380 results about "Depressed - symptom" patented technology

This disclosure relates to administration of a combination of: 1) about 100-110 mg, about 104-106 mg, or about 105 mg of bupropion hydrochloride, or a molar equivalent amount of a free base form or another salt form of bupropion; and 2) about 40-50 mg, about 44-46 mg, or about 45 mg of dextromethorphan hydrobromide, or a molar equivalent amount of a free base form or another salt form of dextromethorphan in certain patient populations, such as patients having moderate renal impairment, patients having mild or moderate hepatic impairment, patients receiving a concomitant strong CYP2D6 inhibitor, patients who are known CYP2D6 poor metabolizers, those in need of an NMDA antagonist that does not cause dissociation, and those at risk of QT prolongation.

The disclosure provides methods for treating treatment-resistant depression a subject in need thereof comprising administering to the subject two or more doses of psilocybin. The methods described herein may be used to treat subjects both responsive and unresponsive to a first dose of psilocybin

This disclosure relates to a method of treating depression and / or reducing risk of suicide, comprising administering a combination of about 90 mg to about 120 mg of bupropion hydrochloride, or a molar equivalent amount of another form of bupropion, and about 40 mg to about 50 mg of dextromethorphan hydrobromide, or a molar equivalent amount of another form of dextromethorphan. The combination may be administered twice a day to a human being suffering from major depressive disorder and having a score of 3 or greater on the Suicidality Item of the Montgomery-Åsberg Depression Rating Scale (MADRS-SI).

Provided herein are, inter alia, methods for identifying subjects suffering from depression that will respond to treatment with an antidepressant.

The invention discloses an autophagy biomarker related to depression, a risk assessment model and application of the autophagy biomarker. A group of autophagy related biomarkers for auxiliary diagnosis of depression are screened out through multi-dimensional screening of large sample data and a machine learning algorithm, the autophagy related biomarkers comprise the following genes: GABARAPL2, RB1CC1, FOS and ULK1, and differential expression of the autophagy related biomarkers is proved in chronic stress induced depression-like mouse cortical brain tissues. The invention also provides a risk assessment model for auxiliary diagnosis of depression, a logistic regression model is constructed by taking the biomarker as a feature, a score formula and a risk boundary value of depression risk assessment are determined, and multi-aspect assessment is carried out on the prediction performance of the model. The prediction model has good prediction accuracy and calibration degree, can efficiently and conveniently realize early diagnosis of depression, and improves the accuracy of depression treatment.

The invention discloses lactobacillus reuteri CCFM1288 for relieving depressive mood and application of the lactobacillus reuteri CCFM1288, and belongs to the technical field of microorganisms. The lactobacillus reuteri CCFM1288 has the tryptophan metabolism capability, and can survive for 7 days in the intestinal tract; after the lactobacillus reuteri CCFM1288 intervenes a depressive mouse, anxiety-like and depression-like behaviors of the mouse can be obviously relieved, flora alpha-diversity is improved, intestinal symbiotic bacteria are promoted to secrete indole substances, intestinal homeostasis is maintained, hypothalamic-pituitary-adrenal axis hyperfunction is relieved, dopamine and serotonin neurotransmitter nervous system functions are improved, and the effect of preventing and treating depression is achieved. The neurotransmitter release is promoted, and the anti-depression effect is achieved. In clinical tests, intervention of Lactobacillus reuteri CCFM1288 can significantly improve the depressive symptom of MDD patients, and a new safe alleviation approach is provided for depression treatment.

The present disclosure provides methods and dosing regimens for the treatment of depression (e.g., treatment resistant depression) with a mucoadhesive composition comprising N, N-dimethyltryptamine (DMT) or a pharmaceutically acceptable salt thereof.

The invention discloses a depression state detection system based on multiple modes, which belongs to the technical field of psychological counseling and comprises an information acquisition module, a preprocessing module, a feature extraction module, a depression state detection module and a depression state evaluation module. According to the method, questionnaire evaluation, semantic evaluation and mood emotion are combined, depression state evaluation of multi-modal information of the user is achieved, the depression state self-test threshold is lowered, and doctors can conveniently and comprehensively know the depression state of the patient. The depression self-evaluation scale is combined with the voice information under the stimulation task, so that the system can provide more accurate evaluation for the depression state of the user, and the accuracy and convenience of traditional depression state self-test are improved. The user self-detection design of the system improves the instantaneity and individuation of depression state detection, and the output depression state evaluation score can be used as an important basis for assisting a doctor in diagnosis and mastering the depression state change of a patient in real time.

A method of treating an orexin-2 mediated disorder in a human subject in need thereof is provided. The method comprises administering seltorexant or a pharmaceutically acceptable salt or hydrate thereof at a therapeutically effective daily dose to the subject, when the subject does not have a hepatic impairment. The method also comprises administering seltorexant or the pharmaceutically acceptable salt or hydrate thereof at a reduced dose less than the daily dose to the subject, when the subject has moderate hepatic impairment.

The invention provides a teenager depression auxiliary diagnosis model training method and system based on multi-modal data, and relates to the technical field of mental health. According to the method, firstly, a VR intelligent interaction scene is constructed, and physiological data of juvenile depression and physiological data of a normal contrast subject in the VR intelligent interaction scene are synchronously collected; data preprocessing and feature extraction are conducted on the electroencephalogram data, the heart rate variability data and the eye movement data, a single-mode depression recognition model is obtained through classifier training, and then a multi-mode depression recognition model and a cross-mode depression recognition model are obtained. The teenager depression auxiliary diagnosis model obtained through machine learning is helpful for early discovery and timely treatment of teenager depression.

The invention provides a depression diagnosis marker which is selected from one or more of immune cell Trem1 classical mononuclear cells, B cells, CD36 mononuclear cells, HLADR mononuclear cells, Th17, Treg, cell factors CXCL14, VEGFR1IL-1, RII, RANK, XEDAR, GITR, CRTAM, IL-11, IGFBP-2 and Fetuin A. The depression diagnosis marker has high MDD curative effect prediction efficiency. The invention further discloses application of the depression diagnosis marker in preparation of products for diagnosing depression, and the depression diagnosis marker has high MDD diagnosis value.

The invention relates to the technical field of artificial intelligence, and discloses an interpretable depression detection method and system, and the method comprises the steps: retrieving a post related to a depressive symptom from a user social media history; on the basis of the post text corresponding to the symptom problem, a problem perception text representation is generated through fusion of a Longform encoder and an attention mechanism, and an optimal reasoning strategy is selected from a predefined reasoning strategy pool through a hybrid expert network; based on an inference strategy of question perception text representation and selection, selecting a most adaptive large language model from a predefined large language model pool through a hybrid expert network; deducing the severity of each depression symptom of the user according to an inference strategy and a large language model, and calculating the overall depression symptom severity of the user; according to the method, the reasoning strategy and the large language model are dynamically selected through the hybrid expert network, the optimal processing combination is adaptively matched for different symptom features and post contents, and the limitation of a fixed reasoning mode is overcome.

The invention provides an application of a pediococcus pentosaceus strain A20001 in preparation of a product for improving memory concentration and anxiety and depression behaviors, and belongs to the technical field of biological medicines. Animal experiments show that the pediococcus pentosaceus strain A20001 has the functions of resisting anxiety behavior, resisting depression, improving cognitive function and improving memory concentration. Therefore, the invention provides the application of the pediococcus pentosaceus strain A20001 in the preparation of the medicine for preventing and / or treating the anxiety disorder, the depression or the cognitive impairment and improving the memory, and further provides the application of the pediococcus pentosaceus strain A20001 in the preparation of the medicine for preventing and / or treating the anxiety disorder, the depression or the cognitive impairment.

The invention provides an intelligent diagnosis and treatment method and system for teenager depression based on large model collaboration, and the method comprises the steps: constructing a remote sensing human living microenvironment and growth soft environment data set, carrying out the preprocessing and feature extraction of the data set, carrying out the space-time matching and characterization of the environment and the brain-behavior clinical data of a subject individual, and carrying out the diagnosis and treatment of the teenager depression. Individual intervention queue data and feature representation matched with the environment are collected; establishing a knowledge graph based on a large model based on the acquired multi-dimensional data; and performing intelligent diagnosis and intervention of environment-brain-behavior enhancement based on the established knowledge graph. Based on a knowledge organization system of environment-brain-behavior auxiliary diagnosis, pathogenesis evaluation and individualized intervention, an environment-brain-behavior multi-dimensional knowledge graph oriented to adolescent depression diagnosis is constructed, and based on a large-model collaborative reasoning mechanism enhanced by environment-brain-behavior multi-dimensional knowledge, the adolescent depression diagnosis is more accurate. An intelligent and precise method system is provided for diagnosis and treatment of juvenile depression, and the diagnosis and treatment level is greatly improved.

The invention provides an application of a spleen T lymphocyte exosome in construction of a depression animal model, a stress rat spleen T lymphocyte exosome is injected into a mouse body to construct the depression animal model, and the depression animal model can be used for depression drug screening, drug efficacy evaluation or diagnosis, and can be used for treating depression. And the method can also be used for researching and revealing pathogenesis of depression. The invention also proves that the IL4 / IL4R alpha signal channel and the BDNF signal channel play an important role in depression through experiments. The invention has important significance for clinical research of depression.

The present disclosure relates to compositions comprising racemic ketamine, or a pharmaceutically acceptable salt thereof, for use in treating psychiatric disorders such as suicidality, suicidal ideation, major depressive disorder, treatment-resistant depression, and post-traumatic stress disorder.

Provided herein are, inter alia, methods for identifying subjects suffering from depression that will respond to treatment with an antidepressant.

The invention discloses a method for identifying somatic depression by using brain structure and function abnormality, which comprises the following specific steps: selecting depression patients into groups, dividing the depression patients into a somatic depression patient group and a non-somatic depression patient group according to whether the score of a health questionnaire somatic symptom group scale of the patients is greater than or equal to 10, and recruiting a health control group at the same time. The three groups of subjects are subjected to resting state structure and functional magnetic resonance imaging scanning, and the brain grey matter volume and the brain region with significant difference are analyzed through a voxel-based morphological index and functional connection method. Pearson correlation analysis is adopted in SPSS26.0, and the relevance between the gray matter volume and the abnormal function connection of the patients in the SD group and the NSD group and the PHQ-15 scale score is compared. The method has the advantages that the illness state of the depression patient can be found in time, the patient can be treated at the best treatment opportunity, and early recognition of the body symptoms of the depression is beneficial for reducing waste of medical resources and relieving the pain of the patient.

The invention provides an emotion perception model construction and early depression risk detection method, and solves the problem that an existing method neglects emotion content and early depression symptom description expressed by a user in social media and is difficult to accurately judge whether the user is in the early stage of depression. Obtaining a social media dynamic data set of a to-be-detected user and obtaining a depression early description post set containing at least one piece of content of the to-be-detected user; constructing the post presentation layer, and capturing a context presentation sequence fused with negative emotion knowledge by using a pre-training language model; constructing the user presentation layer, and obtaining a user presentation matrix fusing heterogeneous knowledge based on the output of the post presentation layer; predicting the early depression risk level of the to-be-detected user through the detection layer; the emotion perception model is obtained through training, and the early depression user can be effectively detected and evaluated through the model.

The invention belongs to the technical field of biological medicines, and discloses application of a ULK1 agonist in preparation of an anti-depression medicine and application of ULK1 in preparation of a depression diagnosis product. Experiments show that ULK1 not only can be used as a biomarker for diagnosing depression, but also can be used as an important target for developing drugs for treating / preventing depression. In addition, the invention further indicates that the ULK1 agonist BL-918 has a rapid anti-depression effect and can be used for treating / preventing depression.

The invention discloses a composition for treating depression and application of the composition. The composition for treating depression is prepared from the following components in parts by weight: 35 to 40 parts of ligustilide, 0.5 to 1.5 parts of atractydin and 0.1 to 1 part of alpha-cyperone. According to the invention, the active components in the Yueju pill, which are targeted to 5-HT1AR and 5-HTT, are compounded for the first time to prepare a new traditional Chinese medicine functional component composition, and in addition, the anti-depression effect of the active components is evaluated by establishing a reserpine mouse depression model, so that the anti-depression effect of the Yueju pill is improved. The result shows that the active component composition screened by the 5-HT1AR and 5-HTT targets can adjust the content of monoamine neurotransmitters in brain tissues in vivo to achieve the anti-depression effect, and in-vivo pharmacological experiments fully prove that the composition has the anti-depression effect.

The invention belongs to the technical field of intelligent diagnosis of mental diseases, and particularly relates to a major depression diagnosis system. The system comprises an input module used for inputting indexes of a patient; wherein the indexes comprise at least one of the following information: a neurocognitive test, child trauma information, multi-modal neuroiconomics information and transcriptomics information; the prediction module is integrated with a machine learning model and is used for obtaining a prediction result about whether the patient suffers from the major depression or not according to the indexes; and the output module is used for outputting the prediction result. The system provided by the invention can accurately diagnose the major depression, can effectively assist doctors in diagnosing and screening the major depression, reduces the workload of workers, and has a very good application prospect.

The invention belongs to the technical field of medical application, and particularly relates to application of neomangiferin in preparation of an anti-depression drug, experiments find that the effect of neomangiferin is obviously better than that of mangiferin, although the neomangiferin and the mangiferin have the same main structure, the inventor finds that the effects of the neomangiferin and the mangiferin have obvious difference, and the effect of the neomangiferin and the mangiferin is obviously different. The compound is unexpected by the inventor before experiments, a new optional medicine is provided for research and development of anti-depression, and the effect of the anti-depression medicine is effectively improved.

The present invention relates to a pharmaceutical composition for preventing or treating depression comprising a Lactobacillus buchneri 200793 strain, and specifically, to a composition for preventing, improving, or treating depression comprising the strain or a culture of the strain, a food composition for alleviating tension, and an animal feed additive composition for preventing or improving depression. When the composition according to the present invention is administered to a subject, sucrose preference is increased, and the expression level of TPH-1 or SERT in small intestine and large intestine tissue is increased, thereby being highly effective in alleviating depression, and thus the composition can be effectively employed for alleviating and improving various symptoms of depression.

The invention discloses a method for constructing a blood serum endogenous biomarker and a depression diagnosis model of a depression patient, which couples non-targeting and targeting screening technologies, systematically characterizes endogenous small molecule metabolites in blood serum, identifies differential metabolites between the depression patient and a healthy control group, and determines the blood serum endogenous biomarker and the depression diagnosis model of the depression patient. And then determining a biomarker closely related to depression by using a regression model. According to the invention, potential depression biomarkers such as N-methyl-L-alanine, N-acetylneuraminic acid, N-acetylglycine, thymine ribonucleoside, decanoyl carnitine, aspartic acid-phenylalanine and the like are discovered. The diagnosis model obtained by training according to the ratio of N-acetylglycine to thymine ribonucleoside has the highest accuracy (99%), and healthy and disease samples can be effectively distinguished by using a threshold value 2.434. Therefore, the ratio of N-acetylglycine to thymine ribonucleoside is a potential biomarker capable of realizing rapid and accurate diagnosis of depression.

This invention relates to the use of (4-benzylpiperazin-1-yl)-[2-(3-methylbutylamino)pyridin-3-yl]methanone (NSI-189) or a pharmaceutically acceptable salt thereof in the treatment of a psychiatric condition in which depressive symptoms are prominent, including major depressive disorder (MIDD), bipolar disorder, post-traumatic stress disorder, substance use disorder, and depression-related aspects of schizophrenia (e.g. negative symptoms) in select patients who exhibit impaired learning and / or memory. The invention also relates to the use of NSI-189 or a pharmaceutically acceptable salt thereof in the treatment of a psychiatric condition in which depressive symptoms are prominent, including major depressive disorder (MDD), in select patients who exhibit impaired learning and / or memory or certain EEG characteristics.

The invention discloses application of a combination of probiotics GOLDGUT-M520 and GOLDGUT-LR99 in preparation of a product for relieving depression, and belongs to the technical field of biological medicines. The probiotics disclosed by the invention can be used for relieving depressive symptoms, improving related symptoms, assisting in treating depression and preventing depression, and also can be used as a nutritional supplement to help maintain the balance of intestinal microflora and promote intestinal health.

The present invention relates to Compound 1 for the use in the treatment of Major Depressive Disorder, Treatment-resistant Depression, Bipolar Depressive Disorder, Depressive Episodes associated with Bipolar disorders, Obsessive Compulsive Disorder, Post-Traumatic Stress Disorder, and / or Borderline Personality Disorder, chemical synthesis of Compound 1, polymorphs of Compound 1, and pharmaceutical compositions comprising Compound 1 and / or its polymorphs.

The invention provides a depressive disorder detection method and device based on sleep monitoring, and relates to the technical field of depression intelligent detection.The depressive disorder detection method comprises the steps that a polysomnogram of a to-be-detected object is obtained, and the polysomnogram comprises a plurality of physiological signals; inputting the plurality of physiological signals into a first depressive disorder detection model to obtain a first intermediate layer signal, the first depressive disorder detection model comprising an attention module, a CNN sequence, a first channel, and a data splicing module, the attention module is used for acquiring an attention index of each physiological signal, calculating an attention weight of each physiological signal and integrating all the attention weights into a second intermediate layer signal; inputting the first intermediate layer signal into a second depressive disorder detection model to obtain a third intermediate layer signal; and inputting the third intermediate layer signal into a third depressive disorder detection model to obtain a depressive disorder detection result of the to-be-detected object. According to the invention, the accuracy of the depressive disorder detection result is improved.

The invention discloses application of a mitochondrial differentiation expression gene in preparation of a depression diagnostic agent and a depression treatment drug, and relates to the technical field of depression diagnosis and treatment. And the mitochondrial differentiation expression gene comprises one or more of TFAM (Transforming Factor Amplification Molecular), SURF1 and NDUFB9. According to the present invention, the SURF1 gene simultaneously expressed in inhibitory neurons and excitatory neurons is focused on, the key effect of the SURF1 in CUMS-induced depression-like behaviors is found, the CUMS-induced molecular and behavior changes are effectively reversed by the overexpression of the SURF1, and the potential anti-depression effect is displayed; besides, animal experiments find that overexpression of the SURF1 can effectively recover the function of an electron transport chain, ATP generation is enhanced, mitochondrial membrane potential is maintained, and oxidative stress is reduced. The effects jointly relieve depression-like behaviors induced by CUMS, and further support the protection effect of the SURF1 in a depression model, which provides evidence for the new effect of the SURF1 in the pathophysiology of depression.