351 results about "Schizophrenia disorder" patented technology

The invention discloses a rehabilitation training evaluation system for dealing with schizophrenia patients, and relates to the technical field of data processing, the evaluation system comprises a multi-modal sensing module, an edge intelligent processing module and a dynamic graph network evaluation module; according to the technical key points, neurophysiology, behavior tracks, cognitive functions and environmental parameters are fused to form a'microscopic neural activity-mesoscopic behavior performance-macroscopic environment interaction 'full-dimension evaluation network, for example, the recessive decoupling phenomenon of'reduced brain oxygen metabolism but normal autonomic nerve function' of a negative symptom patient can be synchronously captured, and the accuracy of the evaluation network is improved. The method comprises the following steps of: firstly, quantifying the coordination and causality of data of different dimensions through a dynamic graph network, disclosing a dynamic association path of insufficient activation of a forehead cortex, social attention distraction and cognitive task error rate increase, and providing a visual basis for mechanism research and intervention target selection.

The invention relates to a system for early detection and risk prediction of schizophrenia. The system comprises a feature extraction module which performs parallel processing on a multi-mode original signal; the fusion prediction module fuses the voice feature signal, the behavior feature signal and the clinical feature signal through a cross-modal attention weighting mechanism to generate a fusion feature vector, and outputs an initial risk prediction signal through a machine learning classifier based on the vector; the dynamic updating module receives an incremental characteristic signal triggered by newly added collected data of a user, generates an updating risk prediction signal through incremental learning, and feeds back the updating risk prediction signal to the fusion prediction module to optimize the weight of a classifier; and the visual output module converts the updated risk prediction signal into a dynamic risk trajectory diagram and an interpretable feature contribution thermodynamic diagram. The system for early detection and risk prediction of schizophrenia can solve the problems that early screening of schizophrenia lags behind and dynamic risk assessment is missing.

The invention discloses a schizophrenia early warning and evaluation system based on human brain multi-region signals, and relates to the technical field of early warning and evaluation, and the system comprises a neural connection analysis unit which is used for recognizing neural connection characteristics of a brain network based on biomarker characteristics, comparing the neural connection characteristics with a database, and determining the neural connection characteristics of the brain network; clustering individuals with similar neural connection features according to a comparison result to generate a risk group; the emotion perception analysis unit is used for acquiring facial emotion states of the risk group in a preset time period and predicting evolution trends of neural connection features in different facial emotion states; and the early warning evaluation unit is used for inputting the evolution trend of the neural connection characteristics into a pre-constructed evaluation model, outputting a risk level evaluation result and formulating an early warning measure based on the risk level evaluation result. According to the method, neural connection features and emotion perception analysis are combined, so that early-stage neural connection abnormity and emotion turning intervals of schizophrenia can be accurately identified and positioned.

The invention belongs to the technical field of electroencephalogram signal processing, and discloses a method for identifying schizophrenia by utilizing an EEGNet model based on electroencephalogram signals, which comprises the following steps of: acquiring the electroencephalogram signals of a subject, preprocessing the electroencephalogram signals, converting time domain signals into frequency domain signals by using Fourier transform on the preprocessed electroencephalogram signals, and then extracting gamma frequency bands; according to a one-leaving cross validation method, dividing the extracted gamma frequency band into a training set and a test set, and dividing a validation set based on the training set; constructing an EEGNet model, and training the EEGNet model through the training set, the test set and the verification set to obtain a trained EEGNet model; and inputting a to-be-detected electroencephalogram signal into the trained EEGNet model to obtain a classification result. The method is suitable for diagnosing whether the patient suffers from schizophrenia or not according to the electroencephalogram signals, the deviation of subjective judgment can be reduced, and the misdiagnosis rate is reduced.

The present application relates to a sustained release delivery composition of a vesicular monoamine transporter type 2 (VMAT2) inhibitor, a deuterated derivative thereof, a pharmaceutically acceptable salt thereof, an active metabolite thereof, or a prodrug thereof for treatment of hyperkinetic movement disorders including, but not limited to, tardive dyskinesia (TD), Huntington's disease (HD) chorea, tremors, dystonia, chorea, tics, myoclonus, stereotypies, restless legs syndrome, and various other disorders with abnormal involuntary movements. The present application also relates to a sustained release delivery composition of an antipsychotic agent, a pharmaceutically acceptable salt thereof, an active metabolite thereof, or a prodrug thereof for treatment of schizophrenia, bipolar disorder, and other psychiatric diseases or disorders. The method of making or using the composition is also disclosed.

The invention relates to the technical field of trajectory accompanying, in particular to a schizophrenia prediction model construction method based on multi-modal subject data and artificial intelligence, which comprises the following steps: standardizing a prospective queue design and a multi-modal data acquisition protocol; carrying out synchronous acquisition and quality control on multi-modal data; carrying out dimension reduction and cross-modal alignment fusion processing on the data; and combining an improved loss function and a self-adaptive optimization algorithm to train the model, and performing verification and iterative optimization. According to the schizophrenia prediction model construction method based on the multi-modal subject data and the artificial intelligence, multi-modal standardized acquisition is taken as a core, multi-dimensional pathological support is provided for prediction through multi-modal data deep coverage and standardized acquisition, and refined preprocessing is designed for different modals, so that the schizophrenia prediction model construction efficiency is improved. In combination with modal specific feature extraction and cross-modal alignment fusion, a dynamic weighted multi-modal network is matched with multi-task learning and improved AdamW optimization, and in combination with comparison pre-training, characterization capability and training efficiency are improved.

Provided are compounds of Formula I and Formula II, described herein, processes for their preparation, their use as pharmaceuticals, and pharmaceutical compositions comprising them and intermediates used in their preparation. Compounds of Formula I and Formula II are useful, for instance, in modulating dopamine and serotonin neurotransmission and treating disorders that may benefit from the same, such as schizophrenia and depression.

The invention discloses a schizophrenia risk structure variation identification and function evaluation method based on three-generation sequencing, and relates to the field of molecular biology, whole genome sequencing is performed on peripheral blood DNA of a patient through three-generation sequencing, multi-tool joint detection is adopted, multi-sample results are integrated, and a high-confidence SV data set is generated; through cross-queue comparison, the patient specific SV is screened, and the high-risk potential pathogenic SV is identified in combination with an SV priority ordering tool SVJudge. By combining transcription factor binding analysis, SCZ drug target data and histocyte specific expression data, the influence of SV on gene regulation is evaluated, and the potential action mechanism of SV in SCZ is analyzed. The SCZ risk gene is screened based on SVJudge scoring and patient carrying conditions, the genetic risk and pathogenic mechanism of the SCZ are analyzed and verified through pathway enrichment, a protein interaction network and a functional module, and a new technical means and theoretical basis are provided for genetic research of the SCZ.

The invention relates to the technical field of biology, in particular to application of synaptic binding protein 11 serving as a marker to preparation of products for diagnosis, auxiliary diagnosis, screening or auxiliary screening of schizophrenia and application of the synaptic binding protein 11 and / or a dopamine D2 receptor serving as a core target to treatment of schizophrenia. The application of the synaptic binding protein 11 or the transcriptional product mRNA or the mRNA fragment of the synaptic binding protein 11 as the marker in any one of the following items: 1) application in preparation of products for diagnosis or auxiliary diagnosis of schizophrenia of human or non-human mammals; 2) application in preparation of products for screening or auxiliary screening of schizophrenia of human or non-human mammals; 3) application in diagnosis or auxiliary diagnosis of schizophrenia of human or non-human mammals to be detected; and 4) application in screening or auxiliary screening of schizophrenia of human or non-human mammals to be detected. Meanwhile, the invention also provides application of treating three symptoms of schizophrenia through a brain region specific targeting dopamine D2 receptor. Researches find that synaptic binding protein 11 or a transcription product mRNA or mRNA fragment thereof can be used as a brand new functional schizophrenia diagnosis marker, and a chemical molecule or gene therapy drug targeting synaptic binding protein 11 or a dopamine D2 receptor can effectively improve three symptoms of schizophrenia.

The invention provides a signal optimization preprocessing-based adolescent schizophrenia deep learning recognition system and method. The system comprises a data preprocessing module, a data fusion module and a schizophrenia recognition module based on a deep learning model which are arranged in sequence, the data preprocessing module is used for carrying out filtering processing on the screened electroencephalogram signals, retaining frequency bands which are most obviously represented by schizophrenia, extracting power characteristics of each frequency band through a time-frequency analysis method, and carrying out compression and normalization processing; the data fusion module is used for disrupting and fusing a plurality of electroencephalogram signal data sets; and the schizophrenia identification module based on the deep learning model is used for realizing accurate identification of the schizophrenia patient in combination with the deep learning model. By optimizing the data processing flow and the model training method, the schizophrenia identification accuracy and stability are improved.

The disclosure provides methods for the for treatment of psychosis, such as schizophrenia, or depression (such as bipolar depression) and / or anxiety, comprising administering to a patient in need thereof, (i) a 5-HT2A or 5-HT2A / D2 receptor ligand, for example a substituted heterocycle fused gamma-carboline as described herein, in free, pharmaceutically acceptable salt or prodrug form, and (ii) a nitric oxide donor, separately (sequentially or simultaneously), or in combination (e.g., in a fixed dose combination).

The invention discloses a schizophrenia patient medication compliance evaluation electronic system based on big data, and relates to the technical field of evaluation.The schizophrenia patient medication compliance evaluation electronic system comprises a data monitoring module, a data analysis module, a treatment adjustment module and a database, and is characterized in that the heart rate, blood pressure and body feedback of each patient after each time of medication in each treatment time period are detected; the method comprises the following steps: analyzing the medication compliance coefficient and the body feedback condition of each patient according to the treatment time of each patient, the blood concentration and the disease recovery score of each patient by a doctor, judging whether each patient needs to adjust the used medicine in each treatment time period, and after each treatment time period of each patient in a hospital is finished, judging whether the patient needs to adjust the used medicine. Whether each patient can go home for treatment or not is judged according to the medication compliance coefficient of each patient in each treatment time period, the illness state recovery score and the historical illness condition after each time of going home, the illness state stability of the patient after going home for treatment is guaranteed, and the accuracy of medication compliance coefficient analysis of the patient and the effectiveness of medication treatment are guaranteed.

The invention discloses a coupling tensor decomposition method based on sparse low-rank constraint, which is used for characteristic decomposition of a multi-frequency dynamic function network connection tensor in resting state function magnetic resonance imaging data. According to the algorithm, on the basis of the traditional coupling canonical factorization (CCPD), an optimization model of sparse and low-rank constraint is constructed, and the sparse and low-rank constraint optimization model is constructed by the algorithm. On the spatial connectivity dimension, redundant function connection is reduced through an L1 sparse penalty term, and the spatial specificity of the key brain network is enhanced; and in time and frequency band dimensions, low-rank regularization constraint is adopted to improve discrimination of cross-subject time sequence characteristics. Generally speaking, the method can effectively extract connectivity characteristics with statistical significance and time states of different frequency bands from dynamic function network connection tensors of multiple frequency bands, thereby effectively identifying functional connection heterogeneity characteristics between schizophrenia patients and healthy control groups.

The present disclosure relates to compounds of formula (I) that are useful as modulators of α7 nAChR, compositions comprising such compounds, and the use of such compounds for preventing, treating, or ameliorating disease, particularly disorders of the central nervous system such as cognitive impairments in Alzheimer's disease, Parkinson's disease, and schizophrenia, as well as for L-DOPA induced-dyskinesia and inflammation.

Provided herein are 19-nor C3,3-disubstituted C21-pyrazolyl steroids of Formula (I):and pharmaceutically acceptable salts thereof; wherein , R1, R2, R3a, R3b, R4a, R4b, R5, R6, and R7 are as defined herein. Such compounds are contemplated useful for the prevention and treatment of a variety of CNS-related conditions, for example, treatment of sleep disorders, mood disorders, schizophrenia spectrum disorders, convulsive disorders, disorders of memory and / or cognition, movement disorders, personality disorders, autism spectrum disorders, pain, traumatic brain injury, vascular diseases, substance abuse disorders and / or withdrawal syndromes, and tinnitus.

The present disclosure provides a method of treating schizophrenia or a schizophrenia-related disorder in a patient in need thereof. The method comprises orally administering to the patient, twice daily via a titration regimen, an oral pharmaceutical composition comprising a plurality of xanomeline beads containing xanomeline or a salt thereof, and a plurality of trospium chloride beads containing a trospium chloride salt, the titration regimen comprising increasing titration of the xanomeline or a salt thereof and the trospium chloride salt.

The present invention relates to a heterocyclic carbonyl derivative modulator, a preparation method therefor, and the use thereof. Specifically, the present invention relates to a compound represented by general formula (II-B), a preparation method therefor, a pharmaceutical composition containing said compound, and a use thereof as a modulator in the treatment of Alzheimer's disease, schizophrenia, pain, addiction, and sleep disorders, wherein each substituent in general formula (II-B) is as defined in the description.

The invention belongs to the technical field of biomarker detection, and relates to a biomarker, a kit and an application of the biomarker in early screening of severe cognitive impairment of schizophrenia, the biomarker is composed of a metabolite SM C20: 2 and a metabolite PC ae C36: 2, and compared with non-severe cognitive impairment (non-SCI) of schizophrenia, the biomarker has the advantages that the biomarker can be used for early screening of severe cognitive impairment of schizophrenia; the biomarker has an area of 0.685 under an ROC curve in a discovery severe cognitive impairment (SCI) sample and an area of 0.720 under an ROC curve in a verification set. The method can be used for early screening the cognitive severity of schizophrenia patients under the condition of no drug interference, and has good stability, repeatability, sensitivity and specificity. The kit has screening and typing functions when being used for detecting schizophrenia patients, can be used for identifying the schizophrenia patients and a healthy control group, and can also be used for distinguishing SCI (severe cognitive impairment) and non-SCI (non-severe cognitive impairment) groups to assist clinical personalized intervention.

The invention belongs to the technical field of image processing, and discloses a schizophrenia classification method based on connection gradient, which comprises the following steps: acquiring magnetic resonance imaging data, and preprocessing the magnetic resonance imaging data; performing brain region division on the preprocessed magnetic resonance imaging data, and constructing a functional connection network and a morphological similarity network; constructing a function connection gradient and a form similarity gradient by using a connection gradient algorithm based on the function connection network and the form similarity network; performing threshold processing on the function connection network and the morphological similarity network to serve as edge features, taking the bimodal connection gradient as node features, and constructing a graph convolutional network classification model; and training and testing the graph convolutional network classification model. According to the schizophrenia classification method based on the connection gradient, schizophrenia classification identification is realized by using the graph convolutional network classification model, the classification accuracy is improved, and the application value is higher.

The invention provides a schizophrenia core epicenter region identification method based on a multi-mode nerve image, and belongs to the technical field of medical image analysis and neuropsychiatric disease diagnosis. According to the method, a macroscopic-mesoscopic-microscopic covariant network model is established by integrating multi-modal nerve image data (including structural magnetic resonance, functional magnetic resonance imaging and diffusion tensor imaging) and mesoscopic-microscopic scale data (gene expression atlas, neurotransmitter distribution and cell construction characteristics). The method comprises the following steps: firstly, constructing various connection networks, then identifying a core epicenter region with abnormal grey matter thickness based on a graph theory algorithm, carrying out cross-scale matching on the epicenter region and gene expression, neurotransmitter and cell structure characteristics by utilizing spatial correlation analysis, and analyzing a diffusion path of the epicenter region through connection omics. By fusing the multi-scale biomarkers, the limitation of single modal analysis is broken through, and systematic analysis of the schizophrenia pathological network is realized.

The present invention provides combinations of biomarkers that can be used in the diagnosis and differentiation of bipolar disorder and schizophrenia. The present invention therefore provides methods of differentiating, diagnosing and treating bipolar disorder and schizophrenia, by examining relevant proteins and RNA in a patient sample.

Disclosed is a method and an oral composition for treating CNS disorders. An embodiment of the invention comprises orally administering an allopregnanolone containing composition to a subject having a CNS disorder. Exemplary CNS disorders include sleep disorders, mood disorders, dysthymic disorders, bipolar disorders, anxiety disorders, stress disorders, compulsive disorders, schizophrenia spectrum disorders, convulsive disorders, memory disorders, cognition disorders, movement disorders, personality disorders, autism spectrum disorders, substance abuse disorders, dementia, pain, traumatic brain injury (TBI), vascular disease, withdrawal syndrome, and tinnitus. The composition and methods disclosed herein exhibit effective oral absorption / bioavailability.

The invention provides a microbial marker and a prediction model for schizophrenia with social anxiety disorder. The microbial marker is a Bacteroides microorganism in saliva. The relevance between the relative abundance of Bacteroides microorganisms in saliva and social anxiety disorder is found for the first time. A Nomogram clinical prediction mode is formed by taking the specific microbial marker as a microbial marker in combination with a children bad experience scale and 6-hydroxymelatonin and 5-hydroxyindoleacetic acid in serum, so that objective biological indexes can be provided for early diagnosis and monitoring of whether schizophrenia is accompanied by social anxiety disorder or not; errors of subjective evaluation are reduced; moreover, the oral saliva sample has the obvious advantage of being easy to collect for psychiatric patients, the working difficulty of medical staff can be greatly reduced, and revolutionary changes of prediction, diagnosis and treatment of schizophrenia accompanied with social anxiety disorder are expected to be brought.

The invention discloses a schizophrenia auxiliary diagnosis method based on spatio-temporal dynamics, and belongs to the technical field of biomedical image mode recognition. A functional brain network image is obtained by f-MRI, and a space-time dynamic model is constructed by means of a Markov chain, so that an individual functional brain network convergence mode and a maximum information flow transmission mode between brain networks can be extracted. Three corresponding index quantization function brain network spatio-temporal dynamic modes are provided, and the three corresponding index quantization function brain network spatio-temporal dynamic modes are a network stationary probability vector, a network optimal step number mean value matrix and a network direct connection strength matrix. According to the method, an effective auxiliary diagnosis model is constructed for three proposed functional brain network spatio-temporal dynamic indexes by means of a statistical test method and a logistic regression algorithm, so that accurate quantitative calculation of the probability of suffering from schizophrenia of an individual is realized, and a powerful technical support is provided for early diagnosis and intervention of schizophrenia; the kit fills the blank of the existing diagnosis technology in the field, and has remarkable clinical application value and scientific significance.

Described herein are steroids of Formula (I):and pharmaceutically acceptable salts thereof, wherein R1, R2a, R2b, R3, R4, R5a, R5b, R6, and Z are as defined herein. Such compounds are contemplated useful for the prevention and treatment of a variety of CNS-related conditions, for example, treatment of sleep disorders, mood disorders, schizophrenia spectrum disorders, convulsive disorders, disorders of memory and / or cognition, movement disorders, personality disorders, autism spectrum disorders, pain, traumatic brain injury, vascular diseases, substance abuse disorders and / or withdrawal syndromes, and tinnitus.

The present invention comprises a series of CBC derivative compounds and a method for their use. The compounds may be used to treat and / or diagnose cancer, Parkinson's Disease, Alzheimer's Disease, schizophrenia, depression, anxiety, substance abuse disorders, pain, and / or other diseases and disorders. The compounds may exist as a pharmaceutically acceptable salt or a stereoisomer thereof.

The invention relates to a heterocyclic carbonyl derivative regulator as well as a preparation method and application thereof. In particular, the present invention relates to a compound represented by general formula (I), a preparation method thereof, a pharmaceutical composition containing the compound, and a use thereof as a modulator in the treatment of Alzheimer's disease, schizophrenia, pain, addiction and sleep disorders, each substituent in the general formula (I) being as defined in the specification. # imgabs0 #

The invention relates to the technical field of psychiatric disease diagnosis, and discloses a defective schizophrenia prediction and analysis system based on multi-modal brain network characteristics, which comprises an image data acquisition module, an image data processing module, a deep learning model module and a prediction module which are connected in sequence, the image data acquisition module acquires a brain MRI image; the image data processing module carries out cortical and subcortical reconstruction on the brain MRI image to obtain a model input index; the deep learning model module performs training by using a training data set composed of a plurality of brain MRI images and defective schizophrenia diagnosis results thereof to obtain a target prediction model; and the prediction module inputs the real-time brain MRI image into the target prediction model to obtain a prediction result. According to the method, the multi-dimensional brain structure image features are integrated, diagnosis information contained in brain region changes is fully mined, prediction results of defective schizophrenia and non-defective schizophrenia are improved, and a more comprehensive biological basis is provided for clinical diagnosis.

The invention provides a metabolic marker for auxiliary diagnosis of schizophrenia, a construction method and application. Wherein the metabolic marker comprises lysophosphatidylcholine (LPC) (17: 0 / 0: 0), oleamide, lysophosphatidyl ethanolamine LysoPE (18: 0 / 0: 0), palmitamide, lysophosphatidyl ethanolamine LysoPE (0: 0 / 20: 4), lysophosphatidyl ethanolamine LysoPE (22: 6 / 0: 0), p-methylphenol sulfate, lactic acid, 3-hydroxybutyric acid, lysophosphatidyl ethanolamine LysoPE (18: 2 / 0: 0), lysophosphatidyl ethanolamine LysoPE (16: 0 / 0: 0) and sphingosine 1-phosphate. By utilizing the metabolic marker, auxiliary diagnosis of schizophrenia can be rapidly and accurately completed in the early stage of disease attack.

The present application relates to the technical field of data analysis, and discloses an auxiliary identification method, device and equipment for early schizophrenia and a storage medium, which are used to improve the auxiliary identification accuracy of schizophrenia. The auxiliary identification method for early schizophrenia comprises: classifying multidimensional data of a target individual, including a topology structure type, a sequence type and a type without special structure; using a graph neural network, a recurrent neural network and a convolutional neural network to extract feature information of the three types of data respectively; fusing the feature information to form a target vector; processing the target vector through a preset early schizophrenia identification model to obtain an identification result, so as to determine the risk degree of the target individual in the early stage of schizophrenia.