527 results about "Brain disease" patented technology

The present invention provides methods for identifying various biological states. Methods for diagnosis of diseases, in particular cardiovascular and brain diseases, are provided herein. One aspect of the invention is the analysis of lipoprotein complexes with summary survey scan mass spectrum for the analysis of biological states. Another aspect of the invention is the use of matrix assisted laser desorption ionization (MALDI) mass spectrometer to analysis lipoprotein complexes for the diagnosis of cardiovascular and brain diseases. Yet another aspect of the invention is a method of diagnosis of brain diseases by evaluating the characteristics of lipoprotein complexes.

A method and system for automatic diagnosis of possible brain disease is based on at least one brain scan image of a patient containing at least one feature of interest and a corresponding result of a medical profile analysis of the patient. A database containing parameters associated with at least one feature of a plurality of brain scan images each compiled from respective patient data and inserted into the database is searched so as to extract from the database a set of respective parameters each associated with the at least one feature and wherein in respect of each feature at least one of the corresponding parameters is indicative of a brain disease profile. The set of respective parameters is analyzed to determine a statistically significant brain disease profile which fits the patient based on the at least one feature of interest of the patient's brain scan image.

The invention relates to the technical field of image processing, and proposes a brain disease classification system based on a self-attention mechanism, aiming at solving the technical problems of low classification accuracy caused by the complicated process of preprocessing, feature extraction and feature selection of magnetic resonance images required in the classification and diagnosis of brain diseases. For this purpose, the brain disease classification system based on the self-attention mechanism in the invention comprises the following steps: pre-processing the acquired human brain magnetic resonance images of the brain disease patients to obtain the gray matter density map of the human brain; using a pre-constructed brain disease classification model to classify the gray matter density map to obtain a brain disease category of the brain disease patient, wherein The pre-constructed brain disease classification model is a three-dimensional convolutional neural network model basedon the self-attention mechanism. The system shown in the embodiment of the invention can quickly and accurately classify the categories of brain diseases.

This invention involves use of temporal or spatio/spector-temporal data (SSTD) for early classification of outputs that are results of spatio-temporal patterns of data. Classification models are based on spiking neural networks (SNN) suitable to learn and classify SSTD. The invention may predict early events in many applications, i.e. engineering, bioinformatics, neuroinformatics, predicting response to treatment of neurological and brain disease, ecology, environment, medicine, and economics, among others. The invention involves a method and system for personalized modelling of SSTD and early prediction of events based on evolving spiking neural network reservoir architecture (eSNNr). The system includes a spike-time encoding module to encode continuous value input information into spike trains, a recurrent 3D SNNr and an eSSN as an output classification module.

The invention brings forward an epileptic feature extraction and automatic identification method based on an electroencephalogram signal. The method comprises following steps: firstly, carrying out wavelet transformation to the electroencephalogram signal to obtain a time frequency image and segmenting the time frequency image into time frequency sub-images respectively having five frequencies including delta, theta, alpha, beta and gamma in the sequence from low to high frequencies; secondary, applying a Gaussian mixture model to estimate the probability distribution of the energy density of the time frequency image and utilizing parameters (mean value, variance, weight number) corresponding to the Gaussian mixture model as features of the electroencephalogram signal; thirdly, applying a feature weighting relief F and a support vector machine-recursive feature elimination to select above features in order to obtain the feature representing the difference between a normal electroencephalogram signal and an epileptic electroencephalogram signal to the greatest extent; lastly, verifying effectiveness for automatic identification of epilepsy represented by the method of the invention in the modes of pattern classification and machine learning, concretely speaking, accuracy of identification and generalization performance of the model. Compared with a conventional method, the epileptic feature extraction and automatic identification method based on the electroencephalogram signal has following beneficial effects: features obtained by extraction and identification have the high accuracy for identification of epileptic electroencephalogram; fine generalization performance of model is obtained; and important significance to auxiliary respects such as clinical diagnosis and automatic identification epileptic brain diseases is gained.

The present invention is concerned with delivering a pharmaceutical composition to the brain of a mammalian subject for treating brain diseases or disorders. The process includes the steps of: (i) providing a dispersion of the pharmaceutical composition as particles having an average particle size of from about 150 nm to about 100 microns, and (ii) administering the dispersion to the mammalian subject for delivery to the brain of a portion of the pharmaceutical composition by cells capable of reaching the brain. The dispersion of the pharmaceutical composition as particles, for example, can be phagocytised or adsorbed by the cells prior or subsequent to administration into the mammalian subject. The dispersion of the pharmaceutical composition can be administered to the central nervous system or the vascular system. After administration, the loaded cells transport the pharmaceutical composition as particles into the brain.

The present invention provides an electrode designed for implantation into the central nervous system (CNS) of a mammal, wherein said electrode is substantially coated with interleukin-1 receptor antagonist (IL-1ra) or a coating composition comprising it, and the IL-1ra actively inhibits scarring on or around the surface of the electrode when implanted into the CNS. The electrode of the invention may be used for brain recording and/or stimulation, and can thus be used for treatment of a brain dysfunction, a brain disease or disorder, or a brain injury, as well as for brain computer interface, brain machine interface, or electrotherapy.

The invention relates to effective components of root of red-rooted salvia to treat disease and its chemical structure and synthesize technique. It is used to treat heart and blood vessel of brain diseases. Filter the main metabolite-beta(-3,4- dihydric phenyl)-alpha hydric propionic acid isopropyl ester in human blood serum after taking medicine. The purity is 98.0%. The results demonstrate the medicine to have varies function in blood vessel dilatation, anti cerebral ischemia, oxidation resisting in vitro, and protect heart and brain and remission the diabetes caused miocardial infarction.

2-arylbenzothiophene derivatives or pharmaceutically acceptable salts thereof, a preparation method thereof, and a pharmaceutical composition for the diagnosis or treatment of degenerative brain disease containing the same as an active ingredient. Since the 2-arylbenzothiophene derivatives of Formula 1 have a relatively high binding affinity for β-amyloid, they can be used as diagnostic reagents for diagnosing Alzheimer's disease at an early stage by non-invasive techniques when they are labeled with radioisotopes:

wherein R¹-R⁴, V, W, X, Y and Z are as defined in the Detailed Descript of the specification. Further, when the pharmaceutical composition containing the 2-arylbenzothiophene derivative binds with a low-molecular weight β-amyloid peptide binding compound, generation of malignant high-molecular weight β-amyloid deposits is minimized. Accordingly, the pharmaceutical composition can be used as a therapeutic agent of degenerative brain disease such as Alzheimer's disease.

The invention belongs to the field of medicine, and relates to D-configuration polypeptide with high stability and capable of realizing mediated targeting of acetylcholine receptor high-expression cells and crossing corresponding barrier membranes and a nano drug delivery system thereof as well as an application in in-vivo and in-vitro brain targeting and in treatment of brain diseases and the like. Test results indicate that DCDX and the acetylcholine receptor are combined with IC50 to obtain 84.5nM which is stable in serum and tolerates hydrolysis of protease; the model drug carried by DCDX is specifically taken in by the positive cells expressing the acetylcholine receptor and has an ability of crossing the barrier formed by the kind of cells; and the nano drug delivery system made of a DCDX-modified polymer carrier material can deliver the entrapped model drug to the target tissue while the drug effect is remarkably improved. The D-configuration polypeptide DCDX provided by the invention can mediate active targeting of the drug or nano drug delivery system and has a good application prospect in the diagnosis and treatment of multiple diseases.

The invention discloses a magnetic resonance imaging (MRI) based brain disease individual prediction method and a magnetic resonance imaging (MRI) based brain disease individual prediction system. The method comprises the following steps: 1: obtaining the MRI of the brain of a patient with mental diseases; 2: carrying out denoising and dimension reduction treatment on the MRI of the brain of the patient; 3: carrying out feature selection by utilizing a ReliefF algorithm; 4: adaptively obtaining a spatial brain area by using a spatial cluster analysis method; 5: removing redundant features by utilizing a correlation-based feature selection algorithm, thus obtaining an optimal feature subset; 6: carrying out multiple linear regression analysis based on the optimal feature subset to recognize potential biomarkers. The method has the beneficial effects that the embodiment of the invention integrates various machine learning methods and can rapidly and conveniently achieve quantitative and individual accurate prediction of the interest features of mental diseases, such as clinical indexes, based on various image data in different mode types, thus being beneficial to understanding the brain structures, function abnormity and potential pathogenesis of the diseases.

The invention belongs to the technical field of protein polypeptides, relates to a polypeptide with a brain targeted medicine delivery characteristic, and provides a molecular structure of the polypeptide, and application of the polypeptide to brain targeted medicine delivery. A phage monoclonal antibody which has the function of penetrating a blood brain barrier and can be enriched in a brain is obtained through repeated in-vivo Sprague Dawley (SD) outbred stock rat screening by a phage display technology; deoxyribonucleic acid (DNA) sequencing proves that the phase display polypeptides comprise a consensus sequence; meanwhile, an immunohistochemical method and in-vivo recovery rate comparison prove that the obtained polypeptide has the brain targeted medicine delivery characteristic. The polypeptide and derivative peptides thereof can be used for modifying medicine-carrying nanoparticles, liposomes, vesicles or micelles to construct a brain targeted medicine delivery system; and the delivery of medicines in the brain can be improved, the treatment effect on brain diseases is enhanced and systemic toxic and side effects are reduced.

A neurotrophic factor production promoting device is provided, which is able to promote production of a neurotrophic factor or neurotrophic factor-like substance in an affected area by a simple technique that, regardless of the place of treatment, can be performed without transplantation of cells or injection into the affected area, in order to prevent or treat various diseases such as brain diseases. In order to apply a high frequency alternating magnetic field in the range of 20 MHz to 180 MHz, 280 MHz to 600 MHz, or 700 MHz to 1000 MHz to cells at a magnetic flux density of no more than 0.01 Tesla, the neurotrophic factor production promoting device includes a high frequency electromagnetic wave generating means generating a high frequency electromagnetic wave of the abovementioned frequency, in which the magnetic stimulation by the high frequency alternating electromagnetic field of the abovementioned high frequency allows the intracellular concentration of calcium ions to be increased so that exocytosis of the neurotrophic factor group is induced, and the magnetic stimulation allows messenger ribonucleic acid (mRNA) of the neurotrophic factor group to be increased in the cells so that the synthesis and extracellular release of the neurotrophic factor group are promoted.

An image generating methodology for displaying brain activation areas with improved accuracy in a living body light measurement system for generating an image of the changes in blood volume of the brain. A spatial intensity distribution of changes in blood volume can be generated by detecting brain activation in many sampling points, and a spatial interpolation process is executed on this data. A threshold process is executed on the distribution to extract the distribution of coordinates having a signal intensity of at least the predetermined threshold. The displacement between the brain activation area and the maximal location of the distribution is stored in a recording unit of the system in order to compensate for the distribution extracted by the threshold process. Accordingly, brain activation areas can be estimated with greater location accuracy, and the diagnosis and medial treatment for brain diseases can likewise be executed with greater accuracy.

The invention discloses a healthcare therapeutic apparatus of cardiovascular diseases, which is composed of a shell, a keypad arranged on the shell, a display, a cuff connected with the shell, an inflator pump arranged in the shell, a pressure sensor, an electromagnetic valve and a control circuit. The pressure sensor is connected with the cuff and used for detecting gas pressure and pressure oscillatory wave in the cuff. The detection signal output end of the pressure sensor is connected with the signal input end of a control circuit. The control circuit executes closed-loop control on the gas in the cuff via the inflator pump, the pressure sensor and the electromagnetic valve. The healthcare therapeutic apparatus can effectively stop the limb blooding of a user at intervals by repeatedly pressing hard upon the limb of the user, thus inspiring the anti-ischemia /ischemia or oxygen deficiency resistant potential of organism tissue cells, improving the viability and operational capacity of the organism, an organ or tissues under the condition of the oxygen deficiency, having a remarkable effect on preventing cardio-cerebrovascular diseases and treating ischemic mind-brain diseases, and reducing the incidence of sudden death and stroke. Therefore, the healthcare therapeutic apparatus of the cardiovascular diseases is suitable for a family and an individual.

The invention relates to a brain disease diagnosis method based on a 3D convolutional neural network. The method comprises the following steps: 1) acquiring MRI brain image data samples of normal states and diseases; 2) performing sample pretreatment: brain tissue extraction and sample standardization; 3) designing a 3D convolutional neural network for brain disease diagnosis; 4) taking the MRI brain image as the input of the 3D convolutional neural network, performing network training, extracting features, and establishing a classification diagnosis model; 5) preprocessing the MRI brain imageof the to-be-detected person, sending the preprocessed MRI brain image as input to the 3D convolutional neural network diagnosis model to obtain an output label, and judging whether the to-be-detected person is sick or not. The method has the advantages that 1) the brain disease diagnosis model is established by using the 3D convolutional neural network, and features are automatically learned from MRI brain images; a multi-hidden-layer deep learning model is constructed, accurate and effective features are automatically obtained by a computer, and finally the precision and generalization ability of the diagnosis model are improved; 2) the method is suitable for diagnosis of various different types of brain diseases such as Alzheimer's disease, depression, children hyperactivity and the like.

Disclosed are compounds which bind VLA-4. Certain of these compounds also inhibit leukocyte adhesion and, in particular, leukocyte adhesion mediated by VLA-4. Such compounds are useful in the treatment of inflammatory diseases in a mammalian patient, e.g., human, such as asthma, Alzheimer's disease, atherosclerosis, AIDS dementia, diabetes, inflammatory bowel disease, rheumatoid arthritis, tissue transplantation, tumor metastasis and myocardial ischemia. The compounds can also be administered for the treatment of inflammatory brain diseases such as multiple sclerosis.

An object of the present invention is to perform a more accurate and detailed diagnosis of a brain disease.

A diagnosis server 10 of the brain disease diagnosis system 1 for diagnosing a brain disease of an examined person includes: an acquiring unit 11 for acquiring a brain image of the examined person so as to obtain an acquired image; a region setting unit 12 for setting a plurality of regions in the acquired image; an individual index value calculating unit 13 for calculating an individual index value based on a pixel value of the acquired image, in each of the plurality of regions; a whole index value calculating unit 14 for calculating a whole index value by weighting the individual index value of each of the plurality of regions; a diagnosis unit 15 for diagnosing the brain disease of the examined person based on the whole index value; and an output unit 16 for outputting information indicating a diagnosis outcome.

A system and method for assessment of a brain status of a subject are disclosed. The brain status comprises a brain disease induced brain dysfunction. An occurrence and/or stage of the brain disease induced brain dysfunction in the subject is determined. The system comprises an apparatus that is adapted to determine the occurrence and/or stage of the brain disease induced brain dysfunction in the subject from random speech of the subject. The apparatus (200) comprises units that are operatively connected to each other, which comprises a unit (205) for registering the speech of the subject over a period of time; a unit (206) devised for analyzing the registered speech and configured to determine a pause component of the speech; and a unit that is adapted to determine the occurrence and/or stage of the brain disease induced brain dysfunction from the pause component, wherein said pause component is an accumulated pause time of a total time of said speech correlated to said occurrence and/or stage of said brain dysfunction.

The present invention relates to the fields of brain function imaging and the construction of a brain network and specifically relates to a fusion brain network construction method based on structuralconnection and functional connection. The purpose of the present invention is to provide a brain network construction method with the fusion of two types of connections with the combination of the advantages of existing structural brain network and resting state function brain network in disease research, and the method is the fusion brain network construction method based on the structural connection and the functional connection. According to the method, a structural connection matrix and a resting state functional connection matrix are fused. According to the fusion brain network construction method, the difference of network attribute indexes of a brain network of a patient with brain diseases and a brain of normal people can be more effectively found, and the research of various brain diseases is helped.

The invention belongs to the field of pharmaceuticals and relates to a cascade brain-targeting drug delivery system and the application thereof. The cascade brain-targeting drug delivery system comprises a first-stage target functional molecule, a second-stage target functional molecule and a drug carrier. The cascade target drug delivery system can identify the blood-brain barrier through the first-stage target functional molecule and identify a brain lesion through the second-stage target functional molecule, so as to achieve the accurate targeting purpose, and can also accurately transfer imaging molecules or drugs to the brain lesion, so as to achieve good imaging and therapeutic effects. The cascade brain-targeting drug delivery system can be applied to prepare formulations for treatment or diagnosis of brain diseases (such as brain tumors) and nervous system diseases.

A system for processing a medical image for personalized brain disease diagnosis and status determination, includes: an image processing unit, which obtains a 3D T1 weighted image, a 2D T2 fluid attenuated inversion recovery (FLAIR) image, a magnetic resonance angiogram (MRA) image, which images only a vessel for checking abnormality of a brain vessel, and a 4D phase-contrast flow image for recognizing a state of a blood flow in a vessel; a complex image analyzing unit, which selects a disease-to-be-diagnosed, sets a brain area according to the selected disease, and analyzes brain tissue and a brain vessel; and a personalized diagnosis and result output unit, which outputs a brain state, a disease-specific risk degree, a risk of disease, and a disease prediction result through a machine learning algorithm by utilizing an age-specific data DB.