198 results about "Autism disorder" patented technology

The invention discloses an intelligent intervention system for children with autism spectrum disorder based on multi-modal neural feedback, which comprises a multi-modal physiological signal acquisition module for acquiring electroencephalogram signals of a target child in real time and generating an original physiological signal set; the multi-modal fusion analysis module receives the original physiological signal set and generates a comprehensive evaluation signal representing the current neurocognitive state and the emotion awakening level of the child; the self-adaptive decision control module receives the comprehensive evaluation signal and generates a self-adaptive control signal containing a neural feedback parameter adjustment instruction and a game interaction strategy instruction; the neural feedback intervention module receives the neural feedback parameter adjustment instruction and generates an adaptive neural feedback stimulation signal to act on the child; and the intelligent interactive game module receives a game interactive strategy instruction and dynamically adjusts game scene contents. The intelligent intervention system for children with autism spectrum disorder based on multi-modal neural feedback can solve the problems of poor individual adaptation of autism intervention, multi-modal data splitting and lack of dynamic adjustment.

The invention relates to an infantile autism classification method based on a double-branch functional topological graph neural network. The infantile autism classification method can realize the classification of the infantile autism by using functional magnetic resonance imaging data. According to the provided autism classification network, long-distance connection and short-distance connection are divided based on the shortest path between brain intervals, then an exponential decay mask is introduced through a functional topological graph Transform branch to adjust attention weight and accurately extract long-distance dependency features, a graph isomorphic network in the other branch is subjected to multiple neighborhood aggregation operations, short-distance dependency features are captured, and the short-distance dependency features are extracted. According to the method, multi-scale dependence of the brain network is extracted in parallel through a double-branch structure, information redundancy is reduced by means of a topology perception attention mechanism, and the adaptive ability of the model to the heterogeneous brain network is improved by using the adaptive fusion module, so that multi-scale dependence of the heterogeneous brain network is balanced in a self-adaptive manner. The classification accuracy is remarkably superior to that of an existing mainstream method, objective and efficient technical support is provided for autism diagnosis, and high interpretability is achieved.

The invention discloses an infantile autism diagnosis method based on brain function correlation structure modeling and a default mode network, which comprises the following steps of: firstly, preprocessing acquired infantile autism data, and extracting average time sequence data from the acquired infantile autism data to construct a whole brain function connection matrix and a DMN (default mode network) function connection matrix; a fusion weight parameter is initialized and is used for fusing the two constructed matrixes; constructing a cross-dimension dual adaptive attention module to extract features from a time dimension and a space dimension; a product-based embedded graph generation module is defined to generate a graph structure, the nodes correspond to brain regions, and the weights of the edges correspond to the similarity between the nodes; and high-order features are extracted from the generated graph structure by defining a graph convolutional network predictor, and node features are mapped to classification tags for subsequent diagnosis of autism. The method disclosed by the invention has the beneficial effects that the brain function association relationship of the autism patient is disclosed, the interpretability of clinical application is enhanced, and the autism diagnosis accuracy is improved.

The invention relates to the technical field of medical auxiliary diagnosis, and discloses an ASD auxiliary diagnosis method fusing dynamic low-order and high-order dynamic central moment networks, and the method comprises the steps: obtaining resting state functional magnetic resonance imaging data of a subject, and carrying out the preprocessing; constructing a low-order dynamic function connection network and a high-order dynamic function connection network to respectively describe direct connection and connection collaboration of the brain region; extracting multi-order central moment characteristics of the two types of networks to obtain stable statistics; generating a low-high order dynamic central moment network by adopting a puzzle fusion strategy; inputting the fusion features into a visual Transform model for classification, and outputting an auxiliary diagnosis result of the infantile autism spectrum disorder; and the key brain region is identified by analyzing the attention weight of the model so as to enhance the interpretability. According to the method, multi-level dynamic connection information is effectively fused, the influence of time asynchronism is overcome, the method has the advantages of being high in classification accuracy, high in generalization ability and good in biological interpretation, and a reliable solution is provided for ASD auxiliary diagnosis.

The invention discloses an SRCIN1 gene therapy vector for autism treatment and application of the SRCIN1 gene therapy vector, and relates to the field of autism treatment. The vector is a lentiviral vector and comprises an overexpression sequence of an SRCIN1 gene, the overexpression sequence is pSLentiti-CMV-Srcin1-P2A-EGFP-WPRE, and the titer of the overexpression sequence is 2.99 E + 08 TU / ml. The SRCIN1 gene therapy vector is delivered to the prefrontal cortex through brain stereotactic injection and is used for up-regulating SRCIN1 expression and improving social behaviors, cognitive functions and anxiety-like behaviors of autism model mice. The SRCIN1 gene therapy vector further comprises an EGFP (enhanced green fluorescent protein) reporter gene which is used for visualizing transfection efficiency. The vector disclosed by the invention is simple to prepare and high in targeting property, and can be used for remarkably improving the social ability and exploration ability of mice, improving infantile autism behaviors such as anxiety behaviors and repeated engraving behaviors of the mice and increasing PSD95 protein expression.

Provided herein are 19-nor C3, 3-disubstituted steroids of Formula (I); and pharmaceutically acceptable salts thereof; wherein R1, R2, R3, and R4 are as defined herein, and A is a heteroaryl ring system comprising 3 or 4 nitrogens 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.

This invention relates to the field of pharmaceutical technology, and discovers that the natural products boldinine and norboldinine can be used as NTSR1 (Neurotensin receptor-1, NTSR1) receptor antagonists and their applications. The compounds are boldinine and norboldinine ((+)-Laurolitsine), which have NTSR1 receptor antagonistic activity and are NTSR1 receptor antagonists that can prevent and treat alcohol poisoning, addiction, schizophrenia, autism spectrum disorder, and mood disorders. Accordingly, this invention can provide novel NTSR1 receptor antagonist prospective compounds with clear targets for the above-mentioned related diseases.

The application discloses an ADHD multi-feature extraction and fusion classification method based on original videos, and comprises the following steps: S1, a network camera is used to collect video records of a subject watching videos, the videos are preprocessed, and preprocessed image frames are obtained; S2, the preprocessed image frames are analyzed to obtain behavior modes including facial actions, eye movements and head movements; S3, feature components of the behavior modes are extracted and fused; and S4, a deep learning network is constructed to classify the fused features. The application further discloses an ADHD multi-feature extraction and fusion classification system based on original videos. The application classifies ADHD patients based on video sequences, avoids invasive influence, reduces cost and is easy to popularize. Through multi-modal feature fusion, the limitation of single modal data is reduced, better accuracy and effectiveness are achieved, and in addition, the application can also be used for classifying autism cases.

This invention provides compounds for the treatment, prevention, or suppression of various pathological conditions (such as proliferative disorders like cancer, β-globin disorders, fibrosis, pain, neurodegenerative diseases, Prader-Willi syndrome, malaria, viral infections, myopathy, and autism) by inhibiting G9a. [Solution] The following general formula (I) TIFF2026062914000401.tif2449 A compound represented by or a pharmacoposly acceptable salt thereof is provided.

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.

A method for early diagnosis of autism spectrum disorder (ASD) including determining an ASD risk index, determining an ASD brain index that includes administering at least one auditory brainstem response (ABR) test to an ear canal of a patient, producing a plurality of biomarkers and identifying at least one biomarker of the plurality of biomarkers as a potential ASD biomarker, and determining an ASD likelihood responsive to each of the ASD risk index and ASD brain index.

Embodiments of the instant disclosure relate to intervention and / or treatment of autism spectrum disorder (ASD). Certain embodiments relate to treating a subject having ASD with one or more proinflammatory cytokines. In some embodiments, the one or more proinflammatory cytokines include, but are not limited to, granulocyte-macrophage colony-stimulating factor (GM-CSF) or derivative thereof, or fusion polypeptide thereof, or recombinant thereof. Other embodiments include a mouse model for assessing efficacy of treatment of ASD in humans and other mammals.

The invention relates to a generalization training content generation method and device for autism patients, computer equipment and a storage medium. The method comprises the following steps: acquiring personalized feature data of an autism patient, and acquiring training target data, generalization baseline data and a generalization stage of generalization training of the autism patient; generating initial generalization training content of the autism patient according to the training target data, the generalization baseline data, the generalization stage and a pre-trained large language model; and performing feature fusion on the training roles of the initial generalization training content according to the personalized feature data to obtain standard generalization training content of the autism patient. By adopting the method, the training content can be enriched and the individuation degree can be improved.

1. The name of the design product: eye movement cognitive and attention assessment training instrument. 2. The use of the design product: for attention deficit groups such as hyperactivity and autism. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

To provide a more effective medicine for treating ASD.SOLUTION: A therapeutic agent for autism spectrum disorder, comprising an oxytocin receptor agonist as an active ingredient, wherein the therapeutic agent is administered in combination with a vasopressin 1a receptor antagonist. A therapeutic agent for autism spectrum disorder, comprising an oxytocin receptor agonist as an active ingredient, wherein the therapeutic agent is administered in combination with a vasopressin 30IU receptor antagonist. A therapeutic agent for autism spectrum disorder, wherein the oxytocin-receptor agonist is not agonistic to the vasopressin 1a receptor; a pharmaceutical composition for the treatment of autism spectrum disorder, wherein the pharmaceutical composition comprises an oxytocin-receptor agonist and a vasopressin 1a receptor antagonist as active ingredients; a pharmaceutical composition for the treatment of autism spectrum disorder, wherein the oxytocin-receptor agonist is not agonistic to the vasopressin 30IU receptor; and a pharmaceutical composition for the treatment of autism spectrum disorder, wherein the vasopressin-receptor antagonist is SR49059. 1a.SELECTED DRAWING: None

In some embodiments, a composition comprising Bifidobacterium bifidum, Bifidobacterium longum, Lactobacillus plantarum, and Streptococcus thermophilus is provided. In some embodiments, provided is the use of a composition of any of the examples herein for preventing or alleviating anxiety and / or sensory hypersensitivity, preventing or treating autism spectrum disorder (ASD), anxiety disorder and / or gastrointestinal (GI) disorder or condition, enhancing attention and / or concentration, or stimulating a GABA receptor in a subject. In some embodiments, methods and kits are provided for preventing or alleviating anxiety and / or sensory hypersensitivity, preventing or treating autism spectrum disorders, anxiety disorders, and / or gastrointestinal disorders or conditions, enhancing attention and / or concentration, or stimulating GABA receptors in a subject. In certain embodiments, the compositions are safe to use and effective to prevent or alleviate anxiety and / or sensory hypersensitivity in subjects, particularly children subjects.

The application discloses an autism diagnosis method based on ARLSTM-GCNs, relates to a medical pattern recognition diagnosis method, and discloses an autism spectrum disorder diagnosis method based on improved AR-LSTM and fusion attention graph convolution network, namely, ARLSTM-GCNs, which comprises the following steps: collecting fMRI imaging is divided into a plurality of ROIs through AAL, and BOLD sequence groups of all ROIs are extracted; linear normalization processing is performed on a time-frequency graph generated by wavelet transform of the BOLD sequence, and then two-dimensional sequence data obtained is used to estimate FCNs between each sample ROI by using Pearson correlation; the processed data set is divided into a training set, a verification set and a test set; an ARLSTM-GCNs diagnosis model is built, including an improved AR-LSTM embedding model and a GCN-SAGPool model; and based on the built ARLSTM-GCNs model, a staged training task is performed. The application realizes relatively accurate prediction of autism.

The present disclosure provides methods of treating Autism Spectrum Disorder. More particularly, the present disclosure relates to methods of using cord blood tissue-derived mesenchymal stromal cells (hCT-MSCs) to treat Autism Spectrum Disorder. The methods involve treating a patient having or suspected of having an Autism Spectrum Disorder by administering a therapeutically effective amount of hCT-MSCs.

The present invention relates to a system for early screening of autism spectrum disorder using eye tracking. According to the present invention, autism spectrum disorder may be diagnosed early by applying response data of a user's gaze, which reacts to images or videos outputted on a display, to an autism spectrum disorder screening model. In addition, the present invention may be used to easily check a degree of suspicion of autism spectrum disorder at home, and thus may be utilized in the fields of early diagnosis, prevention, and treatment of autism in children.

The present invention relates to a composition for treating or preventing neurodevelopmental disorders, particularly autism spectrum disorder, by selectively activating brain-resident regulatory T cells (Tregs). The present composition comprises IL-2 or an IL-2 gene (AAV-GFAP-IL-2) as an active ingredient, and induces Treg proliferation in the brain to alleviate neuroinflammation and improve behavioral defects.

A method of modulating a brain activity of a mammal is achieved by subjecting the mammal to transcranial magnetic stimulation (TMS) with a TMS apparatus at random variable pulse intervals for a time sufficient to modulate said brain activity. The method can also be used by administering electric stimulation to the brain. Improvement in a physiological condition or a clinical condition is achieved. Conditions to be treated include but are not limited to PTSD, autism spectrum disorder addiction (SUD) and Alzheimer's disease.

The application discloses an interactive autistic child training system, method, electronic device and readable storage medium. The interactive autistic child training system comprises a training configuration module, a TOF radar, a multi-modal sensor fusion camera group, a gesture exercise recognition module, an entity interaction behavior recognition module and a prompt module. The training configuration module is used for configuring training actions / training elements corresponding to autistic life self-care skills. The TOF radar is used for collecting gesture exercise data of palms and knuckles. The gesture exercise recognition module is used for judging the gesture exercise data. The multi-modal sensor fusion camera group is used for collecting entity interaction behavior data of a trainee. The entity interaction behavior recognition module is used for judging the entity interaction behavior data. The prompt module is used for outputting an object according to a personalized ability model of the trainee. The application is used to improve the scientificity and systematicness of training.

Embodiments of the methods of the disclosure and compositions related to managing the microbiota in an individual having at least one social behavior deficit. In particular embodiments, an individual has at least one social behavior deficit and was born to a mother who was obese, overweight, or consumed a high-fat diet during pregnancy or carries mutations associated with neurodevelopmental behaviors. In particular embodiments, an effective amount of Lactobacillus reuteri is provided to the individual having at least one social behavior deficit to improve at least one symptom of a social behavior deficit.

The present invention relates to the treatment of an executive function disorder, such as attention deficit hyperactivity disorder (ADHD) and / or autism with both lacosamide and oxcarbazepine.

An apparatus for diagnosing an autism spectrum disorder (ASD) based on a graph neural network includes a preprocessor configured to acquire brain image data, designate a region of interest (ROI) of the brain, and generate preprocessed data for neural network input, a spatial feature extractor configured to extract spatial features from the preprocessed data, a temporal feature extractor configured to analyze changes in brain activity over time and extract attention-based temporal features, a spatial and temporal convergence feature unit configured to analyze spatiotemporal correlations by combining the spatial features and the temporal features, a graph generator configured to convert connectivity between regions of interest into a graph structure and implement the same as nodes and edges, and a graph classifier configured to analyze a spatiotemporal pattern of the connectivity through the graph structure and classify whether or not there is an ASD.

Provided herein are 19-nor C3, 3-disubstituted steroids of Formula (I); and pharmaceutically acceptable salts thereof; wherein R1, R2, R3, and R4 are as defined herein, and A is a heteroaryl ring system comprising 3 or 4 nitrogens 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.

Described herein are methods for diagnosis, screening, and treatment of autism spectrum disorder. Specifically, the invention describes methods of treating autism using dexamethasone to inhibit extracellular vesicle (EV)-mediated IFN-gamma secretion and chronic inflammation. The invention further describes methods of identifying autism in a patient by measuring EV-mediated IFN-gamma secretion in patient samples.

This invention provides a method for addressing one or more behavioral symptoms of autism spectrum disorder (ASD) in a given subject. [Solution] A method of treatment is provided in which an effective amount of cannabidiol (CBD) is administered transdermally to the target.

The invention discloses an infantile autism spectrum disorder detection method based on a sequence transfer diagram, which comprises the following steps: firstly, carrying out channel selection on time sequence data extracted from an fMRI signal, then coding the time sequence data into MTF image data, and locking time and space modes in the data at the same time, thereby breaking through the limitation of traditional static analysis. Secondly, inputting the generated MTF image data into a parallel convolutional neural network for learning, and automatically learning difference features between different ROIs by using the strong feature extraction and mode recognition capabilities of the MTF image data; and finally, inputting all the features into a long and short term memory module with an attention mechanism to model a nonlinear interaction relationship among different ROIs, thereby constructing a high-precision classification model. According to the method, the fMRI data is coded into the MTF image, so that the problem that complex time features are ignored and the problem of linear hypothesis limitation during data processing in the existing method are effectively solved.