244 results about "Brain disease" patented technology

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.

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 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 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 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 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.

The invention relates to the technical field of intelligent medical treatment, in particular to an AI-based brain disease test data management intelligent question-answering system, which comprises a multi-modal data acquisition module, an intelligent data processing module, an AI core model module, an interaction service module and a treatment guidance module, data aggregation is realized through a hospital interface, an equipment protocol and the like, and cleaning, standardization and multi-modal fusion are performed on multi-source data; precise question answering is achieved through the intelligent question answering unit, brain disease classification and stage division are completed through the patient layering unit, and a personalized scheme is generated by means of the treatment scheme generation unit; the interaction mode can be dynamically adjusted according to the cognitive level of a patient, emotion is recognized, guidance is provided, a doctor is supported to correct a scheme, and an iterative model is learned through feedback. The problems that in the prior art, the disease type is single, the data dimension is limited, and interaction adaptation is insufficient are solved, multi-disease-type whole-course precise management of brain diseases is achieved, and scheme scientificity and doctor-patient interaction experience are improved.

The application discloses a kind of group level fMRI brain function network analysis methods based on graph convolutional neural network, comprising: obtaining the fMRI of the brain of multiple groups of different categories of subjects, after preprocessing, establish brain function network for each subject, carry out single sample t test to each group brain function network, calculate the graph theory attribute of node as node feature;Establish and train the classification model of GCN, the input of neural network is the edge of brain function network, node feature, and the output is the category of subject;According to the explainability of GCN, find the most important subgraph structure for classification, the subgraph represents the biggest brain function connection of the difference of brain function network between different groups, and can be used for further analysis on the neural mechanism of brain disease.The application can more comprehensively compare the brain function network of different groups, obtain more accurate results, and can be widely used in aphasia, depression, alzheimer's disease and other brain function network analysis of brain disease.

The application discloses a drug-loaded micelle capable of effectively crossing the blood-brain barrier, which is an amphiphilic conjugate composed of a reduction-sensitive paclitaxel prodrug and a nucleic acid complex, wherein the reduction-sensitive paclitaxel prodrug is used as a hydrophobic part, and the nucleic acid complex is used as a hydrophilic part, and the drug-loaded micelle is self-assembled in an aqueous environment; the reduction-sensitive paclitaxel prodrug is formed by the reaction of paclitaxel and a disulfide bond-containing linker; and the nucleic acid complex is formed by connecting an antisense oligonucleotide and an interfering RNA through a DNA bridge. The drug-loaded micelle exhibits superior blood-brain barrier penetration, effectively realizes enrichment in brain tumors, solves the problem of low blood-brain barrier penetration efficiency of existing nano-carriers, and provides an effective basis for brain drug delivery and brain diseases such as brain tumor imaging and treatment.

The invention relates to the technical field of brain disease target positioning, in particular to a brain disease target positioning system and method based on functional magnetic resonance imaging. The method comprises the following steps: calculating a whole brain function connection map of an individual patient based on 4D rs-fMRI data, calculating a Z score of each voxel function connection strength in the whole brain function connection map of the individual patient, and screening abnormal voxels based on a preset threshold value; aggregating the abnormal voxels through a clustering algorithm so as to determine a cluster; the therapeutic effect scores of the multiple clusters can be predicted through the machine learning model, the clusters and the candidate therapeutic targets can be screened based on the therapeutic effect scores, the positions of the candidate targets can be accurately determined, and multiple selections of the candidate targets can be provided; the target spot is converted into an accurate target spot suitable for an individual through a nonlinear registration algorithm; and determining a target positioning state according to the deviation between the accurate target and the actual treatment target, and adjusting corresponding parameters based on the target positioning state. The invention provides a plurality of targets and effectively determines the positions of the targets.

The invention discloses a brain disease dynamic monitoring model construction method based on multi-modal data fusion, relates to the field of model fusion, solves the problem of insufficient accuracy of existing brain disease dynamic monitoring, and comprises the following steps: S1, obtaining monitoring data; s2, performing data processing on the monitoring data, counting a time sequence change value of the monitoring data, analyzing the time sequence change value of the monitoring data, judging a correlation value of an examination item and a brain disease, sorting the examination items of the brain disease, and optimizing the examination items; s3, constructing a brain disease feature vector and brain disease multi-modal data model; performing data fusion on the brain disease feature vectors according to a multi-modal data model to obtain fused data; s4, training is carried out based on the feature vectors and the fusion data, and a dynamic monitoring model is constructed; the brain disease dynamic monitoring accuracy can be effectively improved.

The invention discloses a dynamic brain network analysis method guided by neural heterogeneity, and is suitable for the technical field of brain image processing and recognition. The method comprises the steps that functional magnetic resonance imaging data are acquired and preprocessed, an overlapped sliding window is used for dividing the functional magnetic resonance imaging data to construct a dynamic functional brain network, and then the dynamic functional brain network is decoupled into a topological consistency network and a time trend network which conform to brain activities; capturing space and time heterogeneity weights of different brain regions in the brain based on a topological consistency network and a time trend network, and identifying key nodes for driving brain network recombination; further weighting the topology consistency network and the time trend network to obtain a heterogeneity dynamic function brain network; propagation of neural information in a time dimension is simulated based on time propagation graph convolution operation, and spatial-temporal features of brain images are extracted from a heterogeneous dynamic function brain network; and finally, inputting the obtained spatial-temporal characteristics of the brain image into a multi-layer perceptron to predict the data category of the brain image to be recognized, and analyzing the influence of the brain disease image characteristics on the spatial-temporal heterogeneity of the brain region to complete the dynamic brain network analysis guided by the neural heterogeneity.

The present invention relates to a device for preventing, alleviating or treating degenerative brain diseases by inducing brain wave synchronization through acoustic vibration tactile stimulation and bone conduction music, and a method thereof. More specifically, the present invention relates to a wearable device which is worn on the head of a wearer, stimulates the brain by providing Vibroussis composed of 40 Hz vibration and sound to the forehead lobe and the temporal lobe of the wearer, and recovers the gamma oscillation of the brain to normal by increasing the cerebral blood flow. Therefore, the device and the method thereof can prevent, relieve or treat degenerative brain diseases (such as mild cognitive impairment or early Alzheimer's disease (dementia)).

This invention relates to the field of medical information processing technology and discloses a method and system for predicting brain diseases in medical assessments of the elderly. By introducing methods such as discontinuous response boundary recognition, stage division, interruption semantic description, and interactive correlation calculation, the method distinguishes between long pauses caused by external interference and pathological delays in responses caused by abnormalities related to brain diseases within the same processing framework. This not only improves the accuracy and stability of brain disease risk prediction results in discontinuous response scenarios but also generates stage risk results and abnormal contribution results from interactive units. This provides a clearer basis for interpreting results in medical assessments of the elderly and addresses the problem that existing technologies cannot effectively distinguish between exogenous interruptions and pathological cognitive delays in discontinuous response scenarios in the elderly, leading to poor accuracy in brain disease prediction.

The present invention relates to a use of a methylated peptide as a therapeutic agent for degenerative brain diseases. Specifically, it has been confirmed that the methylated peptide according to the present invention exhibits improved solubility, bioavailability, phagocytosis by microglia, and anti-inflammatory efficacy compared to a conventional unmodified peptide, and exhibits excellent amyloid beta reduction efficacy compared to a conventional unmodified peptide in an animal model of degenerative brain diseases. Therefore, the methylated peptide according to the present invention can be usefully used as an active ingredient of a composition for preventing or treating degenerative brain diseases and cognitive impairment, learning disability, or memory impairment associated therewith.

The purpose of the present invention is to provide a novel blood-brain barrier permeability regulator.　The present invention pertains to a blood-brain barrier permeability regulator containing a compound represented by formula (I) (Symbols in the formula (I) are as defined in the present specification.) or an isomer thereof, or a salt thereof. The present invention also pertains to a medicine for preventing and / or treating various brain diseases, severe infections, or sepsis by using the compound or an isomer thereof, or a salt thereof alone or in combination with a drug for preventing and / or treating a brain disease.

A method for assessing a patient's brain disease state from brain images includes acquiring the brain images, partitioning them into predefined regions based on anatomical landmarks, extracting disease-indicative features, using a pretrained model to generate a disease-associated biomarker from the features, and determining the patient's brain disease state based on the biomarker.

The application provides a portable magnetic resonance imaging-based brain disease remote diagnosis system, and belongs to the cross field of medical imaging and remote medical treatment.The system comprises a portable magnetic resonance imaging device, a remote data transmission and cooperation network and a remote diagnosis and AI auxiliary workstation; wherein the portable magnetic resonance imaging device is arranged in a primary medical institution, is used for performing standardized scanning on a patient's brain disease under a low-field magnetic field, and acquires brain image data of the patient; the remote data transmission and cooperation network is used for connecting the device end and the diagnosis end, and is used for transmitting the brain image data to the remote diagnosis and AI auxiliary workstation; and the remote diagnosis and AI auxiliary workstation is used for performing brain disease analysis based on the brain image data and generating a structured report, and the report is returned to the primary medical institution.The application combines remote medical treatment and on-site magnetic resonance scanning, optimizes medical resources, and improves diagnosis effect.

In one aspect, the invention relates to an amorphous solid form of Alpha-1062 gluconate. In another aspect, the invention relates to methods of preparing an amorphous solid form of Alpha-1062 gluconate. Also disclosed are methods for treating a brain disease associated with cognitive impairment and / or with a cholinergic deficit in a subject, including administering the amorphous form of Alpha-1062 gluconate to a subject in need thereof.

The present disclosure provides antibodies that specifically bind to human FAM19A5 and compositions comprising such antibodies. In a specific aspect, the antibodies specifically bind to human FAM19A5 and modulate FAM19A5 activity, e.g., inhibit, suppress, reduce, or reverse the onset of reactive gliosis and / or excessive proliferation of reactive astrocytes, utilizing such antibodies. The present disclosure also provides methods for treating disorders, such as central nervous system damage, a degenerative brain disorder, or a neuropathic pain, by administering an antibody that specifically binds to human FAM19A5.

This invention relates to the field of intelligent brain disease diagnosis technology, specifically providing an auxiliary diagnostic method and system based on multimodal decoupled dynamic graph learning. The method includes: acquiring and preprocessing multimodal data (such as neuroimaging, genetic markers, etc.) of the subject; extracting common pathological information and modality-specific features through a shared encoder and modality-specific encoders respectively, and optimizing the separation process using a decoupling loss function; furthermore, fusing all modality embeddings using a multi-head self-attention mechanism with a masked matrix to generate initial node representations, where the mask is used to suppress modality self-attention; subsequently, performing hierarchical dynamic graph convolution based on the node representations: in each layer, dynamically updating the graph adjacency matrix by combining the current node representation with the original features, and iteratively optimizing the node representations through message passing; finally, inputting the optimized representations into a classifier to obtain disease prediction results. This invention improves the automation performance and reliability of diagnosis.

The invention provides a method for predicting pathogenicity of functional non-coding copy number variation in brain diseases, and belongs to the technical field of bioinformatics. Comprising the steps that brain-related features are collected to construct a brain-related feature set, and the types of elements in the brain-related feature set comprise transcription factor binding sites, histone modification and chromatin accessibility; constructing a hybrid model fusing two-dimensional convolution and swing-transformer for capturing collaborative regulation characteristics of brain-related cis-regulatory elements in the DNA sequence and generating N-dimensional brain-related functional feature annotations, and training the hybrid model by using a brain-related feature set; cNV data sets of the two pieces of brain-related copy number variation data are constructed, the data types in the mixed data set comprise coding and non-coding, and the high-confidence non-coding data set only comprises non-coding data; training a random forest model based on the two CNV data sets; the hybrid model and the random forest model jointly form a DeFunCNV model, and the pathogenicity of functional non-coding copy number variation in brain diseases is predicted.

This application provides a disease intelligent diagnostic device based on brain structural connectivity identifiers and its application. The device includes (1) a data acquisition module; (2) a data processing module: reconstructing the spin distribution function of the data and distributing the spin distribution function in a standard space; (3) a data projection module: projecting the spin distribution functions of patients and healthy individuals into the standard space to obtain the Z-value of the maximum direction within the voxel; and (4) a Connectome Identifier extraction module: reducing the dimensionality of the Z-value to form a 1-dimensional feature value. The device of this application can be used for the diagnosis of various brain diseases and the determination of lesion location and severity.

The invention provides a coenzyme Q10 nano-liposome capable of being endocytosed by brain neurons as well as a preparation method and application of the coenzyme Q10 nano-liposome, and belongs to the technical field of biological medicines. According to the coenzyme Q10 nano-liposome and the preparation method thereof, polycarbonate filter membranes of 220 nm, 100 nm and 50 nm are adopted to sequentially extrude a solution, the coenzyme Q10 nano-liposome which has the particle size range of 30-50 nm, is not prone to melt collapse and can be endocytosed by neurons is prepared, and the preparation method is simple and efficient. According to the invention, nano-liposomes simultaneously wrapping 5nm gold and coenzyme Q10 are further prepared, distribution of the nano-liposomes can be traced through a transmission electron microscope, and whether the drug coenzyme Q10 enters extracellular gaps of brain cells and whether the drug coenzyme Q10 is endocytosed into neurons or not is verified. The coenzyme Q10 nano-liposome disclosed by the invention can penetrate through a blood brain barrier, enter extracellular gaps and are endocytosed by neurons to eliminate formaldehyde accumulated in the brain, so that a foundation is provided for research and development of drugs for brain diseases such as Alzheimer's disease.

Provided is a structure composed of a cell population comprising endothelial cells, astrocytes and pericytes, and a 3D (three dimensional) cell growth material within which the cell population is located. The structure has a TEER value of at least 450 Ω / cm2. The cells of the structure may be derived from the brain. The cells may be human cells, and in particular may be primary derived non-immortalised cells. The structure is particularly suited for use in a model of the blood brain barrier, and the invention also provides such a model. The structure is located in a container, in which it separates a first chamber located on a first side of the structure and a second chamber located on a second side of the structure. The first and second chambers respectively contain first and second liquids in contact with first and second sides of the structure. The liquids mimic the brain extracellular fluid and the blood. The blood brain barrier model provided may be used in models of brain disease, and to investigate uptake of agents into the brain or diseased brain.