204 results about "Mental disease" patented technology

The invention provides a spatial-temporal feature fusion-based dynamic graph convolution electroencephalogram depression detection method, which comprises the following steps of: firstly, segmenting a sample into fragments with the length of 1 second, and calculating power spectral density (PSD) as an input feature by adopting a Welch method; the time sequence and spatial characteristics of the EEG signals are synchronously extracted through a double-branch architecture, wherein one branch captures the long-term time sequence dependence of the EEG signals by using a GRU; and the other branch adopts an improved TSCN (separable convolution is introduced), multi-scale spatial features from fine to rough are extracted through causal convolution and expansion convolution of residual layered stacking, after double-branch features are adaptively fused based on an attention mechanism, a dynamic graph structure is constructed, functional connection evolution of brain intervals is modeled by using a graph convolution network, and a dynamic graph structure is constructed. The topological structure of the network is optimized through a back propagation process, and finally depression identification is realized through a Softmax classifier. According to the method, the time sequence modeling capability of the GRU and the multi-scale spatial analysis capability of the TSCN are fused, the representation limitation of a single model is broken through, the dynamic change of a brain function network is adaptively captured through dynamic graph convolution, the physiological interpretability is enhanced, deep complementary fusion of EEG spatial and temporal characteristics is realized, the depression recognition accuracy is remarkably improved, and the method is suitable for popularization and application. And an efficient tool is provided for auxiliary diagnosis of mental diseases.

The invention discloses a method for testing and evaluating safety effectiveness of a digital therapy product for mental diseases, which relates to the technical field of mental diseases and comprises the following steps: S1, evaluating objective indexes of the digital therapy product; s2, testing a man-machine interaction feedback mode; s3, mapping measurement of product output content and patient feedback; and S4, performing real-time adjustment and optimization. According to the method for testing and evaluating the safety effectiveness of the mental disease digital therapy product, aiming at the mental disease digital therapy product, a special testing and evaluating system is constructed, objective indexes and output contents of the digital therapy product are tested, and in the interaction process of a patient and the product, the safety effectiveness of the product is tested. By detecting data such as micro-expression, electroencephalogram signals and eye movement signals of a user in real time, feedback of a patient is obtained in real time, corresponding relation analysis is conducted on the feedback and output content of a digital therapy product, therefore, the treatment effect of the product can be accurately evaluated, and the accuracy of mental disease diagnosis and treatment is improved.

Compositions comprising microalgal extracellular vesicles (MEVs) formulated for intranasal delivery are provided, whereby upon intranasal administration, the MEVs are transported by a specific route, to specific regions in the brain via the olfactory nerve after intranasal administration, and to interconnected brain regions through the lateral olfactory tract (LOT). The MEVs are transported via neuronal axonal transport. The MEVs have the ability to cross synapses including: (i) synapses between olfactory sensory neurons (OSNs) and mitral / tufted neurons; (ii) synapses between mitral / tufted neurons and local neurons in multiple brain regions colonized by the lateral olfactory tract (LOT); and (iii) synapses between neurons in brain regions colonized by the LOT and neurons from the frontal cortex, hippocampus, thalamus, and hypothalamus. The compositions contain extracellular vesicles (MEVs) from microalgae loaded with bioactive cargo for the treatment, detection, diagnosis, or monitoring of diseases, disorders, or conditions of or involving the brain, particularly neuronal delivery of the cargo. The compositions and methods have multiple applications as therapeutic and diagnostic agents for the treatment, diagnosis, and monitoring of diseases, disorders, or conditions of or involving the brain. The compositions can be used in methods and uses for the treatment of cancers involving the brain, and can be used, for example, to deliver therapeutic agents for psychiatric diseases, disorders, conditions, and to deliver therapeutic agents for neurodegenerative diseases, disorders, and conditions.

The invention discloses a disease risk assessment method and screening device based on a multi-group student physical collaborative digital network, and relates to the field of intelligent medical detection. In order to solve the defect that multi-omics-level system collaborative analysis and robust risk assessment are difficult to realize in the prior art, the technical scheme provided by the invention is as follows: acquiring a plasma sample, acquiring a spectral signal by adopting an attenuated total reflection Fourier transform infrared spectrum, and establishing a plasma spectrum digital information space; the method comprises the following steps: constructing a biological collaborative digital network containing four nodes of protein, lipid, saccharides and nucleic acid based on pathophysiology priori knowledge, and defining node strength, edge weight and network collaborative efficiency; a health baseline configuration file is established by using a health sample, a standardized deviation score of a to-be-tested sample is calculated, a comprehensive risk score is obtained, a disease screening result is output in combination with a machine learning model, and digital evaluation of multi-omics collaborative characteristics is realized. The method is suitable for non-invasive rapid screening and risk assessment work of neurodegenerative diseases and mental diseases.

The invention discloses a closed-loop transcranial magnetic stimulation brain regulation and control method based on multi-model fusion and individualized learning. Dynamic optimization and accurate regulation and control of TMS stimulation parameters are achieved. According to the technical scheme, the method comprises the following steps: collecting and preprocessing a multi-guide EEG signal in real time, and extracting frequency domain and time domain features and brain network topology indexes; the EEG state in the future 0.5-2 seconds is predicted by using depth sequential networks such as LSTM and the like, and an individualized stimulation parameter strategy is generated in combination with reinforcement learning and Bayesian optimization; tMS stimulation is triggered based on a prediction result, brain network indexes and reward evaluation, and long-term adaptive adjustment is realized through an individual memory pool. The traditional passive feedback mode is broken through, an active closed-loop mechanism of prediction-learning-regulation is realized, the TMS regulation efficiency and the individual adaptability are remarkably improved, and the method can be widely applied to scenes such as mental disease intervention, cognitive regulation and nerve rehabilitation.

The embodiment of the invention discloses a diagnosis and treatment sample construction method and device and an auxiliary medical large model training method and device. The construction method of the diagnosis and treatment sample comprises the following steps: firstly, determining a plurality of candidate symptom combinations by utilizing a knowledge graph constructed based on mental disease data; next, for any first candidate symptom combination, reasoning the symptom combination by using a sub-graph corresponding to the diagnosis rule in the knowledge graph to obtain a diagnosis class label; then, reasoning the first candidate symptom combination and the diagnosis class label by using a sub-graph corresponding to a treatment guide in the knowledge graph to obtain a treatment suggestion label; and then, constructing the first candidate symptom combination, the diagnosis class tag and the treatment suggestion tag into a first candidate sample for determining a diagnosis and treatment sample set. Furthermore, based on a high-quality diagnosis and treatment sample set constructed at low cost, training of the auxiliary medical large model can be carried out, so that the trained auxiliary medical large model has good generalization.

The invention provides a method and a system for predicting a cell type-specific non-coding variation function, and belongs to the technical field of bioinformatics. Comprising the steps that a DINOSNN prediction model is constructed, model training is carried out, and the model is composed of a convolution and attention mixed neural network model and a non-coding variation prediction model; obtaining all non-coding variations corresponding to each brain mental disease, inputting the non-coding variations into a DINOSNN prediction model, predicting the probability that each non-coding variation is a functional non-coding variation through a trained gradient boosting tree model, and predicting a cell type set influenced by the variation, further establishing a corresponding relationship among the brain and mental diseases, the non-coding variation and the cell type set influenced by the non-coding variation; and selecting a cell type set corresponding to the non-coding variation with the highest probability of functional non-coding variation from the non-coding variations corresponding to the brain and mental disease to be analyzed as a targeted cell set corresponding to the brain and mental disease to be analyzed.

The invention provides a compound as shown in a formula I which can be used as a sodium channel regulator, and a stereoisomer, a tautomer or a pharmaceutically acceptable salt thereof. The invention also provides a pharmaceutical composition containing the compound and the stereoisomer, the tautomer or the pharmaceutically acceptable salt thereof and a carrier or excipient, and a pharmaceutical application of the pharmaceutical composition as a NaV1.8 inhibitor (such as pain, respiratory diseases, neurological disorders, mental diseases and the like).

The invention belongs to the technical field of mental disease auxiliary diagnosis, and particularly relates to a mental disease I auxiliary diagnosis method based on a Transform sub-network asynchronous model. Based on a sub-network embedded Transform asynchronous model, taking fMRI data as input, and taking an evaluation result as output; the Transform asynchronous model based on sub-network embedding comprises a time sequence partitioning device, an SNP-n sub-network partitioning module, a self-attention module and an asynchronism capturing module, and is obtained by performing end-to-end supervised learning on the basis of a PyTorch framework in a three-level core architecture of time sequence partitioning, sub-network partitioning and asynchronous capturing. And finally, a result obtained through the sub-network embedded Transform asynchronous model is classified and evaluated through a classification module. The auxiliary diagnosis accuracy is remarkably improved through the method, and the method comprehensively exceeds static and traditional dynamic methods.

The invention belongs to the technical field of biomedical imaging, and particularly discloses an in-vivo imaging method of an SAPAP3 gene defect disease model, which is characterized in that an effective dose of an S1PR1 receptor targeted radioactive probe is injected into the body of the SAPAP3 gene defect disease model. According to the method, an S1PR1 receptor targeting radioactive probe is utilized, the technical bottleneck that in-vivo and dynamic observation of the S1PR1 receptor cannot be achieved through an existing in-vitro technology is solved, and an indispensable visual tool and a quantitative evaluation means are provided for studying the neurobiological mechanism of related mental diseases such as obsessive-compulsive disorder and accelerating research and development of related drugs.

One or more embodiments described herein relate to predicting, using adaptive artificial intelligence techniques, typical and aberrant physiological reactions of a patient to psychiatric counseling. Treatment plans can be determined and calculated based on previously-gathered demographic and / or biometric data, and / or modifications to treatment plans can be determined and / or implemented based on emergent recognition of reaction types, such as reclassifying reactions that would previously have been deemed typical as aberrant (or vice versa).

The present invention relates to the technical field of medical examination, and provides a mental illness screening system, a chip, an apparatus, and a computer-readable storage medium. The system for screening for mental illness comprises the following modules: an image acquisition module, an ROI detection module, an ROI extraction module, a cropping module, a standardization module, an erythema segmentation module, a statistical feature extraction module, and a report generation module. By using the system of the present invention, it is possible to detect or diagnose whether an individual is suffering from depression. The method for using the system for screening for mental illness of the present invention is simple and highly accurate, and can quickly and conveniently complete a diagnosis procedure.

Provided in the present invention are a compound as represented by formula I which can be used as a sodium channel modulator, and a stereoisomer, tautomer, or pharmaceutically acceptable salt thereof. Further provided are a pharmaceutical composition containing the compound and the stereoisomer, tautomer, or pharmaceutically acceptable salt thereof, and a carrier or an excipient, and the pharmaceutical use thereof as an NaV1.8 inhibitor (against, for example, pain, respiratory diseases, neurological disorders, and mental diseases).

The patent document discloses a dosage form featuring a unique combination of extended-release and immediate-release components of the active ingredient. This dosage form allows for high drug loading, enabling once-daily administration while providing a sustained window of effective treatment with reduced fluctuations in drug concentration. The document also discloses a method for treating neurological or psychiatric diseases or conditions in a subject.

A cerebral cortex model-based simplicial network for diagnosing mental disorder is disclosed. A cerebral cortex model-based method for diagnosing mental disorder performed by a mental disorder diagnosis system, according to one embodiment, may comprise the steps of: modeling a cerebral cortex mesh as a cerebral cortex simplicial complex; and performing message passing for diagnosing a mental disorder using relationship information of the modeled cerebral cortex simplicial complex through a message passing neural network.

The present invention relates to a system and method for recommending health functional foods to elderly patients with geriatric chronic diseases. According to the present invention, a method for recommending health functional foods, performed by a system for recommending health functional foods, comprises the steps of: receiving, from a target patient with a geriatric chronic disease, response data obtained from responses to a medical inquiry form or questionnaire; performing natural language processing on the response data; inputting the natural-language-processed response data into a first learning model to classify a mental disorder and a severity level of the target patient; receiving personal information and EMR data of the target patient; and inputting the personal information, EMR data , and information on the mental disorder and severity level of the target patient into a second learning model to recommend one or more health functional foods to the target patient.

The utility model discloses an acupuncture kit for psychiatry. The device comprises an acupuncture box, acupuncture needles and a transparent cover, the acupuncture needle comprises a colored sheet, a needle handle and a needle body; the needle handle is arranged at the upper end of the needle body; the colored sheet is arranged on the upper part of the needle handle; the upper portion of the acupuncture box is a card containing area used for containing acupuncture needle total number cards, patient information cards and acupuncture needle number cards, the front end of the lower portion of the acupuncture box is open, and the interior of the acupuncture box is divided into four acupuncture needle storage areas through partition plates and used for storing acupuncture needles of different colors. And the transparent cover covers the opening at the front end of the acupuncture box. The beneficial effects of the utility model are that: can avoid the potential safety hazard that the acupuncture needle remains or is lost when acupuncture therapy is carried out on the mental disease patient to the greatest extent. And the color of the acupuncture needle is distinguished, so that the checking range can be narrowed. By recording the number of acupuncture needles at different parts and the total number, checking before and after acupuncture treatment is facilitated. The acupuncture needles with different colors correspond to the storage positions, so that the number of the acupuncture needles can be checked conveniently.

The invention provides a mental disease classification method based on a two-stage multi-atlas neural network, and belongs to the technical field of medical information intelligent diagnosis. The technical problems that early-stage features are excessively mixed due to multi-atlas information interaction in the prior art, unique disease-related specific characterization of each atlas can be diluted, and potential noise in cross-atlas connection can be possibly amplified are solved. The method comprises the following steps: firstly, on the basis of fMRI data, constructing a functional connection matrix by using various brain maps, and constructing cross-map edges through spatial proximity to obtain a joint map; then, adopting a two-stage graph neural network alternate propagation mechanism: only starting a graph inner edge to extract stable features in an odd number layer, starting a cross-graph edge in an even number layer, and realizing multi-graph information fusion in combination with an action mechanism; and finally, realizing mental disease prediction through graph-level pooling and a classifier. According to the method, premature aliasing of map features can be effectively avoided, and the rationality and robustness of cross-map information interaction are enhanced.

The present invention relates to a pyridino aryl or heteroaryl derivative having a Kappa receptor antagonistic activity. In particular, the present invention relates to a compound represented by general formula (IV) or general formula (VII), a stereoisomer thereof, a pharmaceutically acceptable salt thereof, and a pharmaceutically acceptable salt of the stereoisomer. The invention also relates to a pharmaceutical composition containing the compound, a preparation method of the pharmaceutical composition, and a treatment method and application of diseases related to the antagonistic activity of the Kappa receptor, such as depression, depression with pleasant sensation deficiency, bipolar affective disorder, anxiety and other mental diseases. Wherein each substituent in the general formula (IV) or the general formula (VII) is as defined in the specification.

A plurality of circular RNAs (circRNAs) the expression of which is correlated with brain disorders. The circRNAs are useful for compositions, kits, assays, and methods for the identification, diagnosis, screening, treatment and / or monitoring of brain disorders including psychiatric disorders such as bipolar disorder (BD), schizophrenia (SCZ), depression, Attention-Deficit / Hyperactivity Disorder (ADHD), Obsessive-compulsive disorder (OCD), Anxiety Disorders, etc., and neurodevelopmental disorders such as Autism, Asperger's Syndrome, and other Autism Spectrum Disorders (ASD), pervasive developmental disorders not otherwise specified (PDD-NOS), etc.

Provided in the present invention are a compound as represented by formula I which can be used as a sodium channel modulator, and a stereoisomer, tautomer, or pharmaceutically acceptable salt thereof. Further provided are a pharmaceutical composition containing the compound and the stereoisomer, tautomer, or pharmaceutically acceptable salt thereof, and a carrier or an excipient, and the pharmaceutical use thereof as an NaV1.8 inhibitor (against, for example, pain, respiratory diseases, neurological disorders, and mental diseases).

The utility model discloses a medicine feeder for a mental patient, which relates to the technical field of medicine feeding equipment and comprises a medicine conveying cylinder, a connecting seat is arranged at the upper end of the medicine conveying cylinder, a medicine conveying device for feeding medicine is arranged at the upper end of the connecting seat, the medicine conveying cylinder is slidably connected with a push rod for conveying medicine, and the lower end of the push rod is fixedly connected with a push block. An auxiliary device used for assisting medicine feeding is slidably connected to the surface of the medicine conveying cylinder, a first rotating seat is driven to move through vertical movement of a pushing ring, the first rotating seat drives a first rotating shaft to move in the vertical direction, the first rotating shaft drives a transmission rod to move, and the transmission rod slides in a sleeve in the moving process. Rotation of a second rotating shaft and a second rotating seat provides conditions for movement of a transmission rod, movement of the transmission rod drives a rubber tube to move, supporting conditions are provided during medicine feeding, and the situation of choking medicine due to the fact that a medicine conveying device is too deep during medicine feeding is prevented.

The invention provides a method and device for generating information based on brain image data, and the method comprises the steps: carrying out the longitudinal brain image analysis of the brain image data, collected at multiple time points, of a target individual, carrying out regional division on the brain of the target individual at each time point and extracting morphological measurement values of each region based on a longitudinal brain image analysis result and a cortex partition map; based on the brain form norm, the deviation degree of the morphological measurement value of each region of the brain of the target individual at each time point relative to the brain form norm is determined, and the individual brain form deviation vector corresponding to the target individual at each time point is obtained; performing correlation calculation on the individual brain form deviation vector corresponding to the target individual at each time point and the statistical data of the plurality of mental diseases in the whole brain range to obtain cross-disease brain form similarity features; and inputting the cross-disease brain form similarity features into a pre-trained machine learning model, and outputting classification information about whether the target individual is a schizophrenia patient or not, and / or prediction information about clinical indexes.

The invention provides an image processing method and an image processing device, and relates to the field of computers. According to the method, feature extraction is carried out on multi-modal image data of functional magnetic resonance image data and structural magnetic resonance image data of a brain, feature fusion is carried out, classification is carried out based on fused features, an image recognition result is obtained, feature extraction is carried out based on a channel attention and space attention decoupling method, and the recognition accuracy is improved. According to the method, the image features can be extracted more accurately, the accuracy of the image recognition result is further improved, and diagnosis of mental diseases such as bidirectional affective disorder and Alzheimer's disease can be better assisted.

Disclosed herein are salts and solid forms of MDMA, (R)-MDMA, (S)-MDMA, MDE, S-MDE, R-MDE, MDAI, MBDB, S-MBDB, R-MBDB, MEAI, and 5,6-Dimethoxy-2-aminoindane, including salts, solid forms of the compound and salts thereof, as well as polymorphs of solid forms. The solid forms disclosed herein may have improved properties, such as improved physical, chemical, and / or pharmacokinetic properties. Also disclosed are methods for making the salts and solid forms and methods for administering the same. The disclosed salt and solid forms of MDMA, (R)-MDMA, (S)-MDMA, MDE, S-MDE, R-MDE, MDAI, MBDB, S-MBDB, R-MBDB, MEAI, and 5,6-Dimethoxy-2-aminoindane may be useful for treating neurological disease and / or a psychiatric disorder in a subject.

The utility model relates to the technical field of medicine feeders, in particular to a portable medicine feeder for mental patients, which comprises a tube body, a push plate is arranged on the inner side wall of the tube body, one end of the push plate is fixedly connected with a storage frame, and a storage rod is slidably connected into the storage frame. According to the portable medicine feeder for the mental patient, through the arrangement of the storage frame, the storage rod, a limiting sliding block and a limiting clamping block, when the portable medicine feeder is used, a user can manually push the storage rod, the storage rod moves to drive the limiting sliding block to slide in the storage frame, and after the storage rod is pushed to a certain position, the limiting clamping block can be clamped into a groove hole in the storage frame; the storage rod is fixed in the storage frame, so that the overall length of the device is adjusted, the effect of carrying the device is achieved, and the situation that when a user carries the device into a bag, articles in the bag are prone to squeezing the device, liquid medicine is prone to seeping, and subsequent normal medicine feeding is affected is avoided.

The application discloses a structural brain injury disease intelligent classification and target recommendation system. The system comprises: an individualized brain function network construction module: according to individual resting state function images and structure images, a function connection matrix is obtained and brain function connection is represented in the form of a graph to construct an individualized brain function network; a disease judgment classification model training module: a disease judgment classification model is constructed, and the disease judgment classification model is trained by combining a network topology and a graph neural network; a key brain area identification module: the SHAP value of the K nodes with the highest SHAP value is selected by applying SHAP on the trained disease judgment classification model, and the K nodes are used as key brain areas for classification; and a personalized intervention target recommendation module: the key brain areas are registered to a standard brain space template to match recommended treatment targets. The application can more comprehensively and efficiently identify and recommend treatment targets, and provides more accurate data support for intelligent classification judgment and treatment of mental diseases.

To provide modification of the gut microbiome to treat psychiatric disorders or diseases of the central nervous system.SOLUTION: The present disclosure relates to a method of treating at least one symptom of a psychiatric disorder or disease of the central nervous system in a subject by modulating the amount of GABA produced in the gut of the subject. The present disclosure also relates to a method of culturing novel bacterial strains. A method of identifying bacterial strains capable of producing GABA and engineering strains to produce GABA are also disclosed. The present disclosure also describes a method of identifying bacterial strains that can produce, for example, GABA under physiologically appropriate conditions, such as, physiologically appropriate pH.SELECTED DRAWING: None

The invention discloses a mental disease typing method and system integrating DNA methylation and neuroimaging, and the method is a neuroimaging biological annotation method integrating DNA methylation and brain connection group data, and comprises the steps: recognizing brain network features related to a specific molecular phenotype in a whole brain range through a machine learning model; and mechanism-sensitive layering of the mental disorder heterogeneity group is realized. The method does not need to depend on a prior classification or hypothesis mechanism, can be suitable for different types of mental disorder people, provides technical support for exploring potential biological mechanisms and identifying targeted therapy groups, and has high generalizability and clinical application prospects.

The present invention relates to a treatment agent for various diseases of the nervous system and mental illnesses which contains the compound represented in formula (1) or a pharmaceutically acceptable salt thereof as the active component (in the formula, R1 represents a hydrogen, etc., R2 represents a halogen, etc., and R3, R4, R5 and R6 represent a hydrogen, etc.).