860 results about "Cognitive impairment" patented technology

The invention discloses a multi-modal cognitive impairment evaluation system based on a multi-dimensional cognitive function hypergraph. The multi-modal cognitive impairment evaluation system comprises a multi-modal signal acquisition system, a cognitive function evaluation normal form design, a universal feature extraction framework of each modal and online hypergraph learning based on a multi-dimensional cognitive function. The system constructs a dimensionality evaluation normal form containing the cognitive function based on multi-modal physiological and behavior signals, so that comprehensive evaluation of the cognitive function of the elderly is realized. Firstly, physiological and behavior responses of a user under different tasks and situations are recorded and analyzed in real time by fusing multi-mode signals such as electroencephalogram, electrocardio, voice and facial expressions. And then, mining fusion representation among the multi-modal signals, generating unified feature representation, introducing an online hypergraph of clinical knowledge, capturing a complex multi-modal data relationship, and providing an analysis result containing cognitive function evaluation. Finally, in combination with the technical means, the system can realize early detection of cognitive impairment.

The invention discloses a screening and evaluating system and method for cognitive impairment of old people, and belongs to the technical field of intelligent medical treatment. According to the system, electroencephalogram, VR / AR interaction, physiology and gait data are collected through the multi-modal acquisition module, the data processing module carries out data processing, dynamically selects a scale and carries out fusion analysis, the man-machine interaction module realizes natural interaction, and the output module generates a visual report and carries out early warning. The core algorithm comprises dynamic scale selection, multi-modal fusion evaluation and the like. The evaluation method comprises the steps of data acquisition, scale selection, data fusion, confidence verification, output and the like. Compared with a traditional method, the method overcomes the defects that subjectivity is high, and static evaluation, data splitting and base layer application are limited. The cognitive impairment assessment method can accurately and dynamically assess cognitive impairment, improves assessment accuracy, realizes full utilization of data, is simple and convenient to operate, is suitable for various scenes, provides efficient and reliable support for diagnosis, monitoring and intervention of cognitive impairment, and promotes development of intelligent medical treatment.

The invention discloses a cognitive impairment assessment system and method based on general artificial intelligence, and the method comprises the following steps: S1, collecting multi-modal data through a wearable device, a mobile application and an online tool; s2, carrying out preprocessing on the multi-modal data; s3, comprehensively analyzing the preprocessed data, evaluating cognitive competence, and identifying a cognitive impaired field; s4, a rehabilitation training plan is generated through a neural evolution algorithm, and the training content, sequence, difficulty and type are dynamically adjusted; s5, providing cognitive training and adjusting tasks in real time through virtual reality and augmented reality technologies; s6, predicting cognitive load and psychological state, and dynamically adjusting the intervention plan; and S7, protecting data privacy by using a block chain technology, monitoring a training effect, and generating a progress report. According to the method, the intervention plan and the privacy protection technology are dynamically adjusted through multi-modal data analysis based on general artificial intelligence, and a personalized and real-time cognitive impairment assessment and intervention scheme is provided.

Aspects of the disclosure relate to methods of reducing fatigue in a subject having myotonic dystrophy type 1 (DM1). Aspects of the disclosure relate to methods of treating one or more symptoms assessable by the MDHI (e.g., a GI symptom, myotonia, upper extremity function impairment, fatigue, mobility impairment, impairment in the ability to perform activities, pain, vision impairment, communication impairment, sleep impairment, emotional issues, cognitive impairment, social satisfaction impairment, social performance impairment, breathing impairment, swallowing impairment, and / or hearing impairment) in a subject having myotonic dystrophy type 1 (DM1). In some embodiments, the methods comprise administering to the subject a composition comprising complexes (e.g., muscle targeting complexes) comprising an oligonucleotide (e.g., a DMPK- targeting oligonucleotide) covalently linked to an antibody (e.g., anti-TfRl antibody).

The invention discloses a post-stroke cognitive impairment prediction method based on multi-modal feature fusion. The method comprises the steps that firstly, multi-modal information of a stroke patient is collected, wherein the multi-modal information comprises a three-dimensional brain MRI image, an EEG electroencephalogram signal and clinical medical record information; secondly, converting MRI into a tensor, and inputting the tensor into a multi-scale spatial-temporal feature extraction backbone network to obtain MRI modal features; the EEG electroencephalogram signals are subjected to electroneurographic signals and are combined with Transform, and EEG modal features are obtained; the clinical medical record information of the stroke patient is converted into semantic sentences, the semantic sentences are input into a two-channel semantic encoder for encoding extraction, and clinical medical record information features are obtained. And finally, inputting the MRI modal features, the EEG modal features and the clinical medical record information features into a three-modal fusion device to obtain fusion features, and outputting probability prediction through a classifier. The post-stroke cognitive impairment prediction method achieves accurate prediction of post-stroke cognitive impairment, and significantly improves robustness and medical interpretation.

The invention provides an application of a fermentation lactobacillus mucus strain A21196 in preparation of a product for resisting anxiety and improving memory and concentration, and belongs to the technical field of biological medicines. Animal experiments show that the fermented lactobacillus mucus strain A21196 has the effects of resisting anxiety, resisting depression, improving cognitive impairment function and improving memory concentration, and provides a new means for preventing and / or treating nerve diseases related to anxiety disorder, depression and cognitive impairment.

The invention relates to the technical field of cognitive impairment detection, and discloses a cognitive impairment early warning method and device based on an electroencephalogram micro state and an eye movement trajectory, and the method comprises the following steps: S1, data acquisition, S2, electroencephalogram preprocessing, S3, electroencephalogram feature extraction, S4, eye movement feature extraction, S5, feature fusion, and S6, early warning judgment. According to the method, through independent convolution branch of electroencephalogram and eye movement features, a receptive field is expanded by utilizing cavity convolution to capture multi-scale features, and long-distance dependence is modeled by a self-attention layer; after tensor splicing, time sequence information is dynamically fused through a gating cycle unit (GRU), and cross-modal time correlation is captured. The method has the advantages that the multi-modal feature hierarchical extraction and self-adaptive modeling capability is enhanced, the complementarity fusion efficiency is optimized, meanwhile, by means of cavity convolution sparse connection, self-attention parameter sharing and GRU lightweight design, the model complexity and the calculation efficiency are balanced, and efficient feature representation is provided for cognitive impairment early warning.

The invention relates to the technical field of artificial intelligence and biomedical sensing, and discloses a cognitive disorder risk identification device and method integrating multi-modal physiological data, and the device comprises an EEG module, an eye movement module, an fNIRS module, an edge calculation module and an acceleration sensor; the EEG module collects EEG data, the eye movement module collects eye movement eye-tracing data, and the fNIRS module collects near infrared spectrum fNIRS data and inputs the data to the edge calculation module; the edge calculation module runs the multi-modal space-time attention fusion model to output a cognitive impairment risk assessment result by using the multi-modal space-time attention fusion model; the acceleration sensor operates based on an operation dynamic filtering algorithm of a motion accelerometer to inhibit signal drift caused by head motion. According to the invention, through the modularized head-mounted multi-mode edge device, the physiological data is collected and edge end processing and cognitive disorder recognition and screening are carried out in combination with the edge calculation module, so that early recognition and screening of neurodegenerative diseases can be conveniently and effectively carried out at low cost.

The invention discloses an early judgment system for cognitive impairment of Parkinson's disease based on transcranial magnetic-electroencephalogram multi-dimensional features. The early judgment system comprises a transcranial magnetic-electroencephalogram combined acquisition module, a synchronous signal preprocessing module, a multi-dimensional feature extraction module, a feature intelligent fusion module and a storage and display module, the transcranial magnetic-electroencephalogram combined acquisition module is used for disturbing the neural activity of a patient through monopulse transcranial magnetic stimulation coding and acquiring an evoked state electroencephalogram signal; the synchronizing signal preprocessing module is used for denoising and filtering the evoked state electroencephalogram signals; a multi-dimensional feature extraction module performs feature extraction on the processed evoked state electroencephalogram signals by utilizing multi-dimensional electroencephalogram feature analysis to obtain multi-parameter electroencephalogram neural activity features; the feature intelligent fusion module is used for carrying out weight distribution and feature fusion on the multi-parameter electroencephalogram neural activity features to obtain cognitive disorder multi-modal neural indexes; and the storage and display module is used for storing the cognitive disorder multi-modal nerve indexes and visualizing the cognitive disorder multi-modal nerve indexes.

The invention provides a cognitive impairment assessment system and method based on emotion interaction multi-mode physiological signals. The cognitive impairment assessment system comprises a multi-mode physiological signal acquisition module, an emotion interaction module based on emotion recognition and a cognitive impairment assessment module. The multi-modal physiological signal acquisition module acquires multi-dimensional physiological signals such as electroencephalogram, electro-oculogram and electrocardio through a flexible sensor, and a physiological signal coupling network is constructed; the emotion interaction module based on emotion recognition adopts a heterogeneous graph neural network to analyze emotion response under visual stimulation, recognizes deviation between emotion and a preset stimulation type through a comparison model, dynamically optimizes visual stimulation parameters, and forms closed-loop emotion interaction; and the cognitive impairment evaluation module combines an SE-CNN deep learning model and fuses multi-modal physiological data and emotion interaction characteristics to realize accurate classification of cognitive impairment. According to the method, the accuracy and individualized efficiency of emotion recognition can be effectively improved, and a new normal form and a new method are provided for early cognitive impairment assessment.

The invention relates to the technical field of medical care informatics, and discloses an intelligent post-stroke cognitive impairment assessment method based on brain network dynamic reconstruction, which comprises the following steps: acquiring multi-modal brain image data of a patient, dynamically compensating the difference between haemodynamic delay and white matter conduction velocity through a nerve-blood vessel coupling model, and calculating the post-stroke cognitive impairment; precise space-time alignment of function and structure signals is realized; a pulse neural network dynamic entropy change model is constructed based on the alignment data, functional connection strength change is coded into a pulse distribution sequence, and Gamma wave band synchronization enhancement and other compensatory features are extracted; constraining rationality of brain network reconstruction by adopting an energy optimal transmission model, and generating a compensatory thermodynamic diagram; and real-time calculation of the edge end is realized through pulse time sequence compression coding. The limitation of traditional static analysis is avoided, the algorithm achieves microsecond response on embedded equipment through the pulse coding technology, and a reliable stroke rehabilitation evaluation tool is provided for primary hospitals.

An exemplary system and method are disclosed that is configured to detect cognitive impairment (e.g., early cognitive impairment) or assess cognitive function by analyzing, via machine learning and artificial intelligence analysis, behavioral metadata collected from a smart app during the course when a subject is using a cognitive test instrument for cognitive tests that incorporate motor activity (e.g., drawing or writing). The machine learning and artificial intelligence analysis can execute features associated with the test taker's metadata (e.g., time spent on task or questions, changing answers, referring back to the previous question), drawing qualities (e.g., line straightness, completeness), among others.

A juvenile depression cognitive impairment subtype classification and prognosis prediction method relates to the technical field of medical treatment, and mainly comprises the following steps: performing clinical evaluation and therapeutic response evaluation on a subject, performing MRI and magnetoencephalogram data acquisition, constructing a whole brain MSN of the subject, identifying MSN abnormal characteristics, obtaining functional connection change of a frequency band when magnetoencephalogram is abnormal, and determining the cognitive impairment subtype classification and prognosis prediction of the cognitive impairment subtype of the subject. A subtype classification model is established by fusing the MSN and cognitive function evaluation data, and a prognosis prediction model is established by analyzing MSN abnormal features, functional connection changes of frequency bands during abnormality, multi-dimensional treatment reactions and high-risk behaviors. According to the method, different levels of fusion measurement are carried out on the juvenile depression with cognitive function impairment brain mechanism through multi-modal brain images, a subtype classification model with diagnosis and treatment values is established, and a prognosis prediction model with clinical transformation potential is constructed; therefore, a theoretical basis and a technical means are provided for individualized precise diagnosis and treatment of the cognitive impairment of the juvenile depression.

The invention provides a cognitive disorder recognition method and device, electronic equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: acquiring multi-modal data of a target; performing feature extraction on the multi-modal data to obtain a demographic feature and a plurality of acoustic features of the target; each acoustic feature corresponds to the category of the voice data; respectively calculating a category score corresponding to each category based on each acoustic feature; the category score is used for indicating the confidence value of the voice data belonging to the corresponding category; and performing cognitive impairment prediction on the target by using a cognitive prediction model according to the demographic characteristics of the target and the category scores to obtain a prediction result. According to the method and the device, the problem of low accuracy of cognitive impairment recognition in related technologies is solved.

The invention relates to the field of preparation and application of biological peptides, in particular to a swelling hippocampal peptide and application of the swelling hippocampal peptide in preparation of a product for improving memory ability and cognitive impairment. According to the invention, a green controllable enzymolysis technology is utilized to obtain protein polypeptide which has the main molecular weight of less than 1000 Da and has nutritional, health-care and medicinal values, and four active peptide sequences with high biological activity are obtained by further research on the basis of the protein polypeptide.

The invention belongs to the field of medical artificial intelligence, and particularly relates to a multi-modal data acquisition and fusion method for Alzheimer's disease. The method comprises the following steps: firstly, synchronously acquiring eye movement, expression, voice, gait and grip strength data of a subject through a virtual reality multi-task normal form, and combining with an MoCA scale to score a result; then preprocessing and feature extraction are carried out on each modal data, and unified feature representation is constructed; on the basis, a cross-modal attention mechanism is adopted to realize interaction and weighted fusion of multi-modal features, and a unified fusion feature vector table is generated; and finally, outputting structured data organized according to task fragments for auxiliary evaluation and modeling of cognitive impairment. The method can effectively solve the problems that in the prior art, single-mode information is insufficient, and multi-mode data are difficult to align and fuse, has the advantages of being low in cost, easy to popularize and high in detection accuracy, and can be widely applied to early recognition and auxiliary diagnosis of the Alzheimer's disease.

The invention discloses a cognitive impairment intervention system and method based on general artificial intelligence, and the method comprises the steps: S1, collecting the physiological, behavior and cognitive state data of a patient, and carrying out the data preprocessing; s2, inputting a dual convolutional neural network to extract spatial-temporal features, and generating comprehensive cognitive state features; s3, time sequence clustering analysis is carried out, the key fluctuation period of the cognitive state is recognized, the decline trend is predicted, and the risk level is adjusted; s4, analyzing the emotional state of the patient in combination with a dynamic emotion recognition algorithm of micro-expression time sequence mapping, and optimizing an intervention scheme based on a user-defined emotion tag; s5, adjusting the intervention scheme by using an incremental learning method according to real-time feedback of the patient; and S6, optimizing the dual convolutional neural network by long-term training data, improving feature extraction and intervention precision, and realizing personalized long-term intelligent intervention. According to the method, multi-modal data and adaptive optimization are fused, accurate detection, personalized intervention and long-term optimization are realized, the rehabilitation efficiency is improved, and the life quality is improved.

The invention discloses an intelligent cognitive assessment method and system based on a virtual reality environment. According to the method, through accurate eye movement calibration and electroencephalogram calibration, resting-state electroencephalogram information, digital cognitive evaluation task parameters and eyeball movement track parameters of a subject in a virtual reality environment are collected, and through professional preprocessing and feature extraction, deep analysis and fusion are conducted on data through a common-attention multi-mode feature noise reduction fusion model. Through training and prediction of the neural network model, the cognitive function state of the subject can be effectively evaluated, the accuracy and efficiency of cognitive evaluation are improved, and meanwhile, the user experience is optimized. By utilizing the method, early recognition of cognitive impairment is facilitated, a scientific basis is provided for clinical diagnosis and intervention, and the method is of great significance in preventing and delaying dementia of old people.

The invention, which relates to the field of the intelligent accompanying system, discloses an interactive digital person accompanying system for the old with cognitive impairment, comprising a data acquisition module, an emotion recognition module, a first matching module, a second matching module, a personalized interaction generation module and a reinforcement learning adaptive module. The data acquisition module acquires multi-modal sensing data of a target old person; the emotion recognition module outputs an emotion label and an intensity value through feature extraction and Transform fusion; a first matching module screens corpus fragments according to emotion intensity triggering conditions; the second matching module performs secondary screening based on the retrieval weight; the personalized interaction generation module generates digital twin interaction data through probability distribution sampling; the reinforcement learning adaptive module dynamically adjusts the corpus weight according to the interaction reward value; according to the method, the dual guarantee of emotion guarding and physiological safety is realized, the anxiety emotion of the old with cognitive impairment is remarkably relieved, and the novelty of interaction and high emotion relieving efficiency can be kept.

The invention relates to the field of traditional Chinese medicine, and discloses a traditional Chinese medicine composition for treating cognitive impairment of Parkinson's disease and application thereof.The traditional Chinese medicine composition is prepared from, by weight, 180-220 parts of vinegar tortoise plastron, 90-110 parts of morinda officinalis, 90-110 parts of angelica sinensis, 90-110 parts of radix curcumae, 55-65 parts of rhizoma acori graminei, 55-65 parts of polygala tenuifolia, 180-220 parts of calcined dragon bone, 180-220 parts of caulis spatholobi and 1-3 parts of borneol. When all the medicines of the traditional Chinese medicine composition are combined, the compatibility of monarch, minister, assistant and guide is performed, so that the traditional Chinese medicine composition has the effects of tonifying kidney, promoting blood circulation, dispersing blood stasis and inducing resuscitation, can relieve cognitive impairments such as space navigation, space exploration, working memory and the like of a Parkinson's disease cognitive impairment model mouse caused by chronic hypoperfusion, and realizes the treatment effect on the Parkinson's disease cognitive impairment; good application prospects are realized.

The invention discloses an active peptide capable of improving cognitive impairment as well as a preparation method and application of the active peptide. The amino acid sequence of the active peptide is shown as SEQ ID NO: 1, 2, 3, 4, 5 or 6. The invention aims to provide a novel active peptide as well as a preparation method and application thereof aiming at the problems of limited means and unstable effect of improving cognitive impairment in the prior art, the active peptide prepared by the invention can improve the problems of learning and memory impairment and cognitive function decline to a certain extent, enriches technical approaches for improving cognitive impairment, and has a wide application prospect. And a new application choice is provided for related fields.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a multi-mode early cognitive impairment screening method and system based on medical history information.The method comprises the steps that a first electrode plate and a second electrode plate are arranged at the forehead position and the occipital position of the brain of a user to be detected in advance; the third electrode plate and the fourth electrode plate are arranged on the left side of the brain side by side, and the fifth electrode plate and the sixth electrode plate are arranged on the right side of the brain side by side. And obtaining a disturbance coefficient set of the to-be-detected user under the reference detection mode combination, inputting the disturbance coefficient set into a pre-trained early cognitive impairment screening model, and predicting to obtain a prediction probability value of the early cognitive impairment of the to-be-detected user, and by adopting the scheme to screen the early cognitive impairment, the accuracy is high, the universality is strong, and the accuracy is high. The method can be applied to screening of people in communities, nursing homes and the like on a large scale.

The invention discloses an intestinal bacterial composition for improving a cognitive function and relieving neuroinflammation and an application of the intestinal bacterial composition, and belongs to the technical field of biomedicine and microbiology, the intestinal bacterial composition is composed of a Lachnospiraceae bacterium, a Bacteroides multiforme and a Roseburiatestinae bacterium, and the intestinal bacterial composition is prepared from the following raw materials of the following raw materials of the intestinal bacterial composition: the Lachnospiraceae bacterium, the Bacteroides multiforme and the Roseburiatestinae bacterium, the Roseburiatestinae bacterium, the Roseburiatestinae bacterium, the Roseburiatestinae bacterium, the Roseburiatestinae bacterium, the Roseburiatestinae bacterium and the like. Experimental research results show that a modular synergistic effect exists among the helicobacter hirsuticus, the bacteroides multiforme and the Roche enterobacter, and the pharmaceutical composition has a remarkable neuroprotective effect on a cognitive impairment mouse model and an Alzheimer disease mouse model, can improve the cognitive function and relieve neuroinflammation, and can be used for preparing a neuroprotective agent for treating the cognitive impairment mouse model and the Alzheimer disease mouse model. A new thought and method can be provided for treatment of cognitive impairment related diseases and neuroinflammation related diseases.

The invention relates to 2 '-fucosyllactose, in particular to application of the 2'-fucosyllactose to preparation of immune balance drugs or food. The immune balance comprises regulation and control of the ratio of Th17 cells to Treg cells of Th17 / Treg immune imbalance offspring activated by maternal immune or induced by virus infection; according to the application disclosed by the invention, the fact that filial generations generated after the maternal body of a mammal generates immune reaction activation can regulate and control the proportion of Th17 and Treg cells through the influence of 2 '-fucosyllactose on intestinal flora is found, so that Th17 and Treg immune balance is realized; in addition, social and cognitive disorders and repetitive behaviors related to filial generation autism induced by Th17 / Treg immune imbalance can be improved.

The invention relates to the technical field of cognitive impairment image processing, in particular to an Alzheimer's disease early screening method which comprises the following steps: extracting time and space coordinates of a fixation point from a visual glancing task participated by an Alzheimer's disease patient, recording direction change of a jumping behavior in the time and space coordinates, and calculating the direction change of the jumping behavior in the time and space coordinates; and generating jump behavior distribution data. According to the method, by extracting the time and space coordinates of the fixation point and combining the direction change of the jump behavior, comprehensive description of the dynamic law of the fixation behavior is realized, and high-dimensional support is provided for fine analysis of the fixation behavior of a patient in the task execution process. By constructing jump event distribution, track dynamic change and complexity quantification of the gazing path, the offset characteristic, the jump rule and the path fractal dimension in the gazing behavior are accurately revealed, and quantitative description of the abnormal behavior is provided for screening.

The application discloses a kind of cognitive impairment early screening and diagnosis system based on multi-modal data, including following module: (1) multi-modal data acquisition module, for obtaining image data, gene data, behavior data and clinical narrative information;The clinical narrative information includes at least two kinds of patient complaint text, interference factor hook item, symptom time axis;(2) causal purification module, for filtering the pseudo-correlation between multi-modal data by clinical narrative anchoring and reverse intervention deduction, including: narrative anchoring unit: based on preset causal conflict rule base, through structured clinical narrative field identification data conflict, trigger freezing or weight reduction operation;Intervention deduction unit: generate the reverse intervention verification package containing low-cost intervention measures and review plan, and update causal rule base based on review data;The application breaks through the statistical correlation limitation of traditional technology, realizes the upgrade of diagnosis and treatment paradigm from "correlation" to "causality".

The invention relates to the field of medicine synthesis, and discloses a GCG-GLP-1 dual agonist compound. The GCG-GLP-1 dual-agonist compound provided by the invention is used for preparing a pharmaceutical composition for treating diseases, and the application of the pharmaceutical composition in preparing a medicine for treating at least one of the following diseases, the diseases include type II diabetes mellitus, impaired glucose tolerance, type I diabetes mellitus, obesity, hypertension, metabolic syndrome, dyslipidemia, cognitive impairment, atherosclerosis, myocardial infarction, coronary heart disease, cardiovascular disease, stroke, inflammatory bowel syndrome and / or dyspepsia or gastric ulcer, hepatic fibrosis disease and pulmonary fibrosis disease.

The present invention relates to a communication system and method designed for patients in clinical settings who face challenges using standard communication devices. This system includes a mobile application and associated devices that establish a secure, asynchronous communication channel between clinicians, patients, and their support networks. Authorized users can record and send voice messages and other media, which are automatically played on the patient's device without requiring active operation. The system is tailored for ease of use, accommodating patients with severe physical or cognitive limitations through hands-free operation and simple commands. It adapts to the patient's condition, pausing messages during rest or medical procedures and resuming when appropriate. The system also features cross-linguistic messaging, translating and synthesizing voice messages across languages while maintaining the speaker's voice. Additional features include support for rich media, automatic transcription, and AI-driven content analysis. Secure, encrypted channels ensure privacy, complying with healthcare regulations. This invention enhances emotional connection and support for isolated patients, acting as a virtual visitation tool.

The invention discloses a cognitive impairment screening method and system based on face analysis. The cognitive impairment screening method comprises the following steps: selecting a video clip with a preset duration for a user to watch, wherein the video clip comprises a plurality of smile points; setting a corresponding time window according to the appearing time of each laughing point; based on spontaneous facial expressions after the user watches the video, facial motion features of the user in each time window are collected, and the facial motion features comprise values of a plurality of facial motion units of the user; inputting the facial motion features of the user in each time window into a preset model to output a cognitive impairment screening result; wherein the preset model carries out model training based on historical big data, so as to form a mapping relation from the facial movement features to the cognitive impairment screening result. Therefore, by utilizing the method, the cognitive impairment can be screened in a diversified scene, the dependence on professionals is reduced, a user does not need to be wounded, and the method is particularly suitable for early screening of the cognitive impairment.

The invention relates to the field of medicine synthesis, and discloses a GLP-1 / GIP / GCG tri-agonist compound. The GLP-1 / GIP / GCG three-agonist compound is used for preparing a medicine composition for treating diseases, and the application of the medicine composition in preparing a medicine for treating at least one of the following diseases, namely, the GLP-1 / GIP / GCG three-agonist compound, and the GLP-1 / GIP / GCG three-agonist compound. The diseases include type II diabetes mellitus, impaired glucose tolerance, type I diabetes mellitus, obesity, hypertension, metabolic syndrome, dyslipidemia, cognitive impairment, atherosclerosis, myocardial infarction, coronary heart disease, cardiovascular disease, stroke, inflammatory bowel syndrome and / or dyspepsia or gastric ulcer, hepatic fibrosis disease and pulmonary fibrosis disease.