trained hepatic fibrosis outcome prediction model, and outputting hepatic fibrosis index prediction values at a plurality of time points in the future to form a prediction outcome trajectory; when the trajectory meets an early warning condition, matching candidate donors from a pre-constructed donor feature library and generating an intervention recommendation list based on the current state and trajectory features of the target individual; and when new time point data is obtained, updating the data set and repeating the process. According to the method, dynamic and quantitative prediction of the hepatic fibrosis outcome trajectory is realized, and data-driven personalized intervention recommendation is provided.