375 results about "Brain disease" patented technology

The present disclosure relates to a multimodal computing-based early intelligent graded screening system for a brain disease. The system comprises: a multimodal data acquisition unit, configured to acquire multimodal data of a target patient under a specified screening grade to form a screening data set; a multimodal feature generation, completion, fusion and calculation unit, configured to generate and complete feature data of modal features in the screening data set to obtain complete modal features, and extract pathological features for calculation to obtain a first screening result; a knowledge-based intelligent screening unit, configured to encode the feature data of the modal features in the screening data set into corresponding graph structure data features, use a multimodal association graph optimized by expert knowledge to match the graph structure data features to obtain knowledge-based association features, and obtain a second screening result on the basis of the knowledge-based association features; and an intelligent graded screening unit, configured to carry out weighted calculation on the first screening result and the second screening result to obtain a final screening result. The present disclosure achieves accurate early screening of brain diseases of patients.

The invention discloses a brain disease classification method and system. Precise diagnosis is realized through multi-modal data fusion and dynamic modeling. The method comprises the following steps: collecting multi-modal brain image information and cognitive behavior information of a user; performing dynamic function connection analysis on the resting state functional magnetic resonance time sequence signal to obtain a time-varying brain network feature matrix, and performing white matter fiber bundle topology reconstruction on a structure connection matrix; constructing a four-dimensional correlation tensor by using the time-varying network features, the structural connection weights and the anatomical features through a neurodynamic model; performing multi-task learning on the four-dimensional correlation tensor based on a time-varying graph neural network model, and outputting a quantitative diagnosis result; and finally generating a clinical classification report integrating the individualized brain network remodeling target, the disease progress risk layering and the treatment response prediction. By dynamically fusing the structure and functional features, comprehensive characterization of the pathological mechanism of the brain disease is realized, and decision support with both accuracy and interpretation is provided for clinical diagnosis.

The invention discloses a brain image analysis method and system based on multi-modal fusion, and relates to the technical field of brain image processing. A brain image analysis system based on multi-modal fusion comprises a data acquisition module, a data preprocessing module, a feature extraction and fusion module, a brain network analysis module, a comprehensive analysis module and a focus detection module. The comprehensive analysis model adopts a double-branch structure, deep processing is performed on multi-modal features and brain network features, and interactive fusion of the two types of features is realized through a cross-modal attention mechanism; the model is further combined with a classification branch and a regression branch to cooperatively complete brain disease classification and focus quantitative analysis, and the adaptive capacity and diagnosis performance of an existing model in a complex task scene are improved.

The present disclosure relates to a multi-modal brain network computation method associated with structural function, apparatus, device, and medium. The method is applied to train a brain disease prediction model, and the brain disease prediction model includes an association perception dual-channel generation module, a disease feature regression module, a topological structure discriminator, and a time-space joint discriminator. In a model training process, by performing a multi-level interactive fusion learning on a high-order topological feature of brain functional magnetic resonance data and magnetic resonance diffusion tensor imaging data, a multi-modal time series activity signal of each brain region is obtained.

The invention relates to the technical field of image processing, in particular to a craniocerebral disease area identification and detection method and system based on an MRI (Magnetic Resonance Imaging) image, and the method comprises the steps: obtaining a plurality of sub-images with different scales according to a gray level image of the craniocerebral MRI image; performing multi-scale analysis on the gradient value of any pixel point according to each sub-image to obtain a multi-scale gradient coefficient of any pixel point; obtaining a multi-scale local anomaly degree according to the gray values of the pixel points in different local ranges of any pixel point and the distribution in the gradient direction; optimizing the gradient value of any pixel point according to the multi-scale gradient coefficient and the multi-scale local anomaly degree to obtain a self-adaptive gradient value, and performing image enhancement on the grayscale image by using an anisotropic diffusion filtering algorithm according to the self-adaptive gradient value of each pixel point so as to identify a craniocerebral disease region. And the effect of performing image enhancement on the MRI image by using the anisotropic diffusion filtering algorithm is improved.

The invention discloses an artificial intelligence multi-modal medical image processing diagnosis and treatment system, and relates to the technical field of medical image processing and diagnosis and treatment, and the system collects multi-angle multi-modal brain image data, extracts brain region features based on multi-modal brain images, formulates a brain normal model, carries out the set fusion of the obtained brain region features, and carries out the diagnosis and treatment of the brain normal model. Generating a three-dimensional structure model of the brain area, compensating the three-dimensional structure model by using the treatment data of the patient to obtain patient features, and analyzing the patient features to obtain a diagnosis report; according to the method, the brain three-dimensional structure model is constructed by fusing the multi-modal image features and the patient treatment data, and the pathophysiological process of the brain diseases is simulated, so that the diagnosis accuracy and reliability are improved, and technical support is provided for early diagnosis and personalized treatment of the brain diseases.

The present disclosure discloses a multi-modal brain network calculation method, apparatus, device, and storage medium. The method is configured to train a brain disease prediction model. After the brain region structural feature and the brain region functional feature are separately extracted from magnetic resonance diffusion tensor imaging data and brain functional magnetic resonance data, a graph representation diffusion learning network is used to separate the universal feature and the unique feature in the brain region structural feature and the brain region functional feature. And then, multi-modal universal and unique feature fusion is implemented based on an alignment algorithm and adaptive weighting technology. Thus, complementary information between the multi-modal data is fully mining. The model can learn an effective feature of a related disease in a training process, and a finally obtained brain region disease prediction model has higher precision and better prediction effect.

The invention provides an intracranial neuron electrical stimulation system for treating epilepsy and other brain diseases involving epileptic seizure, which can immediately apply intracranial neuron electrical stimulation to the interseizure epilepsy activity in a personalized manner after detecting the interseizure epilepsy pattern each time. And the stimulation is activated by intracranial electroencephalogram (EEG) detection. The intracranial stimulation for epileptic activities in the epileptic seizure period continuously acts along with time, the forming process of an epileptic network can be blocked, the neuronal super-synchronization risk is reduced, and therefore the control effect on epileptic seizure is improved. The invention also includes the use of nerve adjustment biomarkers, thereby dynamically optimizing the detectability and providing more accurate overall assessment for the patient's treatment progress. Finally, according to the scheme, by detecting the intracranial EEG mode and the high-risk state mode (such as the epileptic state and the sudden accidental death risk in epilepsy) in the attack period, an instant alarm is given to the patient / nursing personnel, and it is ensured that safe treatment measures are taken for the patient in time.

Hydrogen-bonded framework nanoparticles comprising a chemiluminescent compound (e.g., L-012) are provided and can be used, e.g., for sono-optogenetic treatment of neurodegenerative diseases such as Parkinson's disease. Related in vivo and therapeutic methods are also provided.

The invention relates to the technical field of clinical treatment scheme recommendation. The invention relates to a clinical treatment scheme recommendation method based on brain diseases. The method comprises the following steps: S1, collecting brain examination data of a patient and other collectable body data, and then extracting disease features and other features of the brain; s2, collecting a historical clinical treatment scheme set and corresponding historical patient data, and combining the historical clinical treatment scheme set with the historical patient data to score the effect of the treatment scheme; according to the scheme adjusting model, the system can carry out personalized adjustment on the treatment scheme according to individual differences of patients, the unique characteristics of each patient are considered, the most suitable treatment scheme is customized for the patient, and meanwhile, the treatment effect of the treatment scheme is predicted by applying a simulation analysis technology; a doctor and a patient can roughly know effects possibly brought by different schemes before treatment, so that the doctor can select a scheme with a better curative effect expectation, and blind attempts are avoided.

The invention is applicable to the technical field of brain discipline, and provides a brain function connection modeling and coding method, device and equipment and a storage medium, the method comprises the following steps: constructing a first brain function connection graph according to rs-fMRI data, performing graph enhancement processing on the first brain function connection graph by adopting a preset adaptive graph enhancement strategy to obtain a second brain function connection graph, performing feature coding on the second brain function connection graph by adopting a preset topological attention coding strategy to obtain corresponding graph features, and performing feature projection on the graph features by adopting a preset feature projection strategy to obtain brain function discrimination features for brain function connection classification, therefore, the generated brain function discrimination features can accurately identify subtle differences of different types of brain function connections, and high-accuracy discrimination can be realized whether normal and abnormal brain function states or brain connection modes corresponding to different diseases, so that powerful support is provided for clinical aid decision making, and the accuracy of brain function discrimination is improved. And the accuracy and scientificity of brain disease diagnosis and treatment can be improved.

The invention discloses a brain disease multi-parameter monitor automatic testing device, and relates to the technical field of monitor testing, the brain disease multi-parameter monitor automatic testing device comprises a bottom plate, the upper side of the bottom plate is fixedly connected with a fixing cover, the front side of the fixing cover is fixedly connected with a controller, and the left side and the right side of the bottom plate are fixedly connected with supporting legs; a test unit is arranged on the upper side of the bottom plate, the test unit comprises a sliding groove, the sliding groove is formed in the upper side of the bottom plate, a sliding plate is slidably connected to the inner side of the sliding groove, and a vibration assembly and a traction assembly are arranged on the upper side of the sliding plate. According to the automatic testing device for the brain disease multi-parameter monitor, through the arrangement of the bottom plate, the sliding plate, the telescopic rod, the testing platform, the first rotating plate, the second rotating plate and the traction ring, a motor can conveniently control the monitor on the testing platform to repeatedly move left and right, meanwhile, a connecting cable of the monitor is dragged, complex scenes can be conveniently simulated, and the testing authenticity is improved.

The invention relates to a pharmaceutical composition comprising benzgalantamine and uses and methods for the preparation thereof. The present invention provides a pharmaceutical composition in the form of a tablet, said tablet comprising a tablet core, wherein said core comprises benzgalantamine (ALPHA-1062) or salt thereof, and an enteric coating, wherein said enteric coating is configured for dissolution at pH 5.5 and above. The pharmaceutical composition could be used in the treatment of a brain disease associated with cognitive impairment and / or with a cholinergic deficit. The invention further provides a method for preparing the pharmaceutical composition.

The invention discloses a brain disease judgment system and method based on fusion of a time sequence affinity graph and a multi-modal network. The method belongs to the technical field of artificial intelligence and medical image crossing. The invention provides a brain disease judgment system based on a time sequence affinity graph fused multi-modal network. The brain disease judgment system comprises a phenotypic feature reconstruction module, a feature extraction module, an affinity graph construction, processing and multi-modal fusion module, a loss construction module and a classification module. The method is used for distinguishing a brain disease patient group from a health control group, and is suitable for a medical image auxiliary diagnosis system, a multi-center brain disease screening platform and an individualized disease risk assessment tool. The core of the method is to solve the problems of insufficient utilization of single-mode information, poor multi-center data robustness and redundant graph structure noise in traditional brain disease classification through time sequence affinity graph construction and multi-mode feature fusion, ROI time sequence data and phenotypic data derived by resting state functional magnetic resonance imaging can be processed, and the accuracy of brain disease classification is improved. The method has application prospects in clinical transformation and multi-center collaborative research.

The invention discloses a brain disease risk prediction method and system based on big data analysis, and belongs to the technical field of brain disease risk prediction. The method comprises the following steps: carrying out standardized preprocessing and tagged classification on brain disease related big data to generate a feature data set; mining specific disease characteristics and risk factors in the set, and carding an association rule; training a risk prediction sub-model for each disease type based on the data, and building a multi-sub-model hierarchical prediction system; and collecting to-be-predicted object data, matching a disease type, and calling the corresponding sub-model to complete risk assessment. The system comprises multiple modules for collaborative operation, and a full-process closed loop of data storage, feature processing, model management and result output is realized. According to the scheme, the pertinence, the accuracy and the efficiency of risk prediction are improved, the traceability of the whole process and the dynamic optimization of the model are realized, and reliable technical support is provided for early screening and risk early warning of brain diseases.

The invention belongs to the technical field of medical nano-materials, and particularly relates to a lactoferrin nano-material for sonodynamic regulation and control of blood-brain barrier permeability as well as a preparation method and application of the lactoferrin nano-material. The nanomaterial is nanoparticles LCe6 formed by self-assembly of lactoferrin and chlorin e6, can target brain microvascular endothelial cells, and can temporarily, reversibly and targeting open BBB by using the sonodynamic effect of the LCe6 in combination with transcranial ultrasonic stimulation, so that the accumulation of the nanoparticles LCe6 at a focus part is improved; in addition, the nanoparticle LCe6 also has a targeting property on brain tumor cells, the tumor cells can be accurately killed by utilizing the sonodynamic effect of the nanoparticle under secondary transcranial ultrasound, and the effect of sonodynamic treatment on brain glioma is exerted to the maximum extent; the nanoparticles LCe6 have excellent biocompatibility and low toxicity; according to the invention, the intracerebral delivery efficiency of the drug can be obviously improved, and a novel method for targeting open brain disease BBB is provided.

The invention relates to a pharmaceutical composition in the form of a tablet, said tablet comprising a tablet core, wherein said core comprises benzgalantamine (ALPHA-1062) or salt thereof, and an enteric coating, wherein said enteric coating is configured for dissolution at pH 5.5 and above. Further the invention relates to the pharmaceutical composition for use in the treatment of a brain disease associated with cognitive impairment and / or with a cholinergic deficit. The invention further relates to a method for preparing the pharmaceutical composition.

The invention discloses a multi-mode brain dysfunction auxiliary diagnosis method based on a dynamic function connection network. A two-stage collaborative learning framework from an individual brain graph to a group relation graph is constructed. Firstly, an individual multi-modal fusion brain map is constructed, node features of the individual multi-modal fusion brain map are obtained through node regularization regression analysis of an rs-fMRI time sequence, an adjacent matrix is obtained through calculation of the brain interval grey matter volume difference of a T1 image, and individual enhancement characterization is obtained through map convolutional network fusion. And then constructing a group relationship enhancement graph, taking individual representation as node features, constructing a dual-channel adjacency relationship for distinguishing homologous / heterologous connection according to age and gender, obtaining final discriminative representation through dual-channel graph attention network aggregation, and performing classification diagnosis according to the final discriminative representation. According to the method, deep fusion of multi-modal information and explicit modeling of key biological variables are realized, and an effective tool is provided for accurate and explainable auxiliary diagnosis of brain diseases.

The invention provides a stem cell exosome for overexpressing A beta degrading enzyme as well as a preparation method and application of the stem cell exosome. Specifically, the invention provides an application of the stem cell exosome overexpressing A beta degrading enzyme in preparation of drugs, and the drugs are used for at least one of the following: prevention, improvement or treatment of cognitive impairment related diseases; neuronal cell damage is improved; wherein the stem cells for overexpressing the A beta degrading enzyme are prepared through lentivirus transfection. According to the stem cell exosome overexpressing the A beta degrading enzyme, the content of the A beta degrading enzyme is obviously higher than that of the exosome prepared by a traditional method. Therefore, when the compound is prepared into a medicine for treating or preventing cognitive impairment related diseases, the medicine can be efficiently conveyed to the brain, and the concentration of the medicine in the brain is remarkably improved, so that the curative effect is enhanced, the dosage of the medicine can be reduced, the side effect is reduced, and the prognosis of brain diseases is effectively improved.

The invention discloses a blood brain barrier water extraction rate imaging method and system based on artery marking and speed selection. By marking spinning of water molecules in artery blood and selectively detecting signals of residual marked blood which does not enter brain tissues in vein blood vessels, quantitative evaluation of blood-brain barrier water permeability is realized. According to the method, the neck artery blood flow is magnetized and overturned by adopting pseudo-continuous artery spin labeling, a speed selection module based on Fourier transform is applied in an acquisition stage to eliminate interference of brain tissue signals, pure vein labeling signals are obtained, and rapid and accurate measurement of water extraction fraction and permeability-surface area volume is realized. In addition, the signal-to-noise ratio and robustness are effectively improved by adopting dual suppression pulses of background tissues and blood. The method does not need an exogenous contrast agent, is non-invasive, short in scanning time and accurate and stable in measurement result, and has important clinical application value for early diagnosis of brain diseases and assessment of pathological changes of the blood brain barrier.

The invention discloses a medical image intelligent monitoring device for brain diseases, which comprises a device control unit for acquiring learning data, a central control unit connected with the device control unit and used for receiving data of the device control unit, generating a control instruction of the device and analyzing the state of the device, comprising an environment control module, a storage module, a safety verification module, a patient condition optimization module and a learning analysis module, the detection alarm unit is connected with the central control unit and is used for receiving the equipment parameters of the central control unit, monitoring the running state of the equipment under the condition of a patient in real time and carrying out fault analysis; the communication control unit is electrically connected with the equipment control unit, the central control unit and the detection early warning unit, is used for data transmission communication among the units, and comprises a wireless module and a communication module; the control assembly comprises a screen display part and a control handle part connected with the screen display part.

The invention discloses a nucleic acid medicinal preparation based on dialkyl imidazole cationic lipid as well as a preparation method and application thereof, and belongs to the field of medicinal preparations. The nucleic acid pharmaceutical preparation comprises drug-loaded lipid nanoparticles, and the drug-loaded lipid nanoparticles comprise one or more dihydrocarbyl imidazole cationic lipids. By adjusting lipid composition and proportion in the drug-loaded lipid nanoparticles, efficient loading and delivery of different types of nucleic acid drugs can be realized; the drug-loaded lipid nanoparticles have good biological safety, the preparation process is simple, and large-scale production is easy; the drug-loaded lipid nanoparticle can effectively pass through a blood brain barrier, realizes specific brain-targeted delivery of the loaded nucleic acid drug, and provides a new strategy for realizing brain-targeted delivery of different nucleic acid drugs for treating brain diseases.

The invention discloses a cerebral apoplexy recurrence risk monitoring method and device and a medium, and relates to the technical field of medical health monitoring, the cerebral apoplexy recurrence risk monitoring method comprises the following steps: according to a preparation result, collecting electroencephalogram, oxyhemoglobin saturation, electrocardio, pulse waves and acceleration signals, synchronously recording timestamps, and generating multi-modal physiological data; performing de-noising processing and feature extraction on the multi-modal physiological data to generate de-noised feature data; performing multi-modal feature fusion on the de-noised feature data by adopting a convolutional neural network to generate a multi-modal feature vector, identifying feature signal modes of epilepsy, brain structures and brain diseases according to the multi-modal feature vector, calculating a cerebral apoplexy recurrence risk score, and generating a risk score result and an anomaly identification report; and carrying out risk grade division on the risk scoring result and the abnormity identification report according to a recurrence risk threshold value and a personalized judgment rule, and generating risk early warning information and personalized intervention suggestions. According to the invention, real-time and explainable risk early warning information is provided for clinicians and patients.

The invention relates to the technical field of brain anomaly detection and artificial intelligence auxiliary diagnosis, in particular to a graph convolutional network brain disease diagnosis method based on sub-graph sampling and multi-feature fusion, and aims to improve the accuracy of brain disease diagnosis. The method comprises the following steps: obtaining resting state functional magnetic resonance imaging data, preprocessing the data, and constructing a brain function connection diagram; and the brain function connection graph represents a brain interval collaborative activation relationship in a graph structure. Subgraph sampling is carried out based on function module division and node degree sorting, and an initial subgraph set is generated; and performing optimization selection on the initial sub-graph set by utilizing reinforcement learning to obtain an optimal sub-graph, introducing a node attention mechanism into the optimal sub-graph, screening key nodes based on attention scores, and generating a discriminant sub-graph. And extracting and fusing position features, neighborhood features and structural features of the discriminant subgraphs, and performing brain disease diagnosis based on the fused features.

The invention discloses oral brain-targeted nanoemulsion as well as a preparation method and application thereof, and belongs to the field of biological medicine preparations. Drug molecules and a stabilizer are dispersed in oil phase molecules, and then a surfactant and water are added to prepare the nano-emulsion. Drug molecules are dispersed in oil-phase molecules and then form an oil-in-water nanoemulsion with water, so that the problems of poor stability and poor water solubility of current drug molecules are solved, the oral bioavailability of the drug is improved, fusion of different drug molecules of multiple targets is realized, the drug loading capacity of the drug molecules can be effectively ensured, and the bioavailability of the drug is improved. In addition, the drug can pass through an intestinal barrier and a blood brain barrier to be successfully delivered to a brain region to realize multi-target treatment, so that efficient treatment of multi-target brain diseases such as neurodegenerative diseases and central nervous system tumors is realized.

The invention provides a nasal delivery preparation for cerebral diseases as well as a preparation method and application of the nasal delivery preparation, and relates to the technical field of pharmaceutical preparations. The nasal delivery preparation for the cerebral diseases comprises gelatin derivative chitosan microspheres coated with stem cell exosomes, and the microspheres are prepared through the method that the stem cell exosomes are coated with gelatin derivatives of a specific structure and then cross-linked with chitosan. The gelatin derivative used in the invention has good surface activity, and can be well compatible with cells and active factors to form a stable inner core coating structure; meanwhile, the surface of the chitosan of which the outer layer is crosslinked with the gelatin derivative is positively charged and can be combined with anions in nasal mucus, so that the retention time of the microspheres in the nasal cavity can be effectively prolonged. Therefore, the nasal delivery preparation not only can slowly release the stem cell exosome, but also improves the absorption efficiency of the stem cell exosome by the nasal cavity, so that the stem cell exosome can reach the brain more effectively.

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 present invention proposes a new method for presenting information relating to the presence of a brain disease on the basis of the input of brain waves.　Provided is an information processing method for an information processing device, the information processing method comprising: calculating, on the basis of a time-frequency analysis, time-frequency information in a set time range from brain wave signals acquired from a plurality of electrodes; and calculating a value relating to the presence of a brain disease on the basis of the time-frequency information.

The invention relates to the technical field of medical artificial intelligence, and discloses a brain disease prediction method based on double encoders and a diffusion model, and the method comprises the steps: carrying out the preprocessing of a functional magnetic resonance image, and constructing a brain function network; data enhancement of semantic preservation is achieved through a diffusion model, a dual random matrix and a cosine scheduling strategy are adopted in the noise adding process, and a GraphTransform neural network containing global topological features is utilized in the denoising process; the spatial features of the brain network and the time dynamic features of the BOLD signals are respectively extracted by using double encoders; designing a triple contrast learning mechanism to optimize cross-dimension feature interaction; and finally migrating to a downstream classification task to realize disease prediction. Small sample overfitting is relieved through diffusion enhancement, and the diagnosis reliability is improved; fusing spatial-temporal characteristics to assist multi-dimensional pathological analysis; and the cross-site adaptability of the model is enhanced, and collaborative analysis of multi-center heterogeneous data is supported. The method is suitable for auxiliary diagnosis of cerebral diseases such as infantile autism.

The invention discloses a multi-modal feature fusion prediction method and system for early screening of Alzheimer's disease, and belongs to the technical field of brain disease prediction. The method comprises the following steps: acquiring multi-dimensional data containing cognitive test scores, brain image scanning results and biomarker concentration levels from a patient record database, and performing standardization processing to obtain a multi-dimensional data set in a unified format; key feature vectors are extracted through a dimensionality reduction analysis method to capture the covariant relation between cognitive test scores and brain image changes; when the reduction range of the cognitive test score exceeds a preset threshold value and the brain image displays an atrophy sign, a classification model is constructed through an integrated learning method to preliminarily classify abnormal signals; and fusing the biomarker concentration level to obtain an abnormal signal vector. According to the method, the accuracy and efficiency of early screening of brain diseases such as Alzheimer's disease are remarkably improved through accurate evaluation of key parts of the human brain, such as hippocampus.