152 results about "Brain disease" patented technology

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 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 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.

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.

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.

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.

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.

The present invention relates to an ACCS gene, a protein and the use thereof. An involved ACCS protein mutant is formed by means of mutating a proline residue at position 134 and a tryptophan residue at position 136 of an animal-derived wild-type ACCS protein into a glutamine residue and a tyrosine residue, respectively. The involved ACCS protein mutant can be used in the intervention and treatment of a variety of diseases comprising brain diseases, and provides a new regimen and approach for the intervention and treatment of brain diseases.

This invention provides a method and system for predicting brain diseases by fusing amplitude and phase information with graph-aware hybrid expert graph neural networks. It relates to the field of neuroimaging analysis technology. The method includes: acquiring raw resting-state functional magnetic resonance imaging (fMRI) data of a subject and corresponding brain disease category labels; preprocessing the raw resting-state fMRI data; extracting the mean oxygenation level dependent signal time series of multiple brain regions of the subject and using the mean oxygenation level dependent signal time series as the signal time series; constructing a functional connectivity matrix and a phase adjacency matrix based on the signal time series; constructing a training dataset; constructing a brain disease prediction model based on a two-branch hybrid expert graph neural network; training the brain disease prediction model using the training dataset; acquiring resting-state fMRI data; inputting the resting-state fMRI data into the trained brain disease prediction model for prediction, and outputting the brain disease prediction result.

The invention relates to the technical field of intelligent brain disease diagnosis, and particularly provides an auxiliary diagnosis method and system based on multi-modal decoupling dynamic graph learning, and the method comprises the steps: obtaining and preprocessing the multi-modal data (such as nerve images and genetic markers) of a subject; common pathological information and modal unique features are extracted through a shared encoder and modal specific encoders respectively, and a decoupling loss function is utilized to optimize a separation process. Furthermore, a multi-head self-attention mechanism with a mask matrix is adopted to fuse all modal embedding, a node initial representation is generated, and the mask is used for inhibiting modal self-attention. Then, hierarchical dynamic graph convolution is carried out based on node characterization, in each layer, a graph adjacency matrix is dynamically updated in combination with current node characterization and original features, and the node characterization is iteratively optimized through message passing; and finally, inputting the optimized representation into a classifier to obtain a disease prediction result. According to the invention, the automation performance and reliability of diagnosis are improved.

The invention provides a biotinylated Angiopep-2 modified targeting nano material as well as a preparation method and application of the biotinylated Angiopep-2 modified targeting nano material. According to the preparation method of the biotinylated Angiopep-2 modified targeting nano-material, Angiopep-2 is directionally anchored through a high-affinity biotin-streptavidin system, ligand conformation inactivation caused by chemical coupling is avoided, targeting activity is maintained, the used Angiopep-2 can help nanoparticles to be specifically recognized and combined and mediate the nanoparticles to cross a blood brain barrier to enter the brain, and the targeting nano-material can be used for preparing the targeted nano-material. The drug concentration in brain tissues is increased, so that the cure rate of brain diseases is improved, the brain targeting efficiency is far higher than that of transferrin and lactoferrin, and the application value is great; by entrapping curcumin and quercetin, a hydrophilic shell is provided, so that the water solubility of curcumin and quercetin is improved, and the bioavailability of curcumin and quercetin is improved.

Resting-state magnetic resonance imaging (fMRI) technology records brain activity by measuring the global blood oxygen saturation of the subject's brain, providing high spatial resolution data. Although the temporal resolution is lower, in elderly individuals at rest, the introduction of time series data still allows for good observation of the hemodynamic direction of individual pixels, making it one of the important evaluation indicators for intervention and treatment of brain diseases. The neural circuit features constructed based on a single brain region towards the whole brain are also an important evaluation method for fMRI data, namely, the quantification of the physiological interaction of the brain region towards the whole brain in a certain state. This invention constructs a method for quantifying the treatment effect of Alzheimer's disease (AD) based on resting-state magnetic resonance imaging (fMRI) technology. This method mainly addresses: (1) the difficulty in quantifying the rehabilitation effect during the treatment of AD patients; and (2) the quantification of the rehabilitation effect of different treatment modes during the treatment of AD patients.

This invention discloses a brain disease classification and prediction method and system based on a multimodal topological sensing graph network, relating to the field of medical image processing technology. By fusing temporal signals from functional magnetic resonance imaging (fMRI) and spatial structure signals from diffusion tensor imaging (DTI), a spatial structure prior matrix is ​​introduced as a topology modulator to impose physical connectivity constraints on the constructed multi-view functional interaction network. Furthermore, a spatiotemporally coupled global fusion graph is generated through a cross-modal attention mechanism. Graph neural networks are used to extract local nodes, mesoscale subnetworks, and global topological statistical features to construct a global state feature vector. Finally, a two-stage optimization learning framework is employed, combining multi-objective self-supervised pre-training and supervised fine-tuning to improve the model's classification performance and robustness under small sample conditions. This invention effectively solves the problems of functional connectivity redundancy, lack of structural constraints, and loss of topological information in traditional methods, significantly improving the accuracy and interpretability of brain disease classification.

The invention relates to an antibody which binds to myelin oligodendrocyte glycoprotein (MOG), an antibody fragment thereof, a hybridoma which produces the antibody or the antibody fragment, a nucleic acid containing a nucleotide sequence which encodes the antibody or the antibody fragment, a transformant cell containing a vector containing the nucleic acid, a method for producing the antibody or the antibody fragment, a composition containing the antibody or the antibody fragment and a method for detecting or measuring an antigen that is present in the brain, a method for diagnosing or treating a brain disease, a method for improving the property of an antibody of accumulating in the brain and a method for increasing the amount of an antibody in the brain which use the antibody or the antibody fragment.

The application discloses a brain-inspired hierarchical dynamic network analysis method, belongs to the deep learning technology in brain image data recognition classification technology, and first carries out data preprocessing on functional magnetic resonance signals; then a signal decomposition module guided by neural dynamics is constructed to decompose the functional magnetic resonance signals into periodic signals and trend signals; then neural weight activation learning is introduced to capture the activation weights of the neural nodes under different levels of signals, so as to represent the activation information transmission of the neural nodes; in addition, a dynamic subnetwork is constructed by using multi-level dynamic activation signals, and a MOE with subnetwork distillation is designed to simulate the functional differentiation and cooperation of the subnetwork; further, a self-attention mechanism is introduced to realize adaptive fusion of information dependence between the subnetworks, so that a multi-level spatiotemporal representation of the functional magnetic resonance signals is obtained; finally, the obtained whole-brain level features are sent into a multilayer perception mechanism, which is used for diagnosis and decision of brain disease image data.

In the present invention, a deuterium-substituted FP-CIT linear precursor is developed in order to increase the linear stability of a precursor. When the deuterium-substituted FP-CIT linear precursor according to the present invention is dissolved in an organic solvent and stored, linearity is maintained well even at room temperature, and the same effect as a conventional radiopharmaceutical product [18F]FP-CIT is exhibited. In addition, D6-[18F]FP-CIT can be prepared simply and in a high yield, and thus can be effectively used as a starting material for the synthesis of D6-[18F]FP-CIT. Furthermore, a novel radiopharmaceutical product D6-[18F]FP-CIT enables visualization of dopamine transporter (DAT) distribution to be performed through improved image contrast, and has particularly high accuracy in diagnosing Parkinson's disease and related degenerative brain diseases, and thus can be effectively used.

The application provides a brain disease AI prediction method and system based on prototype enhanced multi-instance learning, comprising: regarding the electroencephalogram signal of each subject as a bag, regarding each slice segment in the bag as an electroencephalogram instance, inputting the electroencephalogram instance into an instance coding module, performing electroencephalogram instance microstate multi-scale time sequence feature detection and space-time domain joint representation, obtaining space-time domain joint representation features of the electroencephalogram instance, inputting into a cross-instance self-attention modulation module to obtain modulated instance representation, inputting into a prototype calibrated enhanced multi-instance aggregation module, using the constructed and updated prototype as a representative mode of the instance in the bag, calibrating and utilizing prototype guided aggregation to obtain aggregation features, inputting into a classification head and outputting subject level brain disease prediction probability. The application can predict brain diseases.

The present invention pertains to the anti-inflammatory therapeutic effect of xantohumol in spinal cord injury (SCI). The continuous administration of xanthohumol for 1-8 weeks to SCI rats resulted in improved results in 4 clinical tests used and in lowering and faster elimination of macrophages from the SCI lesion. Since the infiltration of the SCI lesion by numerous phagocytic macrophages indicates a severe destructive inflammation of extraordinary longevity, administration of xanthohumol is neuroprotective and resulted in a better and faster recovery of the locomotor function and the strength and sensory function in the hind limbs, in a shorter period of paralysis of the urinary bladder and in the recovery of the body weight lost due to the SCI surgery. Since the traumatic brain injury (TBI) involving the white matter, and stroke involving the white matter initiate the severe destructive inflammation as in the SCI, the administration of xanthohumol is expected to be anti-inflammatory and neuroprotective in both brain diseases. Since neurodegenerative diseases including Alzheimer's disease, frontotemporal dementia and Parkinson's disease, immune mediated neuroinflammation including multiple sclerosis and neuromyelitis optica and cerebrospinal infections have inflammatory pathogenesis involving microgliosis and infiltration by macrophages, the administration of xanthohumol is expected to result in therapeutic inhibition of progression of all above diseases as well as of related neurodegenerative, immune mediated and infectious diseases of the brain and of the spinal cord.

This application discloses a treatment auxiliary device for patients with brain diseases in traditional Chinese medicine internal medicine, including a main body and auxiliary components. The auxiliary components include a lifting mechanism disposed on the main body, and a support mechanism is disposed on the lifting mechanism. In this embodiment of the treatment auxiliary device for patients with brain diseases in traditional Chinese medicine internal medicine, by pulling the limiting plate to adjust the compression of the push spring, the thrust on the stabilizing rod can be temporarily reduced, facilitating the smooth sliding of the lifting block to the ideal height below the patient's neck. After positioning, the limiting plate is adjusted again to enhance the spring thrust, ensuring the stabilizing rod is firmly embedded in the stabilizing groove, ensuring the support structure has reliable load-bearing capacity. Simultaneously, thanks to the structural characteristics of the arc-shaped contact surfaces, the device retains a certain degree of compliance in the locked state, enabling micro-adaptive height adjustment with slight head tilting or lowering movements of the patient. This provides stable support while avoiding neck fatigue and discomfort caused by traditional rigid locking, effectively balancing stability and comfort.

The present invention relates to a DNA aptamer specifically binding to an alpha-synuclein protein and uses thereof. Specifically, the present invention relates to a DNA aptamer selected from the group consisting of nucleotide sequences of SEQ ID NOS: 1 to 12 and binding specifically to an α-synuclein protein, and a composition for detecting an α-synuclein protein, a detection kit, a detection chip or a microarray, all comprising the aptamer as an active ingredient. In addition, the present invention relates to a method for detecting an α-synuclein protein, a method for providing information for diagnosing a degenerative brain disease, a composition for diagnosing a degenerative brain disease, and a pharmaceutical composition for preventing or treating a neurodegenerative disease, all using the DNA aptamer of the present invention.

One aspect relates to a composition for preventing or treating degenerative brain diseases comprising induced pluripotent stem cell-derived mitochondria. According to one aspect, the mitochondria derived from induced pluripotent stem cells were confirmed to exhibit excellent effects in removing reactive oxygen species and in reducing expression of inflammatory cytokines, thereby providing anti-inflammatory effects superior to those of mitochondria derived from other cells. When the mitochondria are treated to damaged nerve cells, recovery and regeneration effects can be confirmed on nerve cells, and as a result of administering the mitochondria to elderly mice, it was also confirmed that the mitochondria have an anti-aging effect. Accordingly, the pharmaceutical composition comprising induced pluripotent stem cell-derived mitochondria according to one aspect of the present invention can be applied in various fields for the prevention or treatment of degenerative brain diseases, particularly bone diseases caused by aging or beta amyloid aggregation.