88 results about "Disease mechanisms" patented technology

The invention provides a protein methylation site prediction method fusing quantum and region perception, which comprises the following steps: step 1, acquiring a protein sequence as a data source, and respectively constructing a training set and an independent test set; 2, constructing a multi-modal feature for each protein sequence by adopting a three-way nested scattering network, and fusing the multi-modal features to obtain an optimized fusion feature tensor; and step 3, inputting the optimized fusion feature tensor into a RaQMeNet network model, and performing a methylation site prediction task. The performance indexes of the method are greatly superior to those of the prior art, and the method has higher adaptability, stability and interpretability, can be widely applied to a plurality of bioinformatics and biological medicine related fields such as protein function annotation, disease mechanism research and drug target discovery, and has good application prospects and commercial values.

The invention discloses a differential gene regulation and control network reconstruction method based on mutual information and a redundancy regulation and control filtering system. The method comprises the following steps: firstly, collecting gene expression data under two or more conditions, respectively calculating mutual information of gene pairs under each condition, and screening the gene pairs with obvious mutual information difference as candidate regulation edges; furthermore, a redundancy regulation and control relation caused by the intermediary variables is identified and eliminated through calculation condition mutual information, and finally a difference regulation and control network with high credibility is constructed. The system comprises a data preprocessing module, a mutual information calculation module, a redundancy indirect regulation and control effect filtering module, a regulation and control direction judgment module, a network construction and threshold optimization module, a difference network construction module and an output and visualization module. The method can effectively improve the biological interpretability and inference accuracy of the network, and is widely applied to the bioinformatics fields such as disease mechanism research, regulatory factor identification and multi-omics integrated analysis.

The invention provides a brain disease drug curative effect analysis and prediction system and method, and relates to the technical field of drug analysis. Multi-source heterogeneous data such as genetic variation spectrum, neural image topological characteristics, metabolic trajectory vectors and microbiome dynamic distribution matrix are integrated, and cross-modal dynamic fusion is realized through decomposition. The system constructs a drug prediction coupling network based on dynamic causal inference, analyzes overlapped potential intervention nodes of multiple disease mechanisms, and generates a disease-drug-phenotype multi-dimensional mapping library. A short-term drug response toxicity threshold is predicted through a metabolic entropy change model, a long-term neurological function degeneration trajectory is evaluated in combination with epigenetic drift, and an anti-fact strategy gradient is utilized to dynamically optimize a drug administration scheme. According to the invention, accurate fusion and dynamic analysis of multi-modal data are realized, a balance mechanism of curative effect and risk is established, and accurate drug combination sequence and administration strategy support can be provided for individualized treatment of complex brain diseases.

The invention relates to the technical field of biology, and discloses a construction method of a gastric cancer organoid culture system, which comprises the following steps: preparation of a conditioned medium: culturing L-WRN cells and collecting the conditioned medium; preparing a gastric cancer organoid culture medium, and mixing the conditioned culture medium, the basic culture medium, a plurality of growth factors and additives; and extraction and culture of organoid: separating cells from gastric cancer tissues, mixing the cells with matrigel, and culturing by using the prepared culture medium. According to the method, the repeatability and the stability of the organoid model are improved, the response consistency of the organoid in drug screening is improved, and the reliability of the model in disease mechanism research and personalized medical application is guaranteed.

The invention provides an ovarian cancer monitoring system based on iron metabolism related protein, comprising: an information monitoring module for monitoring iron metabolism protein index data and ovarian cancer core biomarker index data of a user; the abnormality determination module is used for comparing the iron metabolism protein index data and the ovarian cancer core biomarker index data with historical personal baselines of the current ovarian cancer development stage of the user, determining the variation amplitude, and triggering the ovarian cancer analysis module to perform analysis when the variation amplitude meets preset requirements; and the ovarian cancer analysis module is used for updating the ovarian cancer risk index grade based on the iron metabolism protein index data of the user and the ovarian cancer core biomarker data. According to the invention, the iron metabolism protein index is combined with the ovarian cancer core biomarker, and the biological characteristic of iron-dependent proliferation of the ovarian cancer cells is utilized, so that an index basis which is more suitable for a disease mechanism is provided for monitoring, and the possibility of early abnormality capture is improved.

This invention belongs to the field of vertebrate technology, specifically relating to a method for constructing a complex inflammatory animal model in middle-aged and elderly individuals and its related applications. The method includes: selecting adult animals for adaptive feeding, daily subcutaneous injection of D-galactose to establish a susceptibility to aging and oxidative stress, and administering sodium dextran sulfate (DSS) solution within a specific time window to induce intestinal inflammation, forming a complex inflammatory state through gut-hepatic axis interactions. The constructed animal model simultaneously exhibits intestinal inflammatory damage, abnormal hepatic oxidative stress, and aging-related characteristics, realistically simulating the pathological process of gut-hepatic axis dysfunction in middle-aged and elderly individuals. This model is suitable for screening and evaluating drugs, medical nutrition products, and functional foods that improve gut-hepatic axis dysfunction, providing a reliable technical platform for research on related disease mechanisms and the development of interventional substances.

The invention discloses a culture medium for colonization of intestinal flora and intestinal gas of people with hyperuricemia. The culture medium contains yeast extract, ethanol, adenine, tryptone, mucoprotein, mycoprotein, insect protein, soybean protein, fructose, glucose, inulin, resistant dextrin, dietary fiber, oxalic acid, saturated fatty acid, sialic acid, peanut oil, potassium chloride, sodium chloride, monopotassium phosphate, sodium sulfate, bile salt, cysteine hydrochloride and a plant extract mixture. Vitamin mixed liquid and the like. By means of the bionic colon reactor, by referring to real in-vivo parameters of people suffering from hyperuricemia, the real intestinal environment of people suffering from hyperuricemia in China is well simulated in vitro, the intestinal flora ecology and intestinal gas distribution are reproduced, and a normal intestinal microorganism and intestinal gas database of people suffering from hyperuricemia can be constructed. The method is beneficial to accurate diagnosis of intestinal microecology of people with hyperuricemia, individualized treatment of antisense booking and disease mechanisms, better maintenance of intestinal health and formulation of preventive measures.

The invention discloses a single molecule positioning method based on deep learning feature fusion, and the method comprises the following modules: a data processing module which uses an analog number sample generated by imageJ as a training set, and converts a generated positioning table into a corresponding image as a verification set; the Resnet module is used for extracting the characteristics of the data; the ASF module is a feature fusion module and is used for fusing features of different scales according to different weights; and the attention mechanism module can selectively pay attention to the positioning information of the single molecule and is beneficial to realizing more accurate positioning on the single molecule. According to the technology, the super-resolution positioning of the single molecule is completed through the deep learning network, and the accurate positioning of the single molecule image can be quickly formed. The technology not only is helpful to research and track experiments of single molecule dynamic behaviors, but also has a wide prospect in the aspects of assisting accurate analysis and early diagnosis of disease mechanisms.

The invention relates to the technical field of medical education and biological information visualization, in particular to a disease mechanism-associated protein structure and function visualization teaching virtual simulation system, which comprises a data storage module, a structure calling visualization module, a function-disease association module, an interactive operation module and a teaching evaluation module, all the modules interact through data interfaces. According to the protein structure and function visual teaching virtual simulation system associated with the disease mechanism, deep association of a protein three-dimensional structure, a function mechanism and a disease case is realized for the first time, the knowledge splitting barrier of traditional teaching is broken, and students are helped to construct a complete cognitive chain; autonomous operation and mutation simulation are supported, an abstract structure function relation is converted into an interactive visual model, the learning difficulty is remarkably reduced, and the knowledge absorption rate is increased; an experiment task and evaluation system is built in, a teaching closed loop of preview-operation-assessment-feedback is achieved, and the requirement for large-scale medical talent training is met.

The invention relates to the technical field of molecular immunology and biology, in particular to a sicca syndrome model mouse B cell immune repertoire sequencing and subpopulation analysis method. The method comprises the following steps: S1, sample preparation: preparing a single-cell suspension from a target autoimmune disease animal model and a preset tissue of a control animal, and carrying out B cell enrichment treatment on the single-cell suspension to obtain a B cell sample; s2, multi-dimensional data parallel analysis: analyzing the B cell sample, and performing parallel construction of the multi-dimensional data; and S3, integrated correlation analysis: carrying out correlation analysis on the immune group library data, the cell subset data and the signal channel data obtained in the step S2 to establish a correlation relationship among specific B cell cloning characteristics, specific B cell subset changes and specific signal channel activity, and carrying out multilevel analysis and generating a B cell immune response mechanism of the autoimmune disease. The efficiency and accuracy of disease mechanism research and drug action mechanism evaluation are remarkably improved.

Embodiments of the instant disclosure relate to an in vitro system for generating 3D skin equivalent, compositions, and methods for modeling and analyzing potential therapeutic treatments of Ehlers-Danlos Syndrome (EDS), including personalized therapies and regimens. In certain embodiments, the in vitro system can be utilized to facilitate diagnosis, drug discovery, investigation of disease mechanisms, and treatment of EDS and other connective tissue diseases. In certain embodiments, compositions, and methods for making and using matrix metalloproteinase 9 (MMP-9) inhibitors are disclosed to treat EDS. In some embodiments, a subject has hypermobility type EDS (hEDS).

The invention relates to the technical field of molecular biology and precision medical treatment, and relates to a molecular marker for predicting FOLFOX chemosensitivity and application thereof. A patient-derived colorectal cancer organoid biological sample library is constructed, the heterogeneity of in-vitro drug reaction is analyzed and evaluated through transcriptome, clinical groups sensitive to FOLFOX chemotherapy can be accurately recognized, a molecular marker prediction system containing 11 genes is obtained through further screening, and the molecular marker prediction system is used for predicting the FOLFOX chemotherapy. The method effectively overcomes the technical defect of lack of accurate prediction of colorectal cancer chemosensitivity at present, and has important value in the aspects of revealing disease mechanisms and guiding personalized treatment.

The system according to this embodiment aims to analyze gene expression data, predict disease mechanisms, and generate new research hypotheses. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a prediction unit, a hypothesis generation unit, a treatment method proposal unit, and a report generation unit. The acquisition unit acquires gene expression data. The analysis unit analyzes the gene expression data acquired by the acquisition unit. The prediction unit predicts the disease mechanism based on the data analyzed by the analysis unit. The hypothesis generation unit generates a new research hypothesis based on the prediction results obtained by the prediction unit. The treatment method proposal unit proposes the optimal treatment method based on the hypothesis generated by the hypothesis generation unit. The report generation unit generates an experimental result as a report based on the treatment method proposed by the treatment method proposal unit.

The separation and detection method comprises the following steps: rinsing placenta or villus tissues in a PBS (Phosphate Buffer Solution) added with a 1% penicillin-streptomycin double antibody solution, digesting the placenta or villus tissues into single cells by using a 1640 culture medium containing I-type and IV-type collagenase after the placenta or villus tissues are rinsed sufficiently to obtain a cell suspension, and culturing the cell suspension for later use; diluting and neutralizing collagenase digestive juice by using a 1640 culture medium containing high-temperature heat-inactivated fetal calf serum according to 5 times of volume; repeatedly discarding the precipitate, recovering the supernatant, and centrifuging to obtain a coarse migration body; and establishing a density gradient by using Optiprep as a density medium, washing the precipitate with PBS, and centrifuging to obtain the trophoblast cell-derived transporter. The invention provides a group of placenta villus trophoblast cell source migration body specific expression membrane proteins for the first time, and provides a method for specifically capturing the placenta trophoblast source migration body on the basis of the placenta villus trophoblast cell source migration body specific expression membrane proteins, so that a new direction for researching a trophoblast dysfunction related disease mechanism can be enriched.

This invention relates to a method for disease therapeutic target discovery and drug prediction based on multi-omics networks and deep learning models, belonging to the interdisciplinary field of bioinformatics and artificial intelligence drug discovery. The method includes: integrating genomic expression profiles and common molecular interaction data from disease and control groups to construct a candidate whole-genome network; refining the network based on expression profile data through systematic modeling and the AIC criterion to obtain the real molecular interaction network; extracting the core network using the master network projection method and identifying key targets through pathway analysis; predicting candidate drugs interacting with the targets using a pre-trained deep neural network model; and finally screening potential therapeutic drugs based on multi-dimensional criteria such as regulatory ability, sensitivity, and toxicity. This invention achieves a complete integration from disease mechanism analysis to drug prediction, and is particularly suitable for complex diseases such as atopic dermatitis. It can systematically discover precise targets and efficiently predict repositionable drugs, significantly improving R&D efficiency.

The invention discloses a state recognition method of an adaptive gene editing laboratory mouse, and aims to solve the problem of insufficient state recognition specificity of the gene editing laboratory mouse in the prior art. The method comprises the following steps: acquiring genotypes, disease model types and experimental environment parameters of laboratory mice; optimizing detection and optical flow analysis parameters, generating a time sequence behavior track map and segmenting a key area; extracting general and pathological specific composite features and standardizing the general and pathological specific composite features; calling a genotype pathological feature association database to calibrate a weight and a threshold value, and inputting a random forest classifier recognition state of transfer learning optimization; scores are calculated through a weighted scoring model, the state is judged, and credibility is calculated through a multi-dimensional confidence evaluation model. The method is suitable for various gene editing laboratory mice and experimental environments, the accuracy and suitability of state recognition are improved, and powerful support is provided for gene therapy research and development and disease mechanism research.

The application discloses a virtual cell analysis platform based on cell disturbance data. The platform comprises three parts: a large language model data preprocessing system, a database and an online analysis platform, and a direction matching algorithm. The large language model data preprocessing system comprises an automatic data information extraction and retrieval tool, an automatic data file download tool, a large language model analysis engine and a gene expression data acquisition and feature extraction tool. The application can automatically preprocess the original data by applying the large language model data preprocessing system to the target scientific problem, and then store the data in the database. The characteristic direction matching algorithm is combined to output the drugs and key targets related to the target data set. The efficiency of target point and drug screening is improved. The application is suitable for large-scale omics data mining and disturbance analysis, accelerates the drug research and development, disease mechanism and translational medicine research process, and reduces the research and development cost.

The invention provides a biological sample system for clinical tests. Comprising a sample identification and tracking end, a sample acquisition and processing planning end and a sample storage and inventory management end. The biological sample system plays an important role in a clinical test, and the biological sample system for the clinical test ensures the quality and traceability of a sample and the reliability of data. According to the biological sample system for the clinical test, by effectively managing biological samples, researchers can obtain accurate test results, better understand disease mechanisms, evaluate drug curative effects, find new biomarkers and the like. In addition, the biological sample system should follow ethical specifications and supervision requirements, and rights and interests and privacy of test participants are protected.

The application discloses a virtual cell analysis platform based on cell disturbance data. The platform comprises three parts: a large language model data preprocessing system, a database and an online analysis platform, and a direction matching algorithm. The large language model data preprocessing system comprises an automatic data information extraction and retrieval tool, an automatic data file download tool, a large language model analysis engine and a gene expression data acquisition and feature extraction tool. The application can automatically preprocess the original data by applying the large language model data preprocessing system to the target scientific problem, and then store the data in the database. The characteristic direction matching algorithm is combined to output the drugs and key targets related to the target data set. The efficiency of target point and drug screening is improved. The application is suitable for large-scale omics data mining and disturbance analysis, accelerates the drug research and development, disease mechanism and translational medicine research process, and reduces the research and development cost.

The invention discloses application of double-mutant zebrafish in preparation of a mast cell function defect animal model. The double-mutant zebra fish disclosed by the invention is cpa1- / -cpa5- / -double-mutant zebra fish, and is obtained by further knocking out a cpa1 gene on the basis of a cpa5- / -zebra fish mutant. According to the method, potential compensation effects of two key carboxypeptidases are eliminated at the same time, and the storage and release capacity of mast cell protease is remarkably weakened from the genetics level, so that the zebrafish animal model with the mast cell function defect, which is clear in phenotype and stable in function, is obtained. The animal model can be used for mast cell function mechanism research, related disease mechanism exploration and drug screening and evaluation, and has a good application prospect.

This invention relates to a method for constructing a polycystic kidney disease (PCD) model and its applications. The method involves overexpressing the MYCN gene in a target animal to obtain a PCD model that leads to PCD-related phenotypes. The method includes the following steps: obtaining a first strain of mice with Rosa26 knock-in overexpressing the CAG-LSL-HA tag-MYCN-IRES-BFP-Wpre-polyA gene; obtaining a second strain of mice by inserting Cre-WPRE-polyA into the start codon of the Pax8 gene; and crossing the first and second strains of mice to obtain a MYCN-overexpressing PCD model. This invention employs various experimental methods for validation, including histopathological analysis, immunohistochemical staining, and Western blotting. The model provided by this invention overcomes the limitations of existing in vitro cell models, organoids, and existing animal models in terms of limited phenotypes. The established MYCN-overexpressing PCD animal model exhibits stable disease progression and a short disease cycle, which not only helps to elucidate the disease mechanism but also serves as an ideal platform for drug screening and efficacy evaluation.

This invention relates to an in vitro method for mapping the spatiotemporal electrophysiological dynamics and spatial transcriptional profiles of cells in a functional neuronal cell ensemble. This in vitro method includes simultaneously recording and analyzing spatial data of molecular activity and electrical network activity at the level down to individual cells using high-density electrobiosensors, spatial transcriptomics, optical imaging, and advanced computational strategies. The invention also relates to: methods for identifying the composition of a functional neuronal ensemble; methods for monitoring the spatiotemporal electrophysiological dynamics and transcriptional profiles of cells in a functional neuronal cell ensemble; methods for monitoring the cellular composition of a functional neuronal ensemble; methods for identifying compounds that influence spatial electrophysiological and transcriptional dynamics; and / or methods for identifying the cellular composition of a functional neuronal cell ensemble based on detected dynamics; and devices thereof. This invention allows for a better understanding of disease mechanisms, the identification of therapeutic targets, and the development of new drugs and treatment regimens.

The invention relates to the field of biomedical detection, and discloses a real-time detection device for bacteria-cell interaction gas and application of the real-time detection device in oral cancer marker traceability. The device comprises a co-culture container, the co-culture container is an open container, and a sealing piece is arranged at the open end; the puncture detection module comprises a puncture probe and a detector, during detection, one end of the puncture probe penetrates through the sealing piece, and the other end of the puncture probe is connected with the detector; the temperature control module is used for maintaining the culture temperature in the co-culture container. The device provided by the invention fundamentally solves the industrial problem of in-situ detection of gas generated by bacteria-cell interaction, can be used for establishing a quantifiable marker-disease mechanism correlation model, and promotes the development of accurate diagnosis.

The invention provides a construction method of a CETN3 knockout cell line, a human brain organ with microhead deformity and application of the human brain organ, and belongs to the field of organoids. The CETN3-deficient stem cells prepared by the method provided by the invention still have normal multiplication capacity and differentiation potential, do not influence the development of organoid, can still develop into organoid with good morphology, but have reduced volume, which is similar to phenotypes appearing in small head malformation patients. The CETN3 knockout embryonic stem cell line is utilized, the cells can be self-organized into a structure similar to the human cerebral cortex through nerve induction and 3D culture, and the phenotype of a small head malformation patient is simulated. By utilizing the organ, the research on the disease mechanism can be promoted, and the medicine can be screened more effectively and conveniently.

The invention discloses a method for predicting annular RNA-disease potential correlation by using multi-source information. According to the method, a deep learning framework is constructed based on a multi-view graph convolutional neural network (GCN) and a biased random walk algorithm node2vec. Specifically, the method comprises the following steps: firstly, integrating disease semantic information, circular RNA function information and various similarity indexes, and constructing a multi-view disease and circular RNA similarity graph; then, a multi-channel GCN module is adopted to extract node embedding in the local graph structure, and dimension reduction is carried out through a PCA algorithm to extract common features; thirdly, a heterogeneous graph fusing the similarity and the known incidence relation is constructed according to the method, biased random walk is carried out through node2vec, and global feature embedding is achieved; and finally, the potential association score between the circular RNA and the disease is output through the joint coding information of a bilinear decoder. According to the method, local structure information and global topological information are effectively fused, the prediction performance is remarkably improved, and the method is suitable for identifying the correlation between potential circRNA and diseases. Experimental results of the method on four public data sets are remarkably superior to those in the prior art, and the method has high accuracy, high robustness and good generalization ability and is suitable for life medicine fields such as disease mechanism research and target screening.

The invention discloses a lung tissue organoid culture method. The method comprises the following steps: firstly, carrying out three-dimensional differentiation culture on matrigel by utilizing lung basal cells in vitro to construct an organoid with alveolar epithelium characteristics; then, the organ is transplanted into the spleen of a recipient animal subjected to ectopic surgery (shifted to subcutaneous tissues). The rich blood vessel network and the unique microenvironment of the spleen greatly promote rapid vascularization, structural remodeling and functional maturation of organoids, and lung tissue highly simulating natural alveolus is formed. The invention overcomes the defects of insufficient vascularization and immature function of the existing organ-like model, provides an excellent in-vivo platform for researching lung development and disease mechanisms and carrying out drug screening and toxicological evaluation, and has important application value in the field of regenerative medicine.

This invention discloses a monoclonal antibody against Oit3 protein in mouse hepatic sinusoidal endothelial cells or its antigen-binding fragment and its applications, belonging to the fields of biotechnology and medical immunology. The monoclonal antibody or its antigen-binding fragment comprises a heavy chain variable region and a light chain variable region. The heavy chain variable region contains HCDR1, HCDR2, and HCDR3 with amino acid sequences as shown in SEQ ID NO. 3~SEQ ID NO. 5; the light chain variable region contains LCDR1, LCDR2, and LCDR3 with amino acid sequences as shown in SEQ ID NO. 8~SEQ ID NO. 10. This monoclonal antibody and the recombinant fluorescent antibody exhibit high affinity and high specificity, and can be effectively applied to enzyme-linked immunosorbent assay (ELISA), Western blotting, and immunofluorescence detection. They can serve as important antibody tools for basic research on mouse hepatic sinusoidal endothelial cells and for exploring the mechanisms of liver diseases. Based on its variable region sequence, a recombinant fluorescent antibody, Oit3-scFv-GFP, was also constructed. This recombinant fluorescent antibody enables one-step direct immunofluorescence staining, providing a convenient tool for in situ visualization of mouse hepatic sinusoidal endothelial cells.

The present invention discloses a method for screening biomarkers and related applications thereof, and relates to the field of biological detection technology. The present invention provides a method for screening biomarkers, which does not require the detection of the metabolites of the strains by measuring instruments, but directly predicts the metabolites of each bacterial species by functional prediction of the strain genome sequence, and forms a metabolite sequence library. Subsequently, the metagenomic sequencing data is used to compare with the strain sequence library and the metabolite sequence library respectively, and the abundance of each bacterial species and the abundance of each metabolite are calculated. Then, based on the abundance of the bacterial species and the abundance of the metabolites, as well as the correspondence between the metabolites and the bacterial species, the bacterial species are screened. Even without actually measuring the metabolites, screening can be based on both the bacterial species and the metabolites. This significantly reduces the time and cost of developing markers for disease diagnosis and efficacy prediction and drug screening, and provides a new approach for the study of disease mechanisms and drug development.

The application provides an animal brain metabolite identification method based on mass spectrometry imaging technology and application, and establishes a mouse brain tissue slice endogenous metabolite DESI-MSI analysis method based on mass spectrometry imaging technology, which comprises the following steps: after preparing metabolite control sample imaging samples and brain tissue slice imaging samples, multiple animal brain tissues are attached to the same glass slide, mass spectrometry imaging condition optimization is carried out by using the control sample imaging samples, and mass spectrometry imaging analysis is carried out by using the brain tissue slice imaging samples; the image of the biological sample is divided into multiple structure regions in combination with tissue morphological characteristics; for the structure regions, multivariate statistical analysis is carried out to obtain different metabolites in different structure regions; metabolite identification is carried out based on parent ions and characteristic daughter ion profiles; and the method is used for disease mechanism research and drug intervention effect evaluation on diseases.

The invention discloses a functional phosphorylation modification site screening method based on base editing, which is used for accurately identifying functional phosphorylation modification sites. According to the method, a cell line (such as human gastric cancer cells AGS / HGC27) for stably expressing ABE is constructed, an sgRNA library (containing 39 and 689 sites) covering phosphorylation sites of a whole genome is designed, screening is carried out under the conditions of oxaliplatin and the like, and the technical problem that more than 95% of phosphorylation sites are unknown in function is solved. According to the method, key sites (such as WEE1 S53) related to gastric cancer chemotherapy drug resistance are successfully recognized, the synergistic effect of oxaliplatin and WEE1 inhibitor combination is verified through experiments, and an innovative platform is provided for disease mechanism research and drug development.