533 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 relates to the technical field of physical sign monitoring, in particular to a physical sign parameter monitoring method accompanying the medication process of a severe mental disease patient. According to the method, the dynamic change of the physical sign parameters is captured by acquiring the physical sign parameter time sequence curves of the to-be-monitored patient in the two stages before and after medication. For each time sequence curve, data value differences and fluctuation duration are analyzed, and abnormal fluctuation data segments are identified. Furthermore, a historical normal time sequence curve is utilized to determine a historical normal fluctuation section corresponding to the abnormal fluctuation, and the overall state of the physical sign parameters is visually reflected through comparative analysis and calculation of a stable index. Meanwhile, the physical sign condition of the patient is monitored by combining the stability index of the same physical sign parameter before and after medication and the time sequence characteristics of the abnormal fluctuation data segment. According to the method, the physical sign state of the patient is reflected more accurately by comparing the change of the physical sign parameters of the patient with the historical normal condition, and data support with higher credibility is provided for doctors to carry out medicine effect judgment or illness state analysis.

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 a mental disease identification method based on pulse structure-functional brain network coupling, belongs to the technical field of intelligent auxiliary medical diagnosis, and effectively solves the technical problem of neglected neurobiological mechanism between structural connection and functional connection in the traditional mental disease diagnosis process. According to the technical scheme, firstly, functional and structural brain networks are extracted from functional magnetic resonance imaging and diffusion tensor imaging; then, information feature maps of the two brain networks are extracted through a BrainNetCNN; then constructing a pulse coupling neural network to learn a brain structure-function coupling mechanism so as to obtain pulse structure-function coupling; and finally, inputting the obtained coupling information into a classification layer to obtain a disease recognition result, and training and optimizing the result by using a cross entropy loss function. The beneficial effects of the invention are that the method is helpful for deep understanding of neurological mechanisms of mental diseases, and has wide application prospects in clinical application.

Provided herein are compounds of Formula (I) and Formula (II), or a pharmaceutically acceptable salt thereof, wherein R1, R2, R3, R4, R5, R6, R7, and X are defined herein. Also provided are pharmaceutical compositions comprising a compound of Formula (I) and (II) or a pharmaceutically acceptable salt thereof, and methods of using a compound of Formula (I) and (II) or a pharmaceutically acceptable salt thereof, e.g., in the treatment of a mental disease or disorder.

The invention discloses a white matter fiber bundle structure-function connection coupling method based on a distance function. The method comprises the steps that brain imaging data are collected and preprocessed; extracting white matter fiber bundle structure attributes and function signals of the preprocessed brain imaging data; constructing a structure connection matrix and an individualized function connection matrix according to the white matter fiber bundle structure attributes and the function signals; measuring the connection weight similarity between corresponding columns of the structure connection matrix and the function connection matrix through a distance function, and carrying out white matter fiber bundle structure-function connection coupling according to the connection weight similarity; through the method, the relationship of the white matter fiber bundle structure and function of the brain can be quantified, and the method is suitable for brain structure and function analysis based on the white matter fiber bundle and research on brain development and mental diseases.

Provided herein are compounds of Formula (I) and Formula (II), or a pharmaceutically acceptable salt thereof, wherein R1, R2, R3, R4, R5, R6, R7, and X are defined herein. Also provided are pharmaceutical compositions comprising a compound of Formula (I) and (II) or a pharmaceutically acceptable salt thereof, and methods of using a compound of Formula (I) and (II) or a pharmaceutically acceptable salt thereof, e.g., in the treatment of a mental disease or disorder.

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.

Provided are piperazine fused ring system derivatives of formula (I), pharma- ceutically acceptable salts thereof, processes for their preparation and pharmaceutical compositions comprising said compounds, and uses of said compounds as polyadenosine diphosphate ribose (ADP-ribose) polymerase (PARP) inhibitors in the treatment of related diseases such as cancer, inflammation, metabolic diseases, cardiovascular diseases, immune diseases and psychiatric diseases. [Formula 1] JPEG2025503798000246.jpg20169

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).

Provided herein are elastin-like polypeptides, endosomal escape peptides, and compounds comprising an elastin-like polypeptide chemically or recombinantly conjugated to one or more endosomal escape peptides. Also provided herein are compositions comprising a compound provided herein and an agent, methods of delivering an agent, and methods of treating or preventing a disease (e.g., a cardiovascular disease, a lung or respiratory disease, a musculoskeletal disease, a hematological disease, an immune disorder, an infectious disease, a genetic disease, cancer, a neurological disease, a psychiatric disorder, a metabolic disorder, or an inflammatory disease).

The present invention relates to an optimized polynucleotide encoding a brain-derived neurotrophic factor (BDNF), a transgenic expression cassette comprising the polynucleotide, a recombinant vector, and a medicament useful for the prevention or treatment of neurodegenerative diseases, neurodevelopment-related diseases, stroke, chronic pain, nerve injury, mental disorders, and / or metabolic diseases. The polynucleotide disclosed by the invention can efficiently express the BDNF through codon optimization, and particularly, the expression quantity of the BDNF in nerve cells is remarkably improved.

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).

The invention relates to the technical field of image processing, in particular to a mental patient auxiliary analysis method and device based on facial expression recognition, and the method comprises the steps: collecting a plurality of face images when a patient communicates with a doctor; obtaining a plurality of suspected Chinese character chuan-shaped pattern regions in the eyebrow region and a suspected Chinese character chuan-shaped pattern region sequence; acquiring a pupil area of each face image to obtain a plurality of facial expression change moments; obtaining the possibility that the expression of the patient is angry at each facial expression change moment; obtaining a plurality of anger stages of the patient; obtaining the time point when the doctor menses the keyword of the patient every time, obtaining the anger initial stage of each anger stage of the patient, and further obtaining the control ability of the patient on the own emotion in the conversation process; and the severity of the mental problem of the patient is obtained. The face change of the patient is analyzed, a doctor is assisted to judge the mental state of the patient, and the diagnosis and treatment effect is improved.

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 discloses bifidobacterium adolescentis as well as a culture method and an identification method thereof. Specifically, the invention provides the bifidobacterium adolescentis GSB03505, the preservation number of the bifidobacterium adolescentis GSB03505 is CGMCC (China General Microbiological Culture Collection Center) No.32483, and the invention provides a method for identifying the bifidobacterium adolescentis. The GABA yield of the bifidobacterium adolescentis reaches up to 3200mg / L and is obviously higher than that of the existing strain; in addition, the strain has the potential of being applied to living fungus food, health care products or medicines for improving sleep and regulating or treating mental diseases.

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.

The invention relates to the technical field of diagnosis markers and diagnosis methods for mental diseases of children and adolescents, in particular to a plasma metabolism diagnosis marker and diagnosis system for depression of children and adolescents. The plasma metabolism diagnosis marker comprises at least one of cyclic adenosine monophosphate, methylpyridine, unsaturated undecylenyl carnitine, histidine, prolyl hydroxyproline, tryptophan and glucuronic acid. According to the scheme, non-target metabonomics data is analyzed, and seven candidate diagnostic markers for depression of children and adolescents are screened and determined from common differential metabolites. Then, through targeted metabonomics research, diagnostic markers and combinations thereof with ideal diagnostic effects are determined, and then a diagnostic model is constructed. And a sex factor is also incorporated into the diagnosis model, so that the diagnosis effect is further improved. According to the technical scheme, the technical problem that in the prior art, reliability and popularization potential of depression plasma metabolism biomarkers of children and adolescents are not ideal can be solved, and the depression plasma metabolism biomarkers have ideal popularization and application prospects.

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 invention discloses a Mendel randomization-based mental disease drug targeted analysis method and system, and the method comprises the steps: carrying out the analysis processing through a Mendel randomization analysis process, and obtaining the causal effect estimation of each target gene corresponding to a plurality of phenotypes; performing multiple inspection correction on the causal effect estimation of each target gene corresponding to multiple phenotypes to obtain a significant target gene after correction; carrying out sensitivity analysis on the corrected significant target genes to obtain the target genes screened for the first time; performing co-localization analysis on the target genes screened for the first time to obtain target genes screened for the second time; carrying out pathway enrichment analysis on the target genes screened for the second time, then carrying out protein interaction network analysis, and finally carrying out drug matching on the target genes of the protein interaction network to obtain an old drug new use prediction result or taking the target genes as the target genes for guiding new drug preparation.

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.