11 results about "Disease process" patented technology

The invention discloses a vascular cognitive impairment prediction method, a computer program product and a terminal, and belongs to the field of deep learning. Feature information of medical time sequence data of a patient is extracted based on a time sequence data processing model; compressed feature representation of the feature information is learned through the information bottleneck; and performing prediction according to the compressed feature representation to obtain a prediction result of the disease progress. According to the method, the time sequence data processing model is used for capturing time dependence in the time sequence data, features related to the disease progression of dementia are effectively extracted from clinical data, then the features are screened by adopting information bottleneck, the most important features for classification prediction are identified, redundant and irrelevant information is eliminated, and the accuracy of classification prediction is improved. Therefore, the accuracy of classification prediction is improved. Furthermore, in order to simulate and generate various possible future data paths and reflect the uncertainty in the data, future process data is generated by using a diffusion model after feature extraction.

The invention discloses a disease progress prediction method and system based on frequency domain characteristics, and the method comprises the steps: carrying out the modeling of multi-dimensional frequency domain characteristics, and converting an original physiological signal into a global frequency domain characteristic; self-adaptive frequency band selection: selecting an optimal frequency band combination in the global frequency domain characteristics by using a genetics algorithm; extracting a frequency domain template of the disease, constructing a disease specific frequency domain feature library (DSFP), and calculating a matching score; training a time sequence prediction model according to the optimal frequency band combination and the matching score, and performing disease progress prediction by using the trained prediction model; and using the trained prediction model to predict the disease process. The system comprises a time sequence prediction model training module and a real-time prediction model application module. According to the method, the frequency domain and multi-scale time-frequency structure features are fully mined, and dynamic matching with disease pertinence is realized through the constructed disease specific frequency domain feature library (DSFP).

The application discloses application of a glycoprotein in preparation of a product for treating psoriasis, and belongs to the technical field of medicines. The application proves for the first time that the glycoprotein with a specific amino acid sequence can target macrophages and effectively inhibit inflammatory reactions triggered by various psoriasis inducers, and directly targets a key immune link of disease onset to play a role; in-vivo experiments not only prove significant therapeutic effect, but also make clear that the therapeutic effect depends on macrophages through mechanism research, so that verified solutions are provided for the shortage of current macrophage-targeted therapeutic drugs, and a solid core foundation is laid for development of a new type of therapeutic product which can fundamentally intervene in the disease process and reduce the recurrence rate.

The application relates to a similar case query method based on a cause-effect-dynamic multi-granularity, which comprises the following steps: acquiring a to-be-queried feature of to-be-queried data, inputting the to-be-queried feature into an encoder of a trained dynamic time warping kernel autoencoder, calculating the similarity between a time sequence sign feature in the to-be-queried feature and each preset typical disease process template, then mapping each similarity into a corresponding multi-granularity dynamic feature vector, aggregating all the multi-granularity dynamic feature vectors to obtain a feature vector set, and expanding the feature vector set into a single high-dimensional vector, performing cause-effect attention weighting on the high-dimensional vector through a cause-effect attention network to obtain a cause-effect weighted feature, calculating the cause-effect perception similarity between the cause-effect weighted feature corresponding to the to-be-queried data and the cause-effect weighted feature corresponding to a known case in a database, and taking the known case with the highest cause-effect perception similarity as the similar case of the to-be-queried data, so that a transparent and credible similar case query service is provided.

The universal biomarker array enables diagnosis and localization of disease states earlier than now recognizable, and overall increases the accuracy of diagnosing disease states. A universal biomarker array is formed from a series of mathematical parameters calculated from digital tissue images showing tissue details at cellular levels. The universal biomarker array includes one or more novel arrays of parameters (i.e., a spatial entropy array, a bin array, and / or a quartile array) in combination with one or more arrays of prior art parameters (i.e., a pattern array, a distance array, and / or a morphological array) and / or in combination with one or more other parameters. The AI / ML system can be used to analyze universal biomarker arrays relative to database-based reference data for early diagnosis and localization of disease processes.

The invention discloses a construction method and application of an anti-NMDAR encephalitis disease animal model. The construction method comprises the following steps: mixing a GluN1 (359-378) peptide fragment and mycobacterium tuberculosis (Mtb) into an incomplete Freund adjuvant to obtain an emulsified mixture, performing subcutaneous injection to two sides of the back of an anesthetized mouse, and performing intraperitoneal injection of a pertussis toxin (PTX) solution; injecting the PTX solution with the same concentration into the abdominal cavity of the mouse after 48 hours; after 14 days, repeating the first operation to carry out secondary boosted immunization on the mouse; injecting the PTX solution into the abdominal cavity again after 16 days; and after 32-35 days, biological detection is carried out to determine that modeling succeeds. According to the construction method, the period is greatly shortened, and the experiment efficiency is improved; according to the method, through an optimized immune scheme, the model success rate is high, and the phenotype is stable; the model is an active immune model and can simulate a complete disease process from autoantibody generation and immune cell infiltration to central nervous system injury.

The application relates to the technical field of artificial intelligence, and particularly discloses a disease simulation method and device, electronic equipment and a storage medium, wherein the method comprises the following steps: obtaining pet information, historical feeding information of a pet to be diagnosed, and examination information of a current disease of the pet to be diagnosed; constructing a virtual pet according to the pet information, constructing a simulation model according to the diagnosis information and the historical feeding information, and simulating a disease process of the pet to be diagnosed; determining at least one candidate disease information according to the examination information; inputting the virtual pet and each candidate disease information in the at least one candidate disease information into the simulation model respectively for disease simulation processing, and obtaining at least one simulation result; determining a diagnosis probability of each simulation result in the at least one simulation result according to the examination information, wherein the diagnosis probability is used for identifying a probability that the current disease is disease information corresponding to the simulation result corresponding to the diagnosis probability; and sending each simulation result and the diagnosis probability of each simulation result to a diagnostic doctor.

This application discloses a method, device, equipment, and storage medium for early warning of critical states in biological systems, relating to the field of bioinformatics. It quantifies the degree of local network disturbance using network topology indicators, enabling the sensitive detection of abnormal turning signals in the cell network topology before disease symptoms appear, providing a basis for early disease warning. The method includes: obtaining a cell feature matrix; constructing a cell-related network for each stage of the disease process based on cell expression characteristics in the cell feature matrix; defining local cell networks within the cell-related network of each stage of the disease process; determining a global evaluation indicator for each stage of the disease process based on the topology evaluation indicators of all local cell networks in the disease process stage; and identifying the stage of the disease process where the global evaluation indicator first reaches a maximum value as a critical state, thereby providing disease warning through the critical state.

A universal biomarker array enables the diagnosis and localization of earlier-than-now recognizable disease states as well as increasing the accuracy of diagnosing states of disease(s) in general. The universal biomarker array is formed of a range of mathematical parameters computed from digital tissue images showing tissue details at a cellular level. The universal biomarker array includes one or more novel parameter arrays (namely, a spatial entropy array, a bin array, and / or a quartile array) in combination with one or more state-of-the-art parameter arrays (namely, a pattern array, a distance array, and / or a morphology array) and / or in combination with one or more other parameters. An AI / ML system can be used to analyze the universal biomarker array relative to database-based reference data for early diagnosis and localization of disease processes.

The invention discloses an early warning method, device and equipment for a critical state of a biological system and a storage medium, and relates to the technical field of bioinformatics, the local network disturbance degree is quantified through network topology indexes, abnormal turning signals of a cellular network topology structure can be sensitively captured before disease symptoms appear, and the early warning accuracy is improved. And a judgment basis is provided for early warning of diseases. The method comprises the following steps: acquiring a cell characteristic matrix; constructing a cell correlation network of the disease process stage according to cell expression characteristics of the disease process stage in the cell characteristic matrix; defining a local cellular network in the cellular correlation network of the disease process stage, and determining a global evaluation index of the disease process stage according to topology evaluation indexes of all local cellular networks in the disease process stage; and according to the global evaluation indexes of all the disease process stages, determining the disease process stage of which the global evaluation index has a maximum value for the first time as a critical state so as to carry out disease early warning through the critical state.

The invention belongs to the technical field of biological medicine, and particularly relates to application of Ctss as a silicosis diagnosis marker and an inhibitor thereof. The invention finds and proves that the Ctss has high specific expression in a silicosis model for the first time, the expression level of the Ctss is positively correlated with the disease process, and the Ctss can be used as a potential biomarker for diagnosing silicosis. Further mechanism research shows that the Ctss can promote fibroblast activation and extracellular matrix protein synthesis, so that the pulmonary fibrosis process is driven. Targeted inhibition of Ctss is provided as a new strategy for treating silicosis, the effectiveness of the specific Ctss inhibitor LY3000328 is verified in an animal model, and lung inflammation and collagen deposition can be remarkably relieved. Therefore, the invention provides the application of the Ctss in preparation of silicosis diagnosis or evaluation products and the application of the Ctss inhibitor in preparation of anti-silicosis pulmonary fibrosis drugs, and provides a solution for early diagnosis and targeted therapy of silicosis.