13 results about "Mental illness" patented technology

Administration of pentadecanoylcarnitine or pentadecanoic acid is provided for prevention, management or treatment of aggression, allergies, allergic rhinitis, Alzheimer's disease, anxiety and anxiety disorders, amyotrophic lateral sclerosis, arthritis, asthma, atherosclerosis, attention-deficit hyperactivity disorder, bipoloar disorder, brain damage, cancer, cardiovascular disease, cholestatic pruritis, depression, chronic obstructive pulmonary disease (COPD), cocaine abuse, cough, dermatitis, depression, drug-seeking behavior, gastrointestinal disorders, facial erythema associated with rosacea, glaucoma, hepatic diseases, hyperactive bladder, hypersensitivity disorders, hypertension, impulsivity, inflammation, mental disorders and conditions, metabolic disorders, migraines, nasal and sinus congestion, nausea, neuropathic pain with and symptoms of multiple sclerosis, neurological and neuropsychiatric disorders, obesity, obsessive-compulsive disorders, opioid-induced respiratory depression, osteoarthritis, pain, Parkinson disease, pathological gambling, peptic ulcers, schizophrenia, sleep disorders, spinal cord injury, tardive dyskinesia, tics and behavioral problems with Tourette's syndrome; as well as for supporting appetite, cardiovascular health, hematologic health, memory, metabolic health, mood, prolonged REM sleep, renal health, sexuality, sociability and metabolic, hematological, renal, and weight loss.

Methods of using a pharmaceutical composition comprising ebastine and alprazolam for treating patients suffering from one or more psychiatric disorders, such as major depressive disorder, generalized anxiety disorder, mild chronic depression, premenstrual dysphoric disorder are disclosed.

Provided herein include compounds, compositions, kits and methods for preventing and treating psychiatric disorders. The composition and methods described herein can prophylactically treat a subject prior to and / or after a stressor and / or prior to the recurrence of a stressor, thereby preventing or delaying a relapse of depression or depression-like and / or stress-induced behaviors and disorders. The composition can comprise a serotonin 4 receptor (S-HUR) agonist and an N- methyl-D-aspartate (NMDAR) antagonist and a pharmaceutically acceptable salt, analog, derivative or metabolite thereof.

The present disclosure relates generally to compounds, their methods of synthesis, and their use in the treatment of mental illness or central nervous system disorders.

Methods and compositions are disclosed for rapidly relieving symptoms in subjects with depressive, stressor and substance abuse conditions, e.g., major depression, treatment-resistant depression, post-partum depression, bipolar depression, post-traumatic stress disorder, substance use disorders, negative and cognitive symptoms of schizophrenia, as well as for slowing progression of mild cognitive impairment and dementia, using an intermittent dosing regimen of memantine or amantadine or a structural analogue or pharmaceutically acceptable salt of memantine or amantadine (optionally no more frequently than every 2 days) at a dose sufficient to activate Set A brain structures, in combination with a second drug at a dose which activates Set B brain structures but which does not appreciably evoke brain activity linked to hallucinogenic and other psychotomimetic perceptions. In some instances the second drug is either 1) a low dose psilocybin or low dose of other psychedelic and stimulant molecules capable of activating Set B of brain structures which is administered at a dose low enough such that it does not appreciably evoke brain activity linked to hallucinogenic and other psychotomimetic perceptions, or is 2) mianserin (S isomer, R isomer, or a racemate comprising both isomers or structural analog or pharmaceutically acceptable salt thereof) or another standard of care antidepressant capable of activating Set B brain structures, or 3) lumateperone or another antipsychotic capable of activating Set B brain structures.

The application discloses a virus marker for depression diagnosis and a screening method and application thereof, relates to the technical field of biological detection, and the virus marker for depression diagnosis comprises at least one of s_Stenotrophomonas_virus_Pokken, g_Pokkenvirus, s_Dickeya_virus_AD1 and g_Alexandravirus. The application firstly takes the enterovirus group characteristics as the biomarker of depression, breaks through the limitation that traditional mental illness diagnosis relies on subjective scale evaluation; compared with the prior art, the four virus markers can be used for the diagnosis of clinical depression alone or in combination, have the characteristics of non-invasiveness and high sensitivity, can effectively distinguish the depression patients from the healthy people, can early discover the high-risk groups, and provide a new objective diagnosis method for the precise typing of depression.

1. Name of the product in this design: Transcranial Direct Current Stimulation Therapy Device (TDCS). 2. Purpose of this design: For adjunctive treatment of mental illness and neurological rehabilitation. 3. The key design feature of this product is its shape. 4. Images or photos that best illustrate the design key points: Use a combination of state diagrams.

This invention discloses a method for identifying neuropsychiatric disorders based on resting-state functional magnetic resonance imaging (fMRI). The method includes the following steps: acquiring an fMRI training dataset; selecting a set of data from the fMRI training dataset as the first data; preprocessing the first data to obtain preprocessed fMRI data; inputting the preprocessed fMRI data into a random inactivation training model to obtain fMRI features and fMRI features for each viewpoint; inputting the fMRI features into a neuropsychiatric disorder identification model to obtain predicted neuropsychiatric disorder results; calculating the total loss value and optimizing the random inactivation training model and the neuropsychiatric disorder identification model; selecting another set of data and repeating the process until preset conditions are met; obtaining a trained neuropsychiatric disorder identification model; acquiring fMRI data to be identified; preprocessing the fMRI data to be identified to obtain preprocessed fMRI data to be identified; inputting the preprocessed fMRI data to be identified into the neuropsychiatric disorder identification model to obtain the final neuropsychiatric disorder result. This invention features high identification accuracy and strong stability of the identification results.

This invention discloses a method and system for cross-site neuroimaging-based mental illness identification based on multimodal fusion. The method includes: acquiring and preprocessing multimodal neuroimaging data to obtain a constructed multimodal neuroimaging dataset; constructing a mental illness identification model by inputting the multimodal neuroimaging dataset into the model to obtain site prediction results and disease prediction results; calculating site classification loss and disease classification loss based on the site prediction results and disease prediction results, and establishing an objective function; training the mental illness identification model based on the objective function and the multimodal neuroimaging dataset until a preset training cutoff condition is met, and outputting the trained mental illness identification model; and using the trained model for auxiliary diagnosis of mental illness in new subjects. This invention effectively overcomes the interference of heterogeneity in multi-center data and achieves high-precision, high-generalization objective diagnosis of mental illness.

The application provides a mental illness text analysis cross-time and space alignment method based on atlas embedding, and belongs to the field of text processing. Through dynamic atlas representation and consistency knowledge cluster embedding, the application guides large models to accurately learn medical mechanisms, significantly improves the ability of the models to analyze mental illness text information, realizes cross-time and space alignment of medical large models, and has high reliability, sustainability and low artificial dependence characteristics. The application is particularly suitable for knowledge updating of a sudden medical large model, and realizes effective and reliable cross-time and space text analysis of mental illness by the medical large model.

This application relates to the field of detection and treatment technology for neurological and mental illnesses. This application discloses a transcranial magnetic stimulation (TMS) medical system guided by multimodal physiological signals. It includes: a host computer software module, a navigation robot, a TMS device, a stimulation coil, a stimulation pulse conversion module, a multimodal physiological signal acquisition terminal, and a physiological probe connected to the multimodal physiological signal acquisition terminal. The host computer software module includes: an examination item unit, a data processing unit, a protocol management unit, a synchronization control unit, and an evaluation system. The technical solution of this application effectively solves the technical problems of poor synchronization between traditional TMS devices and the body's physiological signal detection, inaccurate target marking, fragmented functions, and a single evaluation dimension through hardware and software linkage control, synchronous acquisition of multimodal physiological signals and stimulation pulse signals, automatic target marking, and highly integrated software management. It also provides convenience for efficacy evaluation.

The invention provides a method and a device for providing an evaluation score of a subject in each single mental disease dimension based on FNIRS. The method comprises the following steps: at least based on acquired near-infrared data of a target brain region of a subject, an evaluation score of the subject in the dimension of each single mental disease is determined by using each single mental disease analysis model through a processor, each obtained evaluation score represents the tendency of the subject to suffer from the corresponding single mental disease, and each evaluation score represents the tendency of the subject to suffer from the corresponding single mental disease. Wherein each single mental disease analysis model is configured to distinguish the health condition from the corresponding single mental disease; and comparing and presenting the evaluation score of the subject suffering from each single mental disease. Therefore, the evaluation score of each single mental disease can be provided, and the mental health condition of the subject can be truly and comprehensively represented from multiple dimensions.

This invention discloses a method for detecting mental illnesses based on the MF-TSK fuzzy system, belonging to the field of EEG signal processing and intelligent detection technology. The method first segments multi-channel EEG signals, extracting multi-dimensional features in the time, frequency, and spatial domains. In the antecedent part of the fuzzy system, improved fuzzy C-means clustering (FCM) is used, combined with Mahalanobis distance, to cluster features and obtain stable and semantically clear fuzzy rule antecedent parameters. In the consequent part, a Gaussian mixture model (GMM) is used to probabilistically model the fuzzy rules, achieving the parsing of rule parameters. In the multi-dimensional feature fusion stage, a weighted optimization mechanism is constructed, integrating classification error constraints, output consistency constraints, and Shannon entropy weight constraints, to achieve adaptive fusion of multi-dimensional features. Through the above technical solution, high-precision and interpretable automatic detection of mental illnesses is achieved.