310 results about "Mental disease" patented technology

Dosage forms, drug delivery systems, and methods related to sustained release of dextromethorphan or improved therapeutic effects are disclosed. Typically, an antidepressant, such as bupropion or a related compound is orally administered to a human being to be treated with, or being treated with, dextromethorphan.

The invention provides a common mental disease intelligent diagnosis method and system based on multiple agents, and relates to the field of medical artificial intelligence. The method comprises the following steps: S1, extracting diagnosis standards and symptom characteristics of common mental disorders, and constructing a similar patient knowledge base after verification and evaluation of the similar patient knowledge base and expert calibration; s2, acquiring clinical data from a hospital information system, and performing large medical record structuring, clinical scale simplification and scale score analysis on the clinical data to form a similar patient database; and S3, performing symptom matching and scale performance analysis on the input clinical data, constructing a multi-agent mental disease diagnosis framework, and performing multi-agent diagnosis debate based on the multi-agent mental disease diagnosis framework. The technical problems of symptom overlapping and diagnosis subjectivity among common mental diseases are solved by constructing a multi-agent cooperative diagnosis framework, introducing particle size symptom analysis and dynamically integrating structured authoritative medical diagnosis standards.

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 provides an intelligent mental disease recognition method and device based on multi-modal data and a medium. The method comprises the steps that illness state self-described text information and ear image data of a to-be-tested person and impedance and / or temperature information of all designated acupoints in the auricular concha area and the helix area of the to-be-tested person are obtained; performing local feature extraction on the ear image data to obtain an image feature vector; capturing measured value feature vectors between impedance and / or temperature information of different acupoints by using a multi-head attention mechanism; extracting semantic feature vectors of the illness state self-described text information; and forming a multi-modal fusion feature vector from the image feature vector, the measured value feature vector and the semantic feature vector, inputting the multi-modal fusion feature vector into an intelligent disease identification model for classification, and outputting a mental disease prediction result. According to the method, the ear image, the impedance and / or temperature information of each acupuncture point and the multi-modal data of the text information of the patient are fused, and the deep learning model is combined to realize disease classification, so that the recognition efficiency and accuracy are improved.

The invention relates to a sodium channel regulator for inhibiting Nav1.8, and provides a compound as shown in a formula I which can be used as the sodium channel regulator for inhibiting Nav1.8, and an isomer of the compound, or a pharmaceutically acceptable salt of the compound, the invention also provides a pharmaceutical composition containing the compound and the isomer thereof, 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 present invention addresses the problem of providing a novel compound that has an OX2R agonist activity.　The present invention relates to a substituted sulfonamide compound which is represented by formula (I) or a pharmacologically acceptable salt thereof. A compound according to the present invention, or a pharmacologically acceptable salt thereof, has an agonist activity against OX2R, and is useful as, for example, a therapeutic agent for sleep disorders associated with OX2R (for example, narcolepsy, idiopathic hypersomnia, Kleine-Levin syndrome, hypersomnia associated with a physical disease, hypersomnia associated with a mental disease, hypersomnia associated with a drug or a substance, circadian rhythm sleep-wake disorders, insufficient sleep syndrome, and extended sleep).

A system for and a method of diagnosing a mental illness in a patient are disclosed. The method measures signals, such as EEG signals, on a patient and applies a trained machine learning model on these signals. The model classifies the patient as having a mental illness or of being a normal control. The results are communicated to a user. In addition, a sub-type of a mental disorder may be identified by using other machine learning techniques on the features of the signals, such as a neural network, clustering, dimension reduction, and visualization algorithms. One such technique is t-distributed stochastic neighbor embedding (t-SNE).

The invention discloses a mental disease medical auxiliary diagnosis method based on a bimodal knowledge graph and a related device, and relates to the technical field of mental disease diagnos.The method comprises the steps that a static medical diagnosis knowledge graph is constructed; generating a scenarized problem library based on the static medical diagnosis knowledge graph; dynamically updating a personal dynamic knowledge graph based on interaction between the scenarized question library and a user; and finally, optimizing the static medical diagnosis knowledge graph based on the personal dynamic knowledge graph. According to the invention, through cooperation of the static and dynamic knowledge maps, objectivity, dynamics and accuracy of mental disease diagnosis are improved, and closed-loop management of medical auxiliary diagnosis is realized.

The invention provides a method for constructing a chronic unpredictable negative stress model, the chronic unpredictable negative stress model and application of the chronic unpredictable negative stress model in mental disease research, and belongs to the field of brain organ in-vitro model construction. Through dual verification of a microelectrode array and single cell sequencing, an in-vitro brain organ model capable of simulating chronic unpredictable negative stress core characteristics is successfully constructed, and a potential neural network function reconstruction mechanism and a cell molecule basis of the in-vitro brain organ model are disclosed. The comprehensive research normal form breaks through the limitation of a traditional single technology platform, and an accurate and efficient innovative research platform highly related to human is provided for pathogenesis research of mental diseases, especially chronic stress related diseases such as depression and anxiety and development of novel treatment strategies.

The invention discloses an obsessive-compulsive disorder diagnosis system based on brain multi-modal indexes, and belongs to the field of mental diseases. The problem of lack of a cross-modal feature fusion mechanism is solved. The system comprises an electroencephalogram signal acquisition unit used for acquiring an EEG signal of a testee under a preset stimulation normal form and executing preprocessing operation; the brain imaging data acquisition unit is used for synchronously acquiring brain structure imaging data and brain function imaging data of the testee; the multi-modal data fusion unit is used for extracting frequency band power spectrum density characteristics and event-related potential amplitude or incubation period characteristics from the EEG signals; performing standardization processing on the EEG features, the sMRI structural features and the fMRI functional features; integrating modal features by adopting a weighted average fusion algorithm; screening fused feature subsets through a recursive feature elimination method; and the diagnosis model unit is used for inputting the fusion feature vector into a trained SVM classification model and outputting an obsessive-compulsive disorder diagnosis result. Used in the medical field.

Described herein, in part, are methods useful for preventing and / or treating a disease or condition relating to aberrant function or activity of a T-type calcium channel, such as psychiatric disorders (e.g., mood disorder (e.g., major depressive disorder)), pain, tremor (e.g., essential tremor), seizures (e.g., absence seizures), epilepsy, or an epilepsy syndrome (e.g., juvenile myoclonic epilepsy). The present invention further comprises methods for modulating the function of a T-type calcium channel and methods of administering a titrated dosage of a T-type calcium channel antagonist.

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 an AI intelligent regulation and control mental disease treatment system based on multi-mode microwaves, and the system comprises a data collection module which collects multi-mode microwave data, and the multi-mode microwave data comprises basic microwave data, neural pathway activity state data and personalized physiological data; the optimization regulation and control module is used for extracting optimal regulation and control data in the multi-modal microwave data through an improved deep convolutional neural network; the dynamic strategy module is used for collecting historical microwave stimulation data, and learning and optimizing a nonlinear relationship between regulation and control data and microwave stimulation parameters through a hybrid transfer learning model to obtain optimal microwave stimulation parameters for specific brain region neural activities; and the treatment execution module is used for controlling microwave emission equipment based on the optimal microwave stimulation parameters, emitting microwaves to the corresponding brain region of the patient for treatment, generating a three-dimensional visual report of the treatment progress, dynamically adjusting model parameters, realizing real-time optimization of treatment parameters and solving the limitation of static parameters in a traditional method.

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 cognitive function evaluation method based on neurovascular coupling analysis. The method comprises a signal preprocessing step, a signal processing step, a feature extraction step and a coupling coefficient calculation step. A new normal form of cognitive evaluation is constructed from nerve-blood vessel coupling and cross-system cooperation dimensions, and accurate description of a cognitive state is realized through real-time quantification of a dynamic cooperation effect of brain nerve activity (EEG) and blood flow regulation (CBP variability) and establishment of an association relationship between a dynamic cooperation index and cognitive ability (alertness, reaction speed and memory). And a new objective basis is provided for the fields of plateau medicine, intelligent health, neurological and mental disease diagnosis and treatment and the like.

The invention can provide compounds, analogs of blebbistatin, effective and selective inhibitors of nonmuscle myosin II relative to cardiac myosin II. Compounds can be used in the method of treating a disease, disorder, or medical condition in a patient, comprising modulating myosin II ATPase, such as treatment of substance abuse relapse disorder, or of renal disease, cancer and metastasis, benign prostate hyperplasia, hemostasis or thrombosis, nerve injury including retinal damage, lung fibrosis, liver fibrosis, arthrofibrosis, wound healing, spinal cord injury, periodontitis, glaucoma and immune-related diseases including multiple sclerosis; or wherein the disease, disorder, or medical condition comprises addiction including abuse of or addiction to anything classified as a Substance-Related or Addictive Disorder in the Diagnostic and Statistical Manual of Mental Disorders (DSM), such as, but not limited to, cocaine, opioids, amphetamines, ethanol, cannabis / marijuana, nicotine, and activities including gambling.Compounds are of general formulawith substituents as defined herein.

The invention discloses a mental disease brain dysfunction detection method based on dynamic causal modeling, and belongs to the field of biomedical engineering. The dynamic causal relationship of information transmission among regions of the brain can be accurately captured by adopting dynamic causal modeling based on electroencephalogram, and the method has unique advantages in the aspect of revealing abnormal modes of mental disease information transmission. Different from a traditional functional brain network construction mode, the system pays more attention to capturing the causal relationship and dynamic change of brain intervals when processing and explaining data, the modeling method based on the causal relationship can provide more detailed and personalized information transmission analysis to a certain extent, a more accurate result is provided, and the method is suitable for popularization and application. Furthermore, the functional disorder in the mental disease brain neural network can be sensitively recognized, the pathological mechanisms of bipolar affective disorder, schizophrenia and split affective disorder can be revealed, and support is provided for early diagnosis and personalized treatment.

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 present invention addresses the problem of providing a novel compound that has OX2R agonist activity.　The present invention relates to a substituted sulfonamide macrocyclic compound represented by formula (I) or a pharmacologically acceptable salt thereof. This compound, or a pharmacologically acceptable salt thereof, has agonist activity against OX2R, and is useful as a treatment agent, etc., for sleep disorders involving OX2R (e.g., narcolepsy, idiopathic hypersomnia, Kleine-Levin syndrome, hypersomnia associated with a physical disease, hypersomnia associated with a mental disease, hypersomnia associated with a drug or a substance, circadian rhythm sleep-wake disorder, insufficient sleep syndrome, and extended sleep).

The invention provides a mental disease classification method and system based on an individual difference structure covariant network and machine learning, and belongs to the field of mental disease classification. The problem of classification performance bottleneck caused by heterogeneity of mental diseases in the prior art is solved. The method comprises the following steps: acquiring a structural magnetic resonance T1 weighted image, and preprocessing the image; performing brain region segmentation on the pre-processed T1 image based on an AAL brain map, and extracting the gray matter volume of each brain region; constructing an IDSCN network by calculating the Pearson's correlation coefficient of the brain grey matter volume of the paired brain regions; calculating the area under a node topological attribute curve of the IDSCN network; screening node attribute indexes with statistical differences between the patient group and the healthy control group through double-sample t test; and taking the screened node attribute indexes as feature vectors, and inputting the feature vectors into a support vector machine classification model for disease classification. The method is mainly used in the medical image processing field.

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.

This disclosure relates to, inter alia, certain compounds, compositions, and pharmaceutical compositions thereof, that modulate the activity of the transporter protein vesicular monoamine transporter- 2 (VMAT2) and are directed to methods useful in the treatment of transporter protein vesicular monoamine transporter-2 mediated disorders, such as, neurological or psychiatric disease or disorders, including but not limited to, hyperkinetic movement disorders (e.g., tardive dyskinesia, Tourette's syndrome, Huntington's disease, tics, ataxia, chorea (such as, chorea associated with Huntington's disease), dystonia, hemifacial spasm, myoclonus, restless leg syndrome, and tremors). The disclosure further relates to synthetic methods and intermediates useful in the preparation of compounds.

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.

Systems and methods for the identification of auto-antibodies associated with central nervous system or psychiatric disease or disorders. Also disclosed herein are methods of treating central nervous system or psychiatric disease or disorders associated with auto-antibodies.

The invention discloses an MDD early-stage dynamic diagnosis method based on a multi-modal attention network, and relates to the technical field of MDD diagnos.According to the method, early-stage diagnosis analysis is carried out on various types of mental diseases through multi-modal data dynamic fusion and a layered attention mechanism in combination with a multi-modal attention network diagnosis model; the fitting degree of the representation data of the target diagnosis user and various types of mental diseases is deeply analyzed, the diagnosis cycle for the target diagnosis user is further dynamically set, the situation of diagnosis errors caused by one-time diagnosis of multi-modal data is avoided, corresponding diagnosis cycles are customized for different target diagnosis users, and the diagnosis accuracy is improved. The accuracy, the timeliness and the clinical practicability of MDD early diagnosis are remarkably improved, the reliability of clinical decision and the resource allocation efficiency are further enhanced, and an innovative technical scheme is provided for precise prevention and control of mental diseases.

Methods for the diagnosis, prognosis, patient stratification and prediction of pharmacological response in patients afflicted by psychiatric disorders based on electroretinography (ERG) parameters are described.