236 results about "Precision medicine" patented technology

A spatial omics multi-modal fusion method under a single cell level comprises the following steps: extracting spatial morphological characteristics of differential expression genes and cell nucleuses from spatial transcriptome data, single cell sequencing data and histological images, and realizing field adaptation among different platforms by using a conditional variation auto-encoder. And based on a probability inference model, fusing spatial transcriptome expression, unicellular omics and morphological characteristics, and jointly inferring the type and gene expression level of each cell. A spatial cell network is constructed through a graph attention mechanism, and spatial diffusion and recognition of cell types in a full slice range are realized. In combination with a multi-omics enhancement module, undetected gene and protein expression is completed based on expression similarity, and prediction consistency is improved through spatial correction. According to the method, high-resolution reconstruction of single-cell multi-omics information in a three-dimensional space is realized, the information coverage and spatial resolution of spatial omics data are improved, and an efficient and low-cost solution is provided for spatial biology and precise medical research.

The invention relates to the technical field of biomedicine, in particular to a hepatic cell differentiation degree evaluation method based on multi-omics data, which comprises the steps of sample collection and preprocessing, transcriptomics, proteomics and metabonomics data analysis, multi-omics data integration and modeling and result output. By integrating multi-level biological information, a multi-dimensional scoring model is constructed, the liver cell differentiation state is quantitatively evaluated, and a standardized grading system is provided for clinic. The method can solve the limitation of single omics analysis, improves the evaluation accuracy and reliability, has universality, can be popularized to other malignant tumor research, and assists precise medical development.

The invention discloses a tumor personalized drug recommendation method and system based on fusion of multiple clinical guidelines, and belongs to the technical field of bioinformatics and precision medicine. The method comprises the following steps: acquiring gene variation data and clinical feature information of a patient; generating a preliminary drug candidate list based on drug recommendation rules of a plurality of clinical guidelines; calculating the weight of each guide by adopting a dynamic weight distribution algorithm; obtaining drug-gene-disease associated information through multi-hop reasoning of the knowledge graph; calculating a drug evidence score by adopting a multi-dimensional scoring algorithm; carrying out personalized score adjustment in combination with individual features of the patient; and outputting a personalized drug recommendation result. According to the method, through multi-guide dynamic fusion, multi-dimensional evidence scoring, knowledge graph reasoning and personalized adjustment, the problems of incomplete guide coverage, lack of personalization, low response speed and the like in the prior art are solved, the method has the advantages of high accuracy, high clinical applicability, quick response and the like, and the clinical real-time decision-making requirement can be met.

The invention provides a gestational diabetes risk prediction system and method based on multi-modal data fusion, and the system comprises a data collection module which is used for integrating clinical indexes and medical record text data; the data preprocessing module converts the multimode data into numerical values and text variables which can be used for modeling; the variable screening module is used for extracting data features by adopting LASSO regression in combination with a recursive feature elimination algorithm and a Clinical-BERT model; the data prediction module is used for constructing a GDM risk prediction model through a dual-channel calculation unit and a fusion unit, generating an accurate risk probability and providing an interpretable clinical index in combination with an SHAP value; and a prediction result is output through the output module in the forms of a dynamic column diagram, a webpage calculator and an API interface, so that clinical operation and application are facilitated. According to the method, the limitation of a traditional prediction method is broken through, the prediction precision and the real-time monitoring capability are improved, and the development of precise medical treatment is promoted.

The invention provides a cross-omics sparse feature selection system and method based on hierarchical causal modeling, and the system comprises a data input and preprocessing module which is used for receiving multi-omics original data of a multivariate sample; the hierarchical causal structure learning module is connected with the data input and adaptive preprocessing module and is used for constructing a cross-omics hierarchical causal topology; the causal-oriented sparse feature selection module is connected with the hierarchical causal structure learning module; and the model retraining and integration module is used for constructing a three-layer weighted integration discrimination model based on the screened markers, optimizing the fusion weight of each layer through a gradient descent algorithm, and outputting a final prediction result. According to the method, the protein-metabolism biological hierarchy relationship and serum-urine complementary information are fully utilized, and the method has good generalization ability and can be widely applied to marker mining and prediction modeling of cancers, metabolic diseases and the like, so that the accuracy and reliability of precise medical treatment are improved.

The present invention relates to a use of THBS1 as a novel combination drug target that can overcome drug resistance of a targeted anticancer agent. A THBS1 inhibitor according to the present invention inhibits the drug resistance of a target anticancer agent and thus increases an anticancer effect when administered in combination with a target anticancer agent. Accordingly, the present invention can overcome resistance to a targeted anticancer agent and increase a treatment success rate of anticancer drugs for cancer patients, thereby suggesting new possibilities for treatment strategies using targeted anticancer drugs and contributing to the realization of precision medicine.

The invention discloses a multi-omics cancer subtype recognition method, system and device based on density sensing cluster structure guide contrast learning and a medium, and belongs to the technical field of bioinformatics and artificial intelligence crossing. The method comprises the following steps: acquiring and preprocessing multi-omics data; constructing an omics specific auto-encoder and learning potential representation; constructing a density sensing cluster block in the potential space; constructing a cross-omics positive and negative sample pair based on cluster block sample overlapping; difficult negative sample mining; constructing a cluster block level cross-omics contrast learning target, and training and updating; a self-supervised soft refinement mechanism is introduced to dynamically enhance a cluster structure; and carrying out multi-loss joint optimization and model iteration training. According to the method, the robustness and the stability of a cancer subtype recognition result can be improved, high-dimensional, multi-source and multi-noise multi-omics data can be efficiently modeled and analyzed, good generalization ability and application potential are achieved, and reliable technical support can be provided for cancer subtype research, patient stratified analysis and precise medical aid decision making.

The invention discloses a multi-omics data integration and classification method, system and device based on hierarchical attention, and is applied to the field of precise medical big data analysis. The method comprises the following steps: firstly, generating feature embedding and feature importance scores through a plurality of parallel feature-level attention modules; then, embedding and inputting all the characteristics of the omics into a unified omics-level attention module, and generating omics embedding and omics importance scores; and finally, a classification prediction task is executed based on omics embedding, and a classification result is output for disease classification. The invention completely abandons a traditional dependency graph convolutional network and an integration normal form of variants of the dependency graph convolutional network, and provides a universal hierarchical attention integration architecture. The framework supports classification tasks of any complex diseases, is not limited by omics data types and combination modes, not only is remarkably superior to a traditional integration normal form in classification performance, but also shows a unique negative generalization distance, and proves that the framework has excellent generalization ability. Meanwhile, features and omics importance scores automatically output by the model provide a powerful analysis tool for biomarker discovery and precise diagnosis and treatment of complex diseases.

The invention relates to the technical field of medical information, and discloses a CRRT treatment intelligent safety early warning system based on multi-modal fusion, and the system comprises a data collection and fusion module which is used for collecting multi-modal data from a plurality of heterogeneous data sources in real time, carrying out the standardization and fusion processing of the multi-modal data, and generating fusion data with a unified time sequence; the knowledge model library is used for storing medical knowledge, clinical guidelines and historical data related to CRRT treatment and trained early warning and decision models; according to the system, through multi-modal data fusion and machine learning, the crossing from passive alarm to active early warning is realized, and the safety of a patient is remarkably improved. The intelligent decision support can output an individualized treatment scheme, promote precise medical treatment and optimize resource utilization, the system integration and grading alarm mechanism effectively reduces the cognitive load of medical care, and the self-learning ability of the system ensures that an early warning model can be continuously evolved and promotes the continuous improvement of clinical treatment quality.

The invention belongs to the technical field of biology, and particularly relates to a construction method of a TMV and AIE hybrid material as well as a product and application of the TMV and AIE hybrid material. By optimizing TMV purification conditions, the problems of strip dispersion, strip taking difficulty and the like after ultracentrifugation are effectively solved. After TMV is extracted and purified, AIE is dissolved in a KP buffer solution to prepare an AIE molecular solution, then the purified TMV and the AIE molecular solution are mixed and incubated according to the volume ratio of 10: 1, the hybrid material with fluorescence tracing and photodynamic therapy activity is obtained, in-vivo and in-vitro visual tracking imaging and reactive oxygen species (ROS) release functions are achieved, the diagnosis and treatment integrated potential is shown, and the application prospect is wide. And the application boundary of the TMV in the fields of precision medicine and the like is expanded.

The invention belongs to the technical field of biological medicine, and particularly discloses nano composite particles based on a metal organic framework and a preparation method and application of the nano composite particles. The nano-composite particles are constructed through electron adsorption and coordination based on a nano-scale metal organic framework, and under the acidic microenvironment and ultrasonic irradiation of tumors, the nano-composite particles can be decomposed and released in a responsive manner, play a role of a sound-sensitive agent, generate a large amount of active oxygen and induce tumor cells to generate immunogenic death, so that the tumor cell immunogenicity is improved, and the tumor cell immunogenicity is improved. Further, more cytotoxic T lymphocytes are promoted to infiltrate a tumor microenvironment, T cell mediated anti-tumor immune response is recovered, and tumor metastasis is effectively prevented. The carrier metal organic framework ZIF8 of the nano-composite particles can serve as a sound-sensitive agent to generate active oxygen, the function of ultrasonic imaging of tumor tissue can be achieved, the effect of diagnosis is achieved while treatment is conducted, tumor diagnosis and treatment monitoring can be achieved at the same time through one-time drug administration, and a new strategy is provided for precise medical treatment.

The invention discloses an intelligent medicine box accurate medicine discharging control system and method based on multi-mode perception, and belongs to the technical field of intelligent medical equipment. According to the system, tablet data are collected in real time through a multi-mode sensing module (comprising a high-precision weight sensor, machine vision and an infrared counter), and high-precision tablet dispensing is achieved through a three-stage tablet dispensing mechanism. The control method is fused with an adaptive Kalman filtering algorithm for data correction, has the functions of blockage detection and emergency recovery, and can dynamically adjust operation parameters. Experimental verification shows that the total accuracy rate of 1000 times of medicine discharging tests on five kinds of tablets by the system reaches 99.2% (the accuracy rate of special-shaped tablets is 98.7%), and the blockage occurrence rate is 1t; the emergency recovery success rate is 100%, and the standby power consumption is 1t; the method is suitable for complex medication scenes of old patients, and has the advantages of high precision, self-adaption, high reliability and the like.

The invention belongs to the field of disease evolution trajectory prediction in precision medicine, and discloses a Parkinson's disease evolution prediction modeling method based on high-order gene communication tensor. According to the method, firstly, based on an intercellular communication mechanism, a high-order gene module communication tensor is constructed as spatial topological data, so that an information interaction mode between gene modules is described; secondly, a self-adaptive Transform module is designed to extract gene expression features and high-order space topology features at the same time; and finally, integrating multi-view interaction information between the high-order gene module communication tensor and the whole blood transcriptome data through a multi-view collaborative fusion mechanism to realize effective fusion and utilization of features. Experimental results show that compared with a traditional genetic map, the high-order space representation provided by the invention has finer resolution capability, a plurality of evaluation indexes are remarkably improved, and a new technical means is provided for accurate staging of Parkinson's disease.

The invention discloses a reverse transcriptase, wherein a coding sequence of the reverse transcriptase is derived from a Rattus norvegicus genome. The protein sequence of the protein comprises a protein sequence of a wild type Rattus norveicus, or a protein sequence which is subjected to engineering modification on the basis of the wild type Rattus norveicus, or a protein sequence which is subjected to engineering modification on the basis of the wild type Rattus norveicus. Furthermore, the protein sequence of the gene is as shown in any one of SEQ No.1-55. In a rice genome site and a human HEK 293T cell genome site, the system shows efficient editing efficiency of a guide editor. The invention provides a series of efficient reverse transcriptase components which can be carried on a guide editing system, important bottom-layer technical support is provided for research of point mutation in a genome, creation of a special site disease model and correction of genetic mutation sites, and the reverse transcriptase component has a wide application prospect in the field of precision medicine and precision agriculture breeding.

The invention provides a pharmaceutical composition. The pharmaceutical composition is prepared from an angiotensin II receptor blocker, folic acid, 5-methyltetrahydrofolic acid and acceptable auxiliary materials. The composition has the advantages that the composition has the functions of reducing blood pressure and preventing and treating osteoporosis, and is particularly suitable for hypertension and osteoporosis susceptible people with MTHFR C677T gene mutation. Through the implementation of the invention, the pharmaceutical composition is provided for people with a specific genetic background, so that precise medical treatment is realized, the curative effect is improved, the side effect is reduced, and the medication compliance is improved.

The present application relates to the technical field of bioinformatics, and particularly relates to a method for constructing a biological age prediction model based on DNA methylation. The method comprises the following steps: obtaining whole genome methylation sequencing data of a human peripheral blood sample; classifying cell subpopulations of the human peripheral blood sample, and performing single-cell RNA sequencing processing on each cell subpopulation to obtain single-cell sequencing data including lymphocytes, neutrophils and monocytes; performing tissue-specific analysis on CpG sites within a range of 2000 base pairs upstream and downstream of each cell subpopulation-specific transcription factor binding site according to the single-cell sequencing data and the whole genome methylation sequencing data to obtain candidate marker site data. The present application can provide strong support for early detection of accelerated aging, prediction of related disease risks and guidance of precision medicine.

This invention belongs to the field of biomedical technology, specifically relating to the application of DNA damage response gene-related SNPs in the treatment and prognosis of acute myeloid leukemia (AML). This invention is the first to discover that single nucleotide polymorphisms (SNPs) in the DNA damage response pathway (focusing on three major functional modules: damage sensing, signal transduction, and DNA repair) are closely related to the clinical characteristics, treatment response, and survival prognosis of AML. This lays the foundation for constructing a multi-gene risk model based on the DNA damage response pathway and provides theoretical support for integrating germline SNP maps into precision medicine strategies for AML.

The present invention provides an AutoCell Platform (ACP), an advanced, fully automated, functionally closed system designed to revolutionize the manufacturing of genetically modified cell therapies. This innovative platform integrates automation, closed-loop processing, and novel technologies to address the inefficiencies and high costs of traditional methods. Key features include automated centrifugal seeding for enhanced genetic modification efficiency, microbubble-assisted cell selection for precise cell isolation, and a modular design for scalable amplification across clinical and research environments. By reducing production timelines from 30-40 days to less than 3 days and orders of magnitude in cost, the ACP enables instant manufacturing, decentralization, and broader accessibility. Advanced quality control measures and standardized design ensure compliance and consistent therapeutic outcomes. The ACP supports a variety of applications from CAR-T therapies to regenerative medicine, representing a transformative leap in global accessibility of precision medicine and life-saving cell therapies.

The present application relates to a model for predicting the age of a rat, which considers data of immune cells, or data of immune cells, and data of cytokine and / or chemokine levels to predict the age of a rat. The model of the present application significantly improves the accuracy and reliability of age prediction, provides strong support for personalized health management, and helps to promote the development of precision medicine, and ultimately aims to achieve the goal of extending healthy life.

The application discloses an adaptive clustering federated learning modeling method for precision medicine, and relates to the technical field of precision medicine, and comprises the following steps: data collection and preprocessing and model construction and training; the application accurately determines the optimal number of the global model through adaptive clustering, and divides clustering groups according to the similarity of the client model parameters, realizes independent training of the groups, effectively improves the adaptability of the model to different client data characteristics, samples and label distribution differences, avoids the problem that a single model has poor performance in some clients, and significantly enhances the ability of the model to capture complex medical patterns; the model generalization is optimized through grouped training, so that the model can better cope with new data distribution, under the premise of ensuring the privacy and security of medical data, the accuracy and reliability of the model in precision medical scenes such as disease diagnosis and prognosis prediction are greatly improved.

This invention discloses a primer-probe combination for detecting Candida auris and its drug-resistant mutation sites. The Candida auris drug-resistant mutation sites include azole resistance mutation sites: mutation sites A395T and A428G in the ERG11 gene, and an anti-echinocandin resistance mutation site: mutation site C1916T in the FKS1 gene. The primer-probe combination includes primer and probe sequences as shown in SEQ ID NO. 1-13, and corresponding kits and detection methods are also provided. In this invention, the primer-probe combination is prepared as a lyophilized reagent, eliminating the need for detection system preparation and directly detecting the nucleic acid extracted from the sample. It features simple operation, good stability, and wide applicability. Furthermore, by detecting the sample, it is possible to quickly determine whether the sample is positive for Candida auris and whether Candida auris drug-resistant mutations have occurred, while also identifying the drug-resistant mutation targets, meeting clinical needs and achieving precision medicine.

A method for practicing precision medicine comprising providing, to a blockchain platform, each of clinical data and genetic data, providing the blockchain platform, the blockchain platform having a first data structure comprising clinical data and a second data structure comprising genetic data, harmonizing the first and second data structures, creating at least one cohort based on the harmonized first and second data structures, and identifying at least one relationship between the clinical data and the genetic data in each of the at least one cohort.

The invention discloses a drug collaborative screening system based on artificial intelligence prediction and metabonomics verification, and belongs to the technical field of artificial intelligence and bioinformatics crossing. Comprising a multi-source data acquisition and feature fusion module, an artificial intelligence prediction module, a drug screening and visualization module, a metabonomics verification module and a feedback optimization and self-learning module. According to the invention, through multi-modal data integration and feature coding, unified modeling of a drug-disease-molecule network is realized; drug combination synergistic effect prediction is realized through an artificial intelligence algorithm; a prediction result is verified by using non-targeted metabonomics data, and the mechanism interpretability of the model is enhanced; a technical system capable of realizing closed-loop optimization is constructed, and an algorithm and experiment dual-verification platform is provided for drug discovery and precision medicine.

The invention discloses an amino acid L / D type enantiomer separation and quantitative detection method based on LC-MS, and belongs to the technical field of biological analysis. Aiming at the problems of difficulty in separation of amino acid L / D type enantiomers in human serum, low detection sensitivity, complex analysis process and the like, the method disclosed by the invention is simple in pretreatment, short in instrument collection time, high in chiral enantiomer separation degree, small in matrix effect, high in sensitivity, low in cost and convenient for large-scale clinical sample detection in a laboratory; the method not only can make up the limitation that only the L type is detected at present, but also can provide a key analysis method for early warning of diseases, drug effect and toxicokinetics research and precise medical evaluation.

This invention relates to a method and system for constructing a medical knowledge graph based on cross-dimensional alignment using artificial intelligence, belonging to the field of medical knowledge graphs. This method integrates authoritative databases from multiple medical domains by constructing a unified medical semantic space across scales and modalities; and combines deep representation learning and manifold alignment techniques to design a joint embedding and dynamic alignment framework for multi-source heterogeneous knowledge, achieving semantic unification and structure-fidelity mapping of different standard systems in a low-dimensional dense space. This invention not only effectively alleviates the semantic fragmentation problem between ontology but also significantly improves the system's semantic understanding and cross-database retrieval capabilities for complex clinical texts, providing a computable and reasonable knowledge foundation for clinical decision support, disease prediction, and precision medicine.

This application provides a method for using a precision medicine cabinet drawer, belonging to the field of medical device technology, including the following steps: establishing an associated dataset of medicine type, medicine weight, medicine box number, and light source group number; after the user completes identity verification through the identification module, the user inputs the type of medicine to be retrieved and the target weight of the medicine; the processor obtains the corresponding medicine box number and light source group number based on the medicine type, and controls the corresponding multi-color light source group to issue an indication, while controlling the drive component to open the drawer; the user retrieves the medicine according to the light indication; the processor monitors the weight change of each medicine box in real time based on the weighing sensor to determine whether the amount of medicine retrieved is accurate; in this invention, by establishing an associated dataset and combining it with the indication function of the multi-color light source group, the accuracy of medicine retrieval positioning is effectively improved; furthermore, by comparing the actual weight of the medicine retrieved with the target weight of the medicine retrieved, the accuracy of the amount of medicine retrieved is ensured to be consistent with medical needs.

The invention belongs to the technical field of biological medicine and molecular biology, and provides a hypertrophic cardiomyopathy virulence gene MYH7c.794Cgt; the invention relates to T (p.Thr265Ile) and an application thereof. The MYH7 gene mutation is located on the ninth exon, the 794th base is mutated from C to T, namely ACC is mutated to ATC, and the 265th amino acid in the coded amino acid sequence is mutated from threonine to isoleucine. The mutation induces cardiac hypertrophy by disrupting energy metabolism-this defect occurs prior to the occurrence of systolic dysfunction. Along with increasingly prominent status of precision medicine in cardiovascular treatment, a treatment strategy aiming at an upstream pathological process (such as energy homeostasis and mitochondrial dysfunction) provides a way with a wide prospect for preventing and treating MYH7-related hypertrophic cardiomyopathy. MYH7 gene screening has important values in the aspects of promoting early diagnosis, guiding timely treatment intervention and realizing risk-based prevention and management.

The application discloses a wearable system for evaluating postoperative pulmonary complication risk, comprising: a data acquisition module, including a vest, a pulse oximeter and a signal recording box, for collecting physiological signals of an evaluated person during submaximal exercise test; a data processing module connected with the data acquisition module, for processing the physiological signals to obtain characteristic data based on the peak and slope of the physiological signals; a model calculation module connected with the data processing module, for inputting the characteristic data into a model trained by using a stochastic gradient algorithm and including preoperative clinical physiological characteristic data, to calculate a postoperative pulmonary complication incidence rate; and a model evaluation module connected with the model calculation module, for determining whether the evaluated person has postoperative pulmonary complications according to a Melbourne Group Score Scale. The system can accurately predict the probability of pulmonary complications by the cooperation of the modules, and provide precise medical intervention guidance for clinical treatment.

A single-cell level spatial omics multi-modal fusion method, comprising: extracting differential expression genes and nuclear spatial morphological features from spatial transcriptome data, single-cell sequencing data and histological images, and realizing field adaptation between different platforms by using conditional variational autoencoder. Based on a probability inference model, spatial transcriptome expression, single-cell omics and morphological features are fused to jointly infer the type and gene expression level of each cell. A spatial cell network is constructed by a graph attention mechanism to realize the spatial diffusion and recognition of cell types in the whole slice range. Combined with a multi-omics enhancement module, the unmeasured gene and protein expression are completed based on expression similarity, and the prediction consistency is improved through spatial correction. The method realizes high-resolution reconstruction of single-cell multi-omics information in three-dimensional space, improves the information coverage and spatial resolution of spatial omics data, and provides an efficient and low-cost solution for spatial biology and precision medicine research.

The invention discloses an enzyme targeted response type benzyl fluorine bridged glucoside gadolinium compound and application thereof as a magnetic resonance contrast agent. According to the invention, a high-stability macrocyclic gadolinium contrast agent is subjected to structural modification, a glycoside group and a benzyl fluorine bridging group with specific biological enzyme responsiveness are introduced, and the benzyl fluorine bridging group triggered by enzyme response forms a quinone methylation active intermediate; the quinone intermediate can react with a nucleophilic biomacromolecule functional group to form a covalent bond to obtain a gadolinium biomacromolecule compound, so that the magnetic resonance relaxation rate of gadolinium molecules can be remarkably improved, the enhancement of an MRI signal is realized, and the real-time specific enzyme tracking function of MRI is realized. Compared with the existing non-targeted responsive gadolinium contrast agent, the contrast agent provided by the invention has specific biological enzyme targeted responsiveness, can be used for real-time and non-invasive monitoring of diagnosis and treatment of enzyme function abnormality diseases, and has important significance in the aspects of precision medical treatment and the like.