25130 results about "Disease" patented technology

The invention relates to the technical field of remote sensing monitoring, in particular to an intelligent forest disease and pest monitoring method and system based on unmanned aerial vehicle remote sensing, and the method comprises the following steps: collecting multispectral data by an unmanned aerial vehicle, extracting reflectivity and smoothing the reflectivity, carrying out differential recognition on abnormal pixels, extracting curves and screening significant changes, segmenting scab boundaries, and classifying health states. And generating a pest and disease map layer prediction trend. According to the method, the reflectivity time sequence is constructed, differential processing is carried out, the vegetation change trend is dynamically captured, abnormal areas are identified by combining slope offset and persistence analysis, significant pixels are screened according to main peak wavelength offset, boundary information is extracted, the scab positioning precision is improved, and the recognition resolution of the lesion state is enhanced through reflectivity combined analysis; accurate description of disease spot dynamic changes is realized, static image dependence limitation is broken through, monitoring time continuity and space response capability are enhanced, and disease and insect pest change capture efficiency and state classification accuracy are effectively improved.

The invention relates to the technical field of civil engineering tunnel structure safety monitoring and intelligent detection, in particular to a multi-modal fusion tunnel structure apparent disease identification and risk assessment system, which comprises an image acquisition module used for acquiring continuous images of the inner wall of a tunnel lining; a laser point cloud acquisition module; a structure sensor acquisition module; a data synchronization and preprocessing module; the multi-modal feature extraction module is used for performing depth feature extraction on the image, the point cloud and the sensor data; the heterogeneous feature fusion and disease identification module is used for fusing each modal feature and outputting a disease type identification result; and the risk assessment module is used for carrying out size estimation and parameterized expression on the identified diseases. The problems that in an existing tunnel inspection technology, the detection means is single, appearance and internal information cannot be considered, and the disease size is difficult to quantify automatically are solved.

The invention relates to a digital chronic disease intelligent management platform based on an AI model and multi-dimensional data fusion, clinical diagnosis and treatment data, wearable equipment monitoring data, medication record data and environment monitoring data are acquired through a data acquisition module, and after standardized preprocessing is performed through a data fusion processing module, deep analysis is performed through an AI analysis module, and the data fusion processing module performs data fusion processing; in combination with medical knowledge of the knowledge base module, the intelligent decision-making module generates a personalized management scheme, and the personalized management scheme is implemented through the intervention execution module and the intelligent interaction module. Multi-dimensional health data are processed through an AI large model, a complex mode and an association relationship are automatically learned, and accurate disease prediction and risk assessment are realized; the pertinence of the scheme and the compliance of a patient are greatly improved; and real-time interaction and personalized guidance are provided, the participation degree and the self-management ability of the patient are effectively enhanced, and a benign health management cycle is formed.

The invention discloses an unmanned aerial vehicle laser and vision fusion inspection method and system for bridge bottom disease detection, and the method comprises the steps: carrying out the synchronous data collection through employing a calibrated laser radar, a camera and an IMU, and obtaining a three-dimensional laser point cloud and a two-dimensional visual image of the appearance of a bridge; sharpening the image containing the motion blur and completing brightness self-adaption of the image; stable feature points are extracted, multi-frame matching is carried out, the corresponding poses of the images are estimated, and bridge dense point cloud reconstruction is completed; performing geometric component segmentation on the point cloud to generate a geometric prior region; component segmentation is carried out on a support area in the image, and a continuous and accurate component segmentation result is obtained in combination with a geometric prior area; screening the image, calling a targeted disease detection model in a corresponding component area, and generating a segmentation mask for the disease; obtaining a real disease three-dimensional point cloud, and carrying out quantitative calculation on the physical size of the disease; and displaying the real disease three-dimensional point cloud data and the physical size of the disease. The method is high in efficiency and precision.

The present invention provides macrocyclic compounds, the use thereof for selectively inhibiting the activity of RAS mutants, and pharmaceutical compositions comprising the compounds, e.g., for treatment of various diseases including cancer.

The present application provides a method for diagnosing and / or evaluating the presence or absence, severity or degree of the improvement of a retinal disease in a subject, which comprises determining and / or evaluating circulatory parameters, retinal function, retina morphology and / or visual relating quality of life (QOL).

Frog farming method and frog cage are provided. The method comprises: I, putting young frogs in the frog cage in spring; II, putting lure attracting mosquitoes into the frog cage into the frog cage in summer and autumn, thereafter the frogs feed on the mosquitoes; III, avoiding snakes entering the frog cage in the place where the snakes inhabit; IV, covering the frog cage with plastic film in winter, mounting an electric lamp in the frog cage, increasing the temperature of air in the frog cage to keep the frogs not hibernating and promote the frogs to grow; V, adding artificial lure if necessary. The frog cage includes a fixed frog cage and a vehicle-mounted frog cage, both including inner tiers, wherein a water basin, a terrace and a stairway communicating the upper and lower tiers are arranged on each tier, and the frog cage is covered with a mesh enclosure. The frog framing method and frog cage have the advantages that the frogs are farmed in multi-tier cages, lures are used outdoors to attract insects, pests are eliminated for crops, natural ecology is balanced, and food sources are also provided for human; cage framing of frogs is very low in cost; frogs farmed have high reproductive rate, high disease resistance and high feed conversion capacity.

A spiro compound, and a preparation method therefor and a use thereof. Specifically, the present invention relates to a compound of formula (I) or a pharmaceutically acceptable salt, stereoisomer, tautomer, polymorph, solvate, N-oxide, isotopically labeled compound, metabolite, or prodrug thereof, a pharmaceutical composition comprising the compound, a preparation method therefor, and a use thereof in preparation of a drug for preventing or treating KRAS-mediated related diseases.

This disclosure relates to a method of treating a neurological disorder, comprising administering a bupropion, or a combination of a bupropion and a dextromethorphan, to a human being in need thereof, monitoring the human being for Brugada pattern / syndrome, drug reaction with eosinophilia and systemic symptoms, or acute generalized exanthematous pustulosis, and reducing or discontinuing use of the bupropion, or the combination of the bupropion and the dextromethorphan, if the human being develops Brugada pattern / syndrome, subcutaneous tissue disorder, drug reaction with eosinophilia and systemic symptoms (DRESS), or acute generalized exanthematous pustulosis.

The invention discloses an illness state tracking management system and method based on a lingual face diagnosis instrument, and belongs to the technical field of traditional Chinese medicine tongue diagnosis and modern information technology fusion. The system comprises a multi-modal data acquisition module, a data processing and analysis module and an augmented reality visualization module; according to the method, illness state tracking is achieved through the steps of multi-modal data acquisition, data preprocessing and feature fusion, personalized digital twinborn model establishment, augmented reality visualization presentation and the like, multi-dimensional data such as tongue picture macroscopic features and tongue surface microorganism distribution can be integrated, dynamic association between the data is revealed, the health state and the intervention effect are visually displayed, and the method is suitable for being popularized and applied. The method is suitable for the fields of traditional Chinese medicine health management and chronic disease monitoring.

A compound having the following structure of Formula (I): or a stereoisomer of the compound, tautomer of the compound, or salt thereof, wherein R1, R2, L, X, Y, A, and B are as defined herein. Pharmaceutical composition comprising the compounds, and their use in methods of treating diseases are also described.

The invention relates to the technical field of data analysis, in particular to a diabetes clinical test data intelligent clustering analysis system and method based on federal learning. Comprising a data acquisition and preprocessing unit; a federal privacy protection unit; the dynamic clustering modeling unit is used for constructing a clustering model of self-adaptive diabetes data features, and realizing joint clustering analysis of multi-source heterogeneous data by adopting a hierarchical federal architecture and a dynamic parameter aggregation algorithm and combining a diabetes course time decay factor and a clinical feature weight adjustment strategy; a double-track verification optimization unit; and an intelligent decision support unit. According to the method, the incidence matrix of the diabetes disease course time decay factor and the clinical characteristics is introduced, the dynamic weight vector is constructed and applied to clustering distance calculation, so that the model can adapt to dynamic changes of the clinical characteristics in different disease course stages, the adaptability to multi-center heterogeneous data is improved, and the stability of a clustering result is enhanced.

The disclosure provides methods for treating a subject in need thereof comprising administering to the subject a therapeutically-effective dose of psilocybin. The methods described herein may be used to treat a variety of diseases, disorders, and conditions. For example, the methods may be used to treat anxiety disorders, eating disorders, and headache disorders.

The invention relates to the technical field of medical decision, and discloses a medical decision support system based on a knowledge graph, and the system comprises a knowledge graph construction module which constructs an initial knowledge graph based on a medical ontology library, and the knowledge graph comprises entities and association relationships of diseases, symptoms and drugs; the data acquisition module is used for acquiring data from an electronic medical record, wearable equipment, a medical literature library and a hospital information system and normalizing the data through a standardized protocol; the dynamic knowledge updating module is used for processing normalized data through an incremental graph neural network; a multi-source knowledge fusion module; a context awareness module; a dynamic deduction module; and a decision optimization closed loop module. And triggering a preset clinical rule in real time based on the pathological state of the patient, dynamically adjusting the intensity value of the related edge in the factor graph, and persistently storing the intensity value back to the knowledge graph, so that logic adaptation and individualized experience precipitation of general medical knowledge in a special pathological state are realized, and the individualized treatment accuracy is ensured.

The invention discloses an interpretable machine learning genome prediction method and device, belongs to the technical field of combination of biological breeding, biological information and machine learning, and utilizes an advanced machine learning algorithm to perform parameter optimization in combination with biological prior information. Through processing of multi-source data (genome, transcriptome and epigenetic data), dynamic feature engineering (PCA and PHATE dimensionality reduction) and organic combination of various machine learning models, and an automatic parameter adjustment framework based on a grid search and sparrow search algorithm, genome prediction precision and calculation efficiency are significantly improved; meanwhile, the interpretability of the model is realized based on the SHAP value, the SNP site contribution is quantified, a reference is provided for precise breeding, and the method is suitable for animal and plant molecular breeding and medical genetic analysis, and can accelerate genetic analysis of high-value characters, assist precise breeding decision and disease risk prediction, and promote leap-forward development from experience breeding to intelligent breeding.

Computer-implemented methods are disclosed for assessing the effect of musculoskeletal activities on disease and / or clinical events, the method comprising: receiving a patient-specific vascular and musculoskeletal model of a patient's anatomy, including at least one vessel of the patient; receiving at least one characteristic of the patient's musculoskeletal activity; generating or updating a computational anatomic vascular and musculoskeletal model of the patient's anatomy based on the received at least one characteristic of musculoskeletal activity; performing at least one of a computational fluid dynamics analysis or a structural mechanics simulation on the computational anatomic vascular and musculoskeletal model; and estimating at least one of the patient's risk of disease or clinical events based on the performed computational fluid dynamics analysis and / or structural mechanics simulation. Systems and computer readable media for executing these methods are also disclosed.

The invention discloses an intelligent physical examination data quality control analysis method and system, and relates to the technical field of medical quality control. The problems that existing physical examination data are diversified in source, complex in format, single in quality control means and the like are solved. Structured data, medical images and texts are integrated, and standardized processing and data mapping are achieved through a unified platform. Static and dynamic rule libraries are constructed, and the threshold is dynamically adjusted in combination with individual features, so that personalized risk assessment is realized. Structured data anomaly detection is performed by adopting machine learning, an image quality problem is identified by utilizing a convolutional neural network, and text anomaly is processed and analyzed through a natural language, so that the anomaly detection accuracy is improved. The system realizes multi-dimensional quality control and intelligent early warning, enhances the integrity and credibility of physical examination data, supports precise health management and disease early warning, improves the quality control efficiency, meets the intelligent analysis requirements of large-scale multi-modal physical examination data, and promotes the application of intelligent health management.

The invention relates to the field of radiology reports, and discloses a radiology report generation method fusing disease perception comparison and cross-modal alignment, and the method comprises the following steps: respectively extracting visual features of a medical image and text features of a radiology report through a visual encoder and a text encoder; utilizing a disease perception contrast learning module DACL to map the visual features and the text features to a unified semantic space; self-adaptive feature matching is carried out by using a dynamically updated memory matrix through a cross-modal memory alignment module CMA; a three-stage training strategy optimization model is adopted, and pre-training, reinforcement learning fine adjustment and knowledge distillation are included. Incremental knowledge is dynamically updated through text features, cross-modal feature alignment tasks are focused, coupling with feature representation is avoided, alignment efficiency is improved, a three-stage training strategy is adopted, if student model performance is better, weights are migrated, and clinical accuracy and generalization ability of the model are further enhanced.

The disclosure provides methods for treating a subject in need thereof comprising administering to the subject a therapeutically-effective dose of psilocybin. The methods described herein may be used to treat a variety of diseases, disorders, and conditions. For example, the methods may be used to treat anxiety disorders, eating disorders, and headache disorders.

The invention discloses a traditional Chinese medicine famous prescription and operation multi-agent collaborative consultation method and system. The system comprises a medical case knowledge base module, an expert knowledge base module, a medicine knowledge base module, a pre-differentiation module, a multi-agent diagnosis module and the like. The method comprises the following steps: constructing structured experience medical case data, an expert experience knowledge graph and a traditional Chinese medicine knowledge graph knowledge base, carrying out mixed retrieval to carry out pre-differentiation, calling multi-agent consultation according to preliminary disease differentiation and syndrome types, and obtaining an expert diagnosis scheme for risk verification and prescription adjustment optimization. According to the invention, a multi-agent collaborative diagnosis technology is utilized, and a multi-agent architecture for simulating real traditional Chinese medicine consultation is constructed. A plurality of expert agents perform independent diagnosis and complement each other, so that the limitation of a single agent is avoided, and comprehensive and accurate diagnosis is provided. And in combination with expert experience knowledge graph and big language model reasoning, the system can flexibly differentiate symptoms according to the symptoms of the patient, accurately judge disease types and syndrome types, and improve the differentiation accuracy.

Pharmaceutical formulations of novel indole compounds and psilocybin analogs are manufactured, provided in novel oral, transdermal, and nasal pharmaceutical compositions for use to treat neurological, mood or abuse diseases and disorders.

The invention discloses a heart failure treatment aid decision generation system based on multi-modal data fusion, and the system comprises a data collection module which is used for collecting the multi-modal data of a patient, and the multi-modal data comprises structured data, unstructured data and medical image data; the data processing module is used for carrying out standardization, quantization and vectorization processing on the multi-modal data; the knowledge graph construction module is used for constructing a knowledge graph of heart failure treatment, and the knowledge graph comprises a disease entity, a pathological feature, a treatment scheme and an association relationship thereof; the reasoning module is used for generating a personalized treatment decision based on the knowledge graph and the patient data; and the treatment scheme generation module is used for dynamically adjusting and outputting a personalized treatment scheme in combination with the real-time state data of the patient. The problems that multi-modal data are difficult to fuse and real-time disease change is difficult to dynamically adjust in heart failure diagnosis and treatment are solved, accurate diagnosis and personalized treatment are realized through knowledge graph reasoning and a dynamic correction mechanism, and the diagnosis and treatment efficiency and accuracy are remarkably improved.

The invention relates to the technical field of data visualization, in particular to a multi-modal fusion anorectal data visualization analysis method and system, and the method comprises the following steps: synchronously collecting an anorectal image and a pressure signal, aligning a timestamp, carrying out the preprocessing, outputting a standardized image and a coded physiological signal, and extracting edge and texture features. After LSTM coding, splicing with image features, and fusing into a joint feature map; generating a multi-scale heat map by the feature pyramid network, and coarsely positioning a lesion; after boundary optimization, U-Net recovers details, and a pixel-level lesion probability graph is output; superposing the Jet color gradation to the original image in a semitransparent manner, and marking a pressure abnormal time period; and performing weighted scoring to generate a fourth-level clinical suggestion. According to the anorectal disease diagnosis method, through multi-modal space-time alignment, multi-scale feature fusion, boundary optimization and a quantitative scoring system, the problems of data splitting, insufficient precision and low efficiency of a traditional method are solved, a high-precision and high-robustness intelligent diagnosis tool is provided for anorectal diseases, and the scientificity and efficiency of clinical decision making are remarkably improved.

The invention provides a traditional Chinese medicine intelligent health diagnosis system and method based on multi-source data fusion, and relates to the technical field of health diagnosis. The system comprises a database construction module which is used for collecting condition data and historical disease data of a patient and constructing a traditional Chinese medicine holographic health database. And the multi-dimensional classification module is used for establishing a multi-source data classification standard and adding category fields to the multi-source data in the traditional Chinese medicine holographic health database from multiple dimensions to obtain associated field data. The importance classification module is used for constructing an importance degree judgment standard and judging the importance degree of different associated field data to obtain importance labels, and the exclusive customization module is used for classifying health states according to different importance labels to obtain current health data and formulating exclusive treatment strategies. According to the method, the data are classified and associated from multiple dimensions, the organization and utilization efficiency of the data is improved, and the importance degree judgment method can provide a basis for priority processing and analysis of the data.

The invention relates to an ischemic cerebrovascular disease angiography image segmentation analysis method, which comprises the following steps: detecting gray transition abnormity, structural fracture and artifact delay signals in an angiography image, extracting negative segmentation priori points indicating a suspected ischemic area, aggregating to form a priori abnormal area, introducing a symmetric disturbance test mechanism, and analyzing the angiography image according to the priori abnormal area. Judging a potential blocked or abnormal blood vessel segment, and dynamically adjusting the segmentation threshold of the region; establishing a local interference window in the judgment region, extracting frequency and rhythm features of density stripes, and performing compensation segmentation on interrupted blood vessel segments caused by unsteady pulse change through a convolution kernel scaling strategy; calculating a texture difference value and a frequency domain response offset, if the offset is within a preset physiological tolerance range, triggering an interpolation completion mechanism, and generating a credible completion layer for subsequent calibration reference; and performing dynamic feedback adjustment and continuous calibration on the previously segmented path by analyzing the multi-path divergence degree of the vascular branch end point and the path offset change in the image sequence.

The invention discloses a cold region tunnel freeze injury diagnosis, risk grading and control system and method, and relates to the technical field of tunnel engineering. The system comprises a basic data layer for storing disease, index and measure databases; the core analysis layer is used for diagnosing a frost heaving mechanism, identifying single-factor, double-factor and three-factor frost heaving types, calculating freezing depth and frost heaving force and analyzing sensitivity; the risk level evaluation layer is used for dividing a disease zone, a frost heaving level, a freezing injury risk and a freezing injury risk response level; and the intelligent decision-making layer constructs an intelligent comprehensive decision-making analysis platform to realize risk-measure accurate matching and closed-loop management and control. The method comprises the four steps of data acquisition and storage, frost heaving mechanism diagnosis and quantification, risk grade evaluation and intelligent decision measure matching, through multi-source data fusion, multi-factor coupling analysis and zoning and grading prevention and control, the whole-process accurate management and control of the cold region tunnel frost damage is realized, and the frost damage treatment efficiency and the tunnel operation safety are improved.

The invention relates to the technical field of disease prediction, in particular to an intelligent risk prediction method based on a neural network, and the method comprises the steps: collecting blood glucose and body mass index data, carrying out the clustering analysis, and constructing a multi-risk group feature; a risk transfer track before attack is recognized by combining a historical index change path and time sequence analysis; and calculating an individual risk coefficient by using deep learning, and comparing the individual risk coefficient with a risk threshold to generate early warning information, thereby realizing dynamic accurate evaluation. According to the method, by collecting the blood glucose level and body weight index data and conducting standardization processing, risk group division based on health index clustering can be achieved, a group recognition system with health feature differentiation is constructed, and the structured understanding of the individual health trend is enhanced. And on the basis of the clustered group, extracting a fluctuation path of individual historical indexes by means of a time sequence mode, identifying key transfer characteristics before disease attack, and enhancing the traceability of the disease formation process.

The invention discloses a bovine I-type alpha interferon-ferritin fusion protein, a mutant thereof, a preparation method and an application of the bovine I-type alpha interferon-ferritin fusion protein. The bovine I-type alpha interferon is fused with a ferritin subunit, and interferon molecules are highly repeatedly and orderly displayed on the surface of a ferritin nanocage by utilizing the self-assembly characteristic of ferritin, so that the expression level, the structural stability and the antiviral activity of the interferon are remarkably improved. The fusion protein is further subjected to single-site or multi-site rational design mutation, and a mutant with significantly improved antiviral activity and stability is obtained. According to the invention, a silkworm or insect cell eukaryotic expression system is adopted to express the fusion protein or the mutant thereof, and the expression system is safe to operate, simple and convenient in procedure, low in cost and extremely beneficial to large-scale industrial production; the prepared fusion protein or mutant nanoparticles have application prospects in preparation of drugs or reagents for preventing or treating bovine viral diseases.

The present invention provides a class of small molecule compounds for targeted degradation of a pan-KRAS protein, a preparation method therefor, and use thereof as a therapeutic agent for preventing and / or treating cancer. The compound of the present invention can effectively degrade and / or inhibit the pan-KRAS protein in cells, and can be used for preparing a drug for treating and / or preventing related diseases or disorders caused by pan-KRAS mediation.