34 results about "Disease early" patented technology

The invention relates to pattern recognition and anomaly detection in the technical field of computers, and discloses a laying hen disease early-stage AI early warning method and a laying hen disease early-stage AI early warning system. The method comprises the following steps: obtaining a laying hen behavior video stream, an excrement image and body temperature data, and carrying out preprocessing such as timestamp alignment and equipment number mapping to generate a training and reasoning input structure; then target tracking, key point coding, region segmentation, contour feature coding and body temperature fragment statistical coding are executed to extract a multi-source feature sequence; performing time window alignment, scale normalization, pattern recognition and anomaly detection processing on the multi-source features to realize disease type recognition; and finally, generating an early warning event based on rule triggering and threshold judgment, and forming closed-loop disposal through prevention and treatment scheme retrieval and instruction arrangement. According to the method, collaborative analysis and early abnormity early warning of multi-source heterogeneous data are realized, and the disease recognition accuracy and early warning timeliness are remarkably improved.

The invention belongs to the technical field of early diagnosis of diseases, and discloses a polypeptide targeting macrophage PLIN2, a PET molecular probe and a preparation method and application thereof.Firstly, the polypeptide P672-1 targeting the PLIN2 is synthesized, and 64Cu-NOTA-P672-1 is prepared on the basis of the polypeptide P672-1. The molecular probe has the good chemical yield and stability, has the good targeting performance on the PLIN2, and can be used for preparing 64Cu-NOTA-P672-1. And the biological safety performance is good. Compared with the conventional 18F-FDG, the 64Cu-NOTA-P672-1 prepared by the preparation method disclosed by the invention has a good early diagnosis effect. The preparation method of the 64Cu-NOTA-P672-1 is simple and easy to implement, and the 64Cu-NOTA-P672-1 can be used for diagnosing early abdominal metastasis of ovarian cancer with high expression of PLIN2 The nuclide 64Cu-NOTA-P672-1 disclosed by the invention has important clinical significance on early diagnosis of ovarian cancer metastasis.

The present invention relates to a biomarker HLA-DOA for sepsis-related diseases and a use thereof. The present invention provides a use of HLA-DOA or an active fragment or functional fragment thereof in the preparation of a product for early diagnosis, risk assessment, immune status assessment, prognosis prediction and / or treatment regimen selection of sepsis-related diseases in a subject.

The invention discloses a cerebrovascular disease risk prediction method, and particularly relates to the field of disease risk prediction, and the method comprises the following steps: S1, collecting basic clinical data, dynamic physiological parameter data and image feature data of a target object, and constructing a data set; s2, performing feature extraction on the basic clinical data, the dynamic physiological parameter data and the image feature data, and fusing the extracted features to obtain a fused feature set; s3, constructing a fusion prediction model through the fusion feature set; and S4, inputting the data set into the fusion prediction model, and outputting a risk prediction result. According to the method, the information short board of a single data type is made up, different types of data are converted into a unified computable form through standardized quantitative processing, core information with risk indication significance is effectively screened out in combination with abnormal data proportion analysis and dynamic fluctuation intensity evaluation, and the recognition capability of early potential risks of diseases is remarkably improved.

The invention belongs to the field of early screening of diseases, and particularly relates to application of a protein marker in preparation of a product for early screening, identification and diagnosis of amyotrophic lateral sclerosis. The expression of LanCL1 in serum of ALS patients is higher than that in serum of healthy people and is negatively related to the state of illness, and the marker has no abnormality in Parkinson's disease patients and is high in specificity. The product is a blood detection kit, sampling is convenient, patient compliance is good, early diagnosis, identification and illness state evaluation of ALS can be achieved, and the problems that an existing marker is insufficient in specificity and complex in sampling are solved.

The present invention relates to a sepsis-related disease biomarker HLA-DQA and the use thereof. Provided in the present invention is the use of HLA-DQA or an active fragment and functional fragment thereof in the preparation of a product for early diagnosis, risk assessment, immune status assessment, prognosis prediction, and / or treatment regimen selection of a sepsis-related disease in a subject.

The application relates to the technical field of disease prognosis risk early warning, and particularly discloses a disease prognosis risk early warning method and system based on big data, which comprises the following steps: obtaining all reference prediction patients of a current patient based on all types of health data of the current patient; obtaining the evaluation and assignment of each type of health data of the current patient based on each type of health data of the current patient and an evaluation and assignment model of each type of health data; obtaining a disease early warning reference graph and a disease early warning comparison graph of the current patient based on the evaluation and assignment of all types of health data of all reference prediction patients of the current patient; and obtaining a disease prognosis risk estimation value of the current patient based on the disease early warning reference graph and the disease early warning comparison graph of the current patient, and then obtaining a disease prognosis risk early warning result of the current patient. The application realizes accurate quantification of the degree of future development and recovery risk of the disease of the current patient, improves the accuracy and reliability of disease early warning, and ensures effective use of resources.

Early stage, rapid, low-cost detection of disease components in a biological sample is critically important. A point of care device can include a collection region and can be used to hold a sample that is combined with a fluorescent dye and a plurality of magnetic particles such that disease components in the sample are tagged with the fluorescent dye and the plurality of magnetic particles. At least one magnet can be located next to the collection region to establish a magnetic field gradient to draw the tagged disease components into the collection region from the device. A fluorescence microscope can image the small collection region based on the fluorescent dye to detect the disease components. The fluorescence microscope uses light to excite the fluorescent dye and a filter to transmit light emitted by the fluorescent dye to the fluorescence microscope, while restricting light used to excite the fluorescent dye.

Methods and systems for the detection and diagnosis of diseases, including cancer. [Solution] A patient's sample is analyzed by absorption spectroscopy in the near-infrared and mid-infrared ranges to create a spectral signature, and the sample is obtained from the patient. This signature is processed by a computational engine using one or more machine learning techniques to determine whether the spectral signature indicates the presence of a disease, including one or more cancers. The embodiment operates outside the conventionally accepted wavelength range, facilitating faster, simpler, and more reliable testing.

The invention relates to an infectious disease morbidity prediction method and system based on three-layer dimension parallel convolution, and belongs to the technical field of artificial intelligence and public health monitoring. Aiming at the problems of difficulty in collaborative modeling of multi-source data, difficulty in balancing long-term trend and short-term fluctuation and lack of adaptive feature fusion in an existing prediction method, the method comprises the following steps: firstly, preprocessing input time sequence data and decomposing the data into coarse, medium and fine granularity features; then long-term trend, medium-term period and short-term fluctuation characteristics are extracted through three parallel branches of a kernel adaptive network, a multi-scale time sequence convolution network and a time sequence convolution module respectively; and then the multi-scale features are subjected to adaptive weighted fusion by using a gating fusion unit, and finally a prediction value is output through a full connection layer. The method is mainly used for accurately predicting the morbidity of infectious diseases and provides support for disease early warning and prevention and control resource allocation.

The invention discloses a method for auxiliary assessment of early risk of Alzheimer's disease based on EFEMP1 detection. The method is an in-vitro non-diagnostic analysis method, and comprises the following steps: detecting the biomarker level of EFEMP1 in a biological sample of a subject, and obtaining information for evaluating the AD early risk of the subject based on a detection result. The biomarker comprises an EFEMP1 genotype, an EFEMP1 mRNA (messenger Ribonucleic Acid) expression level or an EFEMP1 protein concentration. When the risk assessment is realized by quantitatively detecting the concentration of the EFEMP1 protein in the plasma, an immunodetection kit containing a capture antibody and a detection antibody aiming at different epitopes of the EFEMP1 protein can be specifically adopted. Objective information for risk judgment is obtained by detecting the protein concentration, genotype or mRNA level of EFEMP1 in a biological sample of a subject, early-stage and non-invasive monitoring of an AD upstream pathological mechanism is achieved, and a new strategy is provided for early warning of diseases.

The invention discloses application of integrin alpha3 as a focal segmental glomerulosclerosis biomarker, and belongs to the technical field of biological detection. A sample used for detection is kidney tissue, and the expression level and distribution of integrin alpha3 in the sample are detected. By detecting and analyzing the expression level and distribution of the integrin alpha 3 in the epithelial cells of the cyst wall layer in the glomerulus, the segmental sclerosis globules in the early stage of the disease can be identified, and the method is an effective method for assisting in diagnosing focal segmental glomerulosclerosis.

The invention relates to an early disease detection device based on high-depth cell-free nucleic acid sequencing, and the device can comprise a library preparation part which is used for preparing a library set comprising a whole genome sequencing library, a whole epigenome sequencing library, a transcriptome sequencing library and an epitranscriptome sequencing library based on cell-free nucleic acid extracted from blood; the sequencing part is used for sequencing the library set to generate high-depth cell-free nucleic acid sequencing data; the signal calculation part is used for mapping the high-depth cell-free nucleic acid sequencing data to a genome reference sequence so as to determine the position of the high-depth cell-free nucleic acid sequencing data on a genome, and calculating at least one disease-specific signal value based on the mapped high-depth cell-free nucleic acid sequencing data; a prediction signal value generation section for processing the at least one disease-specific signal value to generate a disease prediction signal value; and a prediction model generation unit for generating a disease prediction model using the marker information relating to the blood and the disease prediction signal value to detect the occurrence or absence of a disease in the input blood and the type of the disease.

The invention discloses an auxiliary decision-making system for early diagnosis and progress prediction of cerebral small vascular diseases. The system comprises a data acquisition module, a data processing module, a comprehensive analysis module and a data storage module, the data acquisition module is used for acquiring proteomics detection data of a patient; the data processing module is used for analyzing the detection data of the patient to obtain analysis results of different detection data; the comprehensive analysis module is used for carrying out comprehensive analysis on analysis results of different detection data and generating auxiliary decision prompt information; and the data storage module is used for storing the data of the patient. Through comprehensive analysis of iron metabolism imbalance detection data and protein detection data of a patient, the disease early diagnosis sensitivity and disease early prediction accuracy are improved, and meanwhile, the accuracy of a diagnosis report auditing result is improved.

The application discloses a screening method for an early diagnosis marker of Alzheimer disease based on alternative splicing, and relates to the technical field of biomedical diagnosis. The application aims to solve the problems of single feature dimension, insensitivity to splicing changes, insufficient robustness and the like in the prior screening method for an early diagnosis marker of Alzheimer disease, and provides a multi-dimensional feature fusion screening method based on alternative splicing.

The present application relates to disease early warning technology, the present application provides an infectious disease early warning method, device and storage medium based on electronic medical record, wherein the method comprises: obtaining the electronic medical record text of patients in each medical institution; extracting the disease symptoms of each patient from each electronic medical record text in turn; determine whether each disease symptom meets the preset infectious disease symptom condition; if yes, the patient corresponding to the disease symptom is recorded as a case of infectious disease, and the early warning level of the case of infectious disease is determined, different infectious disease types correspond to different early warning levels; early warning is carried out according to the early warning level corresponding to the case. In this way, the cases meeting the infectious disease symptoms can be monitored in time through the electronic medical record text, and then early warning is carried out, which is fast and efficient.

The present invention relates to a biomarker PAX5 for sepsis-related diseases and a use thereof. The present invention provides a use of PAX5 or an active fragment or functional fragment thereof in the preparation of a product for early diagnosis, risk assessment, immune status evaluation, prognostic prediction and / or treatment plan selection of a sepsis-related disease in a subject.

The present application relates to the technical field of genetic diagnosis, and specifically discloses a TBC1D24 gene mutant and application thereof. Specifically disclosed are any one of the following TBC1D24 gene mutants: a nucleic acid TBC1D24, which has a c.677_680delCCCG mutation and a c.731C>T mutation compared with a wild-type TBC1D24 gene with a sequence of SEQ ID NO:1; a polypeptide TBC1D24, which has a p.A226Gfs*28 mutation and a p.A244V mutation compared with a protein encoded by a wild-type TBC1D24 gene with a sequence of SEQ ID NO:2. The application also discloses application of the TBC1D24 gene mutant in screening of familial infantile myoclonic epilepsy. The present application widens the pathogenic gene spectrum of familial infantile myoclonic epilepsy, strengthens the understanding of the disease by clinicians, provides experience for screening and diagnosis of the disease in clinic, especially for pre-pregnancy screening, provides a research direction and a new theoretical basis for early diagnosis and effective treatment of the disease, and also provides a new molecular target for developing specific drugs for treating the disease in practice.

The invention discloses a dual microRNAs AND initial logic sensing system based on engineering 8-17 DNAzyme. The dual microRNAs AND initial logic sensing system comprises an 8-17 DNAzyme AND logic element with dual catalytic activity centers, two closed chains and a substrate probe, when miRNA-21 and miRNA-205 exist in a sample at the same time, the two closed chains are combined with two recognition arms of the AND logic element so as to close catalytic activity sites of the AND logic element, and when miRNA-21 and miRNA-205 exist in the sample at the same time, the two closed chains are removed through a strand displacement reaction, so that the catalytic activity of the AND logic element is activated, and a substrate probe is cut to generate a fluorescence signal; the method realizes AND logic gate operation recognition of double miRNAs, has the characteristics of high specificity, high sensitivity, visual output and the like, can be used for constructing a logic detection platform of tumor-related microRNAs, and has application potential in the aspect of early diagnosis of diseases.

The invention is suitable for the technical field of biological data processing, and provides a method for evaluating the immune state and the immune age of a human body, and the method comprises the steps: firstly, based on a preset T cell receptor immune group library, rapidly obtaining multi-dimensional feature set information, then inputting the multi-dimensional feature set information into a preset multi-task deep learning model, and obtaining the immune age of the human body. And evaluation result set information is accurately generated. The problem of signal confusion can be solved, disease specific interference is effectively distinguished and stripped, a purer and more accurate immune age prediction result is obtained, meanwhile, the classification accuracy of the health state under the complex immune background is improved, the generalization ability and robustness of the model are remarkably enhanced, and the method is suitable for popularization and application. A brand-new and integrated solution is provided for early warning of diseases, dynamic monitoring of health status and evaluation of anti-aging intervention curative effect.

The application discloses application of anti-myosin autoantibody in preparation of a diagnosis product for neonatal necrotizing enterocolitis. The application determines that IgG and IgM type anti-myosin autoantibody in peripheral blood of a child with neonatal necrotizing enterocolitis (NEC) is significantly increased in early stage of the disease; the application finds that the anti-myosin autoantibody can be used as an early prediction and diagnosis marker, and is used for diagnosis of NEC; the marker has high diagnosis efficiency for early NEC, and provides an effective basis for early prediction and diagnosis of NEC.

The invention relates to an illness state early warning method and system based on multi-modal data fusion and a medium, and the method comprises the steps: obtaining multi-modal data information which comprises clinical physiological parameters, text medical record data and image data of a patient; performing cleaning, denoising and normalization processing on the multi-modal data information to obtain standardized data information; performing feature extraction on the standardized data information to obtain feature data information, and performing fusion based on the feature data information to obtain fused potential risk factors; the fusion decision result is imported into a constructed dynamic risk assessment model to obtain and output a risk assessment result, the method can adjust assessment parameters in real time according to dynamic changes of patient condition data, the method adapts to individual differences and condition evolution processes of different patients, and timeliness and dynamic adaptability of early warning are improved; medical resource allocation can be optimized, and the diagnosis and treatment efficiency and the prognosis effect of the patient can be improved.

The invention provides application of a combination of A beta1-42 and CD64 in preparation of a reagent for early detection of sepsis, and belongs to the technical field of biology, the diagnosis value of combined detection of A beta1-42 and CD64 is significantly higher than that of a single index, a synergistic effect is generated, and the technical problem that a single marker is insufficient in sensitivity or specificity is solved. The CD64 is quick in response, the Abeta1-42 is closely associated with the pathological process, and the combination is beneficial to identifying high-risk patients in the early stage of diseases and striving for precious intervention time for clinic. The marker combination not only can be used for qualitative diagnosis, but also can be used for risk stratification and prognosis judgment, and has comprehensive functions, and the quantification level of the marker combination is positively correlated with the severity of diseases.

The invention belongs to the technical field of biosensing, and particularly relates to a biosensor for detecting high-mobility group protein B1 as well as a preparation method and application of the biosensor. The biosensor is an organic electrochemical transistor and comprises a substrate; the source electrode, the drain electrode and the gate electrode are arranged on the substrate; an organic semiconductor channel connecting the source electrode and the drain electrode; the nucleic acid aptamer molecular layer is fixed on the surface of the gate electrode and can be specifically combined with HMGB1; the sealing agent molecular layer is used for passivating uncombined sites on the surface of the gate electrode; and when the HMGB1 is combined with the aptamer, electrolyte ions are caused to be doped into an organic semiconductor channel, so that the output current is efficiently modulated, and rapid, high-sensitivity and high-specificity label-free electrical detection is realized. The biosensor is suitable for dynamic monitoring of HMGB1 in various diseases such as acute kidney injury and sepsis, and a novel tool is provided for disease early diagnosis and disease course management.

This application discloses a method, device, equipment, and storage medium for early warning of critical states in biological systems, relating to the field of bioinformatics. It quantifies the degree of local network disturbance using network topology indicators, enabling the sensitive detection of abnormal turning signals in the cell network topology before disease symptoms appear, providing a basis for early disease warning. The method includes: obtaining a cell feature matrix; constructing a cell-related network for each stage of the disease process based on cell expression characteristics in the cell feature matrix; defining local cell networks within the cell-related network of each stage of the disease process; determining a global evaluation indicator for each stage of the disease process based on the topology evaluation indicators of all local cell networks in the disease process stage; and identifying the stage of the disease process where the global evaluation indicator first reaches a maximum value as a critical state, thereby providing disease warning through the critical state.

A method for disease risk assessment using multiple data sources, and a computer program for implementing the same. The present invention relates to a method for improving the accuracy of early cancer detection by a priori identifying a PRS and then combining it with biomarkers to improve accuracy. The method comprises the following steps: i) calculating an individual's genetic risk, including a polygenic risk score (PRS); ii) measuring biomarkers (including proteins and metabolites); and iii) (optionally) updating the calculated risk to take into account additional clinical variables, including age, sex, and history of infectious diseases or environmental exposures (e.g., smoking).

The invention provides a bridge support disease early warning method and device, relates to the field of civil engineering structure monitoring, and solves the technical problem that accurate recognition, automatic early warning and efficient monitoring of bridge support diseases are still limited in the prior art. The method comprises the following steps: acquiring bridge support time sequence data, inputting the data into a BERT model in four channels based on a strain data sequence, a displacement data sequence, a vibration signal data sequence and an environment temperature data sequence for feature extraction and feature fusion, and determining fusion features; based on the fusion features, predicting the occurrence probability and the support displacement safety interval of each bridge support disease by adopting a long-short-term memory network model; when the actually monitored bridge support displacement value exceeds the support displacement safety interval or the occurrence probability of one or more bridge support diseases exceeds the corresponding threshold value, a bridge support disease early warning signal is triggered. The method and the device are used in a bridge support disease early warning process.

The application discloses application of intestinal bacteria metabolism key genes in preparation of an atherosclerosis diagnosis product, and belongs to the technical field of biological medicine. The application establishes a CRISPR / Cas12a detection method based on RPA and crRNA array mediation, can accurately detect eight intestinal bacteria metabolism key genes closely related to cardiovascular diseases, and realizes rapid diagnosis according to the detection result. The application provides a new technical means for evaluating the risk of cardiovascular diseases, and has potential application value in early disease screening and prediction judgment.

The invention discloses a method for auxiliary assessment of early risk of Alzheimer's disease based on GALNT10 detection. The method is an in-vitro non-diagnostic analysis method, and comprises the following steps: detecting the biomarker level of GALNT10 in a biological sample of a subject, and obtaining information for evaluating the AD early risk of the subject based on a detection result. The biomarker comprises a GALNT10 genotype, a GALNT10 mRNA (messenger Ribonucleic Acid) expression level or a GALNT10 protein concentration. When the risk assessment is realized by quantitatively detecting the concentration of the GALNT10 protein in the blood plasma, an immunodetection kit containing a capture antibody and a detection antibody aiming at different epitopes of the GALNT10 protein can be specifically adopted. Objective information for risk judgment is obtained by detecting the protein concentration, genotype or mRNA level of GALNT10 in a biological sample of a subject, early-stage and non-invasive monitoring of an AD upstream pathological mechanism is achieved, and a new strategy is provided for early warning of diseases.

The invention relates to the technical field of health management, in particular to a digestive system disease early warning and management method and system, and the system comprises a household non-sensing layer, a data transmission and network layer, a cloud intelligent analysis and management platform, and a user interaction and application layer. Slow degradation of digestive system functions can be found earlier than subjective feeling and accidental inspection, and early warning is achieved; data comes from non-inductive family environment monitoring, and the daily digestion related behavior state of the user is truly reflected; behavior data of kitchen activities and toilet activities are subjected to correlation analysis, and a more complete digestive system function evaluation view angle is provided; non-video and non-audio sensors are adopted, only whether behaviors occur or not and the duration of time are monitored, specific activity content is not involved, privacy is protected to the maximum extent, active operation of a user is not needed, and continuity and authenticity of data are guaranteed; the used sensors are mature and low-cost Internet of Things equipment, and are easy to install and expand in a common family environment.