319 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 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 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 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 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 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.

The invention provides a diabetes development stage identification method and device based on a bio-electricity signal, and relates to the technical field of biological data analysis, and the method comprises the steps: obtaining a bio-electricity signal triple set and diabetes data, the bio-electricity signal triple set comprises a plurality of bio-electricity signal triads, and the diabetes data comprises a diabetes development stage text; obtaining a mapping relation between the bioelectricity signal triple and the diabetes development stage text, and labeling the diabetes development stage text by taking the mapping relation as a label; performing iterative training on the initial diabetes development stage identification model through the bioelectricity signal triple set and the labeled diabetes development stage text set, and obtaining a final diabetes development stage identification model after training is completed; and inputting the to-be-measured bioelectricity signal triple into the final diabetes development stage identification model to carry out diabetes development stage identification so as to obtain a final prediction result of the diabetes development stage.

The invention relates to the technical field of biological research data analysis, and discloses a computational biological research data analysis display method, which comprises the following steps of: extracting total RNA (Ribonucleic Acid) from biological tissues or cells, reversely transcribing the RNA into cDNA (Complementary Deoxyribonucleic Acid), constructing a library, and sequencing the library by utilizing a high-throughput sequencing platform to obtain a large number of short sequences. According to the method, the number of the short sequences mapped to the same genome coordinate interval is compared and the threshold value is judged, so that the abnormal repeated area can be effectively identified, whether the gene expression difference result is credible or not is clearly judged, and when the number of the abnormal sequences exceeds the preset repeated sequence threshold value, the influence operation of the repeated sequences is eliminated, and the accuracy of the gene expression difference result is improved. That is to say, completely identical and overlapped short sequences are removed under the condition that the FPKM value is relatively high, and the updating result is compared again, so that the problem of expression quantity distortion caused by repeated reckoning can be corrected.

The invention discloses 36 pairs of polymorphic primers and 84 pairs of SNP molecular markers used for amplifying the swallow aculeatus, and an application of the polymorphic primers in genetic diversity analysis of the swallow aculeatus, and particularly discloses an application of the polymorphic primers in the genetic diversity analysis of the swallow aculeatus and application of the polymorphic primers in the genetic diversity analysis of the swallow aculeatus, the number of the polymorphic primers is 36, and the number of the swallow aculeatus SNP molecular markers used for amplifying the 36 pairs of the primers is 84. According to the invention, the SNP molecular markers with abundant polymorphism are established on the swallow-wing fish in batches for the first time, 36 pairs of polymorphic primers are screened out, and an efficient analysis technical tool is provided for research on genetic diversity, family identification, population genetic structure, genetic breeding, enhancement and release and the like of the swallow-wing fish; the method for developing the SNP marker is simple, convenient, rapid and efficient, and manpower and cost are saved.

The application discloses a fish scale protein polypeptide anti-chronic heart failure efficacy evaluation system, relates to the technical field of biological active polypeptide evaluation and cardiovascular disease function analysis, and comprises a raw material processing module for preparing fish scale protein polypeptides, a CHF model construction module for animal experiments, a function detection module, and a data processing and analysis module. Multidimensional statistics and visual algorithm are adopted to integrate tissue remodeling indexes such as heart function, oxidative stress and inflammatory reaction, so that comprehensive evaluation of the efficacy of fish scale protein polypeptides is realized. The application can realize systematic, quantitative and mechanistic evaluation of the anti-chronic heart failure effect of fish scale protein polypeptides, solve the problems of single evaluation index, poor repeatability of results and lack of mechanism correlation in the prior art, and has the advantages of comprehensive evaluation, reliable results, wide application range and strong generalizability.

The invention discloses an electrochemical aptamer sensor for detecting kanamycin as well as a preparation method and application of the electrochemical aptamer sensor, and belongs to the technical field of electrochemical biosensing analysis. According to the invention, a gold electrode assembled with a kanamycin aptamer with sulfydryl is taken as a sensing interface, a target molecule kanamycin is combined to the sensing interface, and a silver nanoparticle-zirconium-based metal organic framework material signal probe is combined with kanamycin on the sensing interface, so that the sandwich type kanamycin electrochemical aptamer sensor is constructed. The kanamycin electrochemical aptamer sensor prepared by the invention has relatively high sensitivity, relatively low detection limit and relatively wide detection range, the detection limit of the sensor is 0.085 aM, and the detection range is 0.1 aM <-1 > nM. The electrochemical aptamer sensor prepared by the invention is used for quantitative detection of kanamycin, and has the advantages of good stability, strong specificity, good reproducibility and the like.

The invention belongs to the technical field of biological detection and analysis, and particularly relates to a high-fidelity biotinylation probe as well as a preparation method and application thereof in polysulfide detection. Specifically, the invention develops a novel biotinylation probe HPB, and the probe is formed by coupling HPE-IAM and biotin. Experimental results show that HPB is superior to IAB and other traditional reagents in the aspects of maintaining and efficiently labeling polysulfide, and a high-specificity solution is provided for Pr-SnH analysis at the proteomics level. By optimizing a marking scheme and introducing a precipitation-redissolution step, the detection accuracy of the polysulfide modified protein is further improved, so that a reliable tool is provided for protein polysulfide modification detection research under a complex cell background, and the method has a good practical application value.

This invention provides a method for constructing a fingerprint spectrum of the Astragalus-Hirudo-Tongluo formula and a method for quality evaluation of its capsules, belonging to the field of traditional Chinese medicine analysis technology. This invention sequentially detects samples using an ultraviolet detector and an evaporative light scattering detector. Under the same chromatographic conditions, using two different detectors for the same sample allows for the simultaneous acquisition of fingerprint spectra of compounds with both strong and weak ultraviolet absorption. This method maximizes the detection of chemical components in the Astragalus-Hirudo-Tongluo formula while saving time and effort. The method is simple, easy to implement, highly sensitive, specific, and robust. Based on the fingerprint spectrum method, this invention identifies chromatographic peaks, thereby achieving better quality control of the Astragalus-Hirudo-Tongluo formula.

The application provides an explainable analysis method and system for burn prediction results, and belongs to the technical field of intelligent burn analysis. The method is to set a target result, continuously adjust biomarkers corresponding to the burn prediction result, obtain improved biomarkers, use a random forest model to predict the improved biomarkers to obtain improved results, continuously adjust the biomarkers corresponding to the burn prediction result until the improvement target is reached, and then explain and analyze the burn prediction result according to the improvement target. The technical problem that the existing technology cannot perform explainable analysis on the grading and typing prediction results by using the machine learning method is solved, a clinically understandable decision basis is formed, and systematic computing support is provided for burn diagnosis and treatment.

The invention provides a papillary thyroid carcinoma neck lymph node metastasis risk prediction method and system based on blood indexes and TI-RADS grading, and relates to the technical field of medical data analysis. According to the method, an original data set is constructed by obtaining TI-RADS classification, the maximum diameter of nodules, the number of nodules, gender, age and conventional hematology indexes including apolipoprotein B and carcino-embryonic antigen of a patient, characteristic variables are screened by adopting LASSO regression, and an independent prediction risk factor model is established in combination with multi-factor Logistic regression. And further constructing a column graph model, carrying out performance verification through multiple statistical indexes, and deploying the model to a webpage calculator based on a shiyapp to realize convenient output of the individualized risk probability. According to the method, efficient and accurate prediction of the preoperative lymph node metastasis risk is realized, and reliable decision support can be provided for clinic.

The invention discloses a fractionation method of an endocrine disrupter in an environmental water body, and belongs to the technical field of water body pollutant analysis. The fractionation method of the endocrine disrupter in the environmental water body comprises the following steps: S1, enriching and concentrating a water sample to obtain a to-be-fractionated liquid; s2, fractionating the to-be-fractionated liquid by adopting an analytical liquid chromatography system and a C18 semi-preparative chromatographic column to obtain fractions. According to the method, high-resolution synchronous fractionation of the endocrine disrupter in a wide polarity range (Log Kw:-0.19-6.62) is realized, the defects of low fractionation efficiency and high solvent consumption of the traditional semi-preparative liquid chromatography are effectively overcome by optimizing a gradient program of an analytical liquid chromatography system, the time and economic cost are saved, and the method is suitable for industrial production. And the fraction can be directly compatible with biological activity test after salting-out-extraction treatment.

The invention belongs to the technical field of information analysis, and particularly relates to a whole genome methylation region splitting method based on a co-methylation state of adjacent CpG loci. On the basis of whole genome methylation sequencing data, methylation regions are split within a whole genome range according to the co-methylation state of adjacent CpG sites, more methylation region information is provided for diagnosis of diseases such as cancers, and the effect of improving the methylation signal-to-noise ratio of the diseases such as the cancers is achieved.