14 results about "Complex disease" patented technology

This invention provides an automated loading and fixation detection system for small model animals. Utilizing the siphon principle, the system constructs a siphon pathway using both soft and rigid capillaries, achieving pump-free loading and fixation of animals. Combined with an imaging analysis unit, it performs imaging analysis of the animals. By controlling the siphon flow rate, it enables rapid and automated fixation of animals without gel embedding or prolonged anesthesia, effectively improving fixation efficiency and shortening fixation time. Furthermore, the detection system of this invention is simple in structure, easy to operate, economical, and readily applicable. It achieves high-throughput, automated, pump-free fixation, and non-microfluidic chip-based automated loading and fixation detection for small model animals, providing highly promising technical support for the further development and screening of drugs for treating complex diseases, as well as other related research on small model animals.

This invention relates to a method for disease therapeutic target discovery and drug prediction based on multi-omics networks and deep learning models, belonging to the interdisciplinary field of bioinformatics and artificial intelligence drug discovery. The method includes: integrating genomic expression profiles and common molecular interaction data from disease and control groups to construct a candidate whole-genome network; refining the network based on expression profile data through systematic modeling and the AIC criterion to obtain the real molecular interaction network; extracting the core network using the master network projection method and identifying key targets through pathway analysis; predicting candidate drugs interacting with the targets using a pre-trained deep neural network model; and finally screening potential therapeutic drugs based on multi-dimensional criteria such as regulatory ability, sensitivity, and toxicity. This invention achieves a complete integration from disease mechanism analysis to drug prediction, and is particularly suitable for complex diseases such as atopic dermatitis. It can systematically discover precise targets and efficiently predict repositionable drugs, significantly improving R&D efficiency.

The present application relates to a kind of based on double-chamber hydrogel microsphere intestinal bionic organ model and its construction method, belong to biotechnology field.It is specifically double-chamber hydrogel microsphere with rough pit surface wrapped by intestinal barrier, construction method includes the following steps: the preparation of double-chamber hydrogel microsphere microfluidic device, preparation of rough double-chamber hydrogel microsphere with rough surface and load content, microsphere surface matrix glue modification and surface construction intestinal barrier.It is specifically applied to drug testing and the application research of microorganism and intestinal interaction.The intestinal bionic organ model prepared in the present application has simple manufacturing steps, short manufacturing time, low cost, and the subsequent culture maturation time is greatly shortened compared to other organoid models.The intestinal organ microsphere is expected to become a research platform for intestinal diseases, to promote the research of various intestinal disease treatment methods, and to help cope with the treatment challenges brought by various intestinal complex diseases.

This invention provides a method and system for retrieving patient digital models based on event graphs. The method includes: designing a timeline-based event extraction and structuring method for unstructured or semi-structured data in electronic medical records, constructing an event graph of the electronic medical records; extracting key information from the medical record text in the event graph using an intelligent model; designing a patient digital model retrieval algorithm; establishing a key information retrieval and matching mechanism; and quickly searching for digital models similar to the current patient's condition from a historical case database, providing data support for subsequent diagnosis and treatment analysis. This invention can extract key information from massive heterogeneous clinical data, construct patient digital models with high clinical relevance, interpretability, and dynamic adaptability. Based on event graph technology and large-scale model applications, it improves the modeling accuracy and clinical applicability of patient data in the diagnosis and treatment of complex diseases, significantly improving the accuracy, completeness, and clinical relevance of patient digital models.

The application discloses a kind of construction methods of diabetic nephropathy combined with acute kidney injury rabbit comorbidity model, it is related to biological medicine technical field, the construction method includes the following steps: rabbit is fed with high-fat high-sugar feed, then injection tetraoxypyrimidine, constructs type 2 diabetes rabbit model, continue to feed high-fat high-sugar feed, detect rabbit blood sugar, blood fat and urine albumin / creatinine ratio, carries out pathological examination of kidney tissue, determines whether the diabetic nephropathy rabbit model is established successfully, to the diabetic nephropathy rabbit model that is successfully constructed injection folic acid-sodium bicarbonate solution, detect blood urea and creatinine level, carry out pathological examination of kidney tissue, determine whether the diabetic nephropathy combined with acute kidney injury rabbit comorbidity model is successfully constructed.The application provides effective animal model for the pathogenesis and new treatment target of diabetic comorbidity, effectively solves the problem that single animal model cannot comprehensively reflect complex disease and the influence of drug side effects on experimental animals.

This invention provides a method and apparatus for evidence-based tracing of chronic diseases based on atomic-level constrained fingerprints, relating to the field of data processing technology. This invention utilizes a semantic unit library for retrieval combined with tracing display, improving tracing accuracy to the semantic unit level, achieving atomic-level localization and logical visualization. By combining semantic unit constrained fingerprints with contraindication conflict penalties, deep context awareness is achieved, effectively avoiding medication errors. This solves the problems of existing technologies such as excessively coarse evidence-based granularity, missing logical links, and inability to handle complex disease scenarios.

The application provides a disease treatment mechanism analysis method, system and application based on single-cell sequencing, spatial transcriptome and mass spectrometry imaging, and specifically belongs to the technical field of multi-omics analysis.The disease treatment mechanism analysis method comprises the following steps: preparing a tissue section;respectively performing tissue staining, single-cell sequencing, Spatial-seq 2.0 spatial transcriptome sequencing and mass spectrometry imaging on the tissue section; and performing single-cell spatial multi-omics affine registration and data joint analysis.The disease treatment mechanism analysis method fuses the data of multiple omics, has high spatial resolution, can realize cell subtype positioning, can comprehensively analyze the multidimensional mechanism of drug treatment of complex diseases from gene expression to metabolite, and breaks through the spatial and data integration limitations of traditional technologies.

This invention relates to the fields of biotechnology and medicine, specifically to methods and systems for screening gene mutation sites associated with the risk of complex diseases, constructing multi-gene genetic risk rating models for complex diseases, and predicting the risk of disease onset.

The application belongs to the technical field of biomedical data analysis and spatial omics data processing, and particularly relates to a disease target network construction method fusing pathological images and spatial transcriptome. Based on automatic or manual definition of a region of interest according to pathological characteristics, single-cell data and spatial transcriptome data are integrated; the spatial enrichment degree of different cell types and genes is quantitatively scored; and finally, a spatial co-localization network of genes and cells is constructed in the region of interest. The method can be compatible with various pathological imaging methods, and is suitable for irregularly shaped and significantly spatially heterogeneous disease tissues, overcoming the limitations of traditional spatial analysis methods guided by transcriptome characteristics in disease region positioning, and realizing systematic analysis from spatial pathology positioning to cell, gene and molecular correlation levels. The method is suitable for spatial mechanism research of complex diseases such as cardiovascular diseases, tumors and neurodegenerative diseases, and has high biological interpretation value and practical application significance.

The application discloses a road disease detection method and device under the perspective of a UAV, an electronic device and a program product. The method is realized based on a trained target detection model. When the target detection model is trained, the initial query is dynamically adjusted through a self-adaptive noise query generation mechanism, so that the generated noise query is more consistent with the target distribution characteristics, thereby improving the query expression stability, model convergence efficiency and detection accuracy. After training, the model removes the noise query generation process without increasing the inference complexity. In addition, during training, a Hungarian matching cost function and a consistency loss function are designed in combination with scale perception, shape consistency and direction consistency, which can improve the matching reliability of small targets and complex disease targets. Moreover, whether the model is in the training process or the application process, the backbone network is provided with a direction perception strip feature enhancement module, which can enhance the representation ability of the spatial continuity features of cracks, joints and strip-shaped diseases.

This invention relates to the fields of bioinformatics and immunogenetics, and discloses an HLA typing tool selection system, method, device, and medium based on a typing tool invocation agent. The method includes: acquiring second-generation sequencing data and Sanger sequencing data from patients with autoimmune diseases; having a typing tool invocation agent invoke several HLA typing tools to perform HLA typing analysis on the second-generation sequencing data, obtaining several HLA typing results; combining these with reference sequencing data to determine the performance evaluation results of each HLA typing tool; screening and optimizing HLA typing tools; and finally, obtaining the final HLA typing result. This invention, through data-driven tool optimization and gold standard validation, significantly improves the accuracy, reliability, and ability to discover new risk alleles in HLA typing in complex disease contexts, effectively reducing the cost of large-scale research, and has significant value for the study of the genetic mechanisms of autoimmune diseases and precision medicine.

This application provides a service bus-based resource scheduling method for internet hospitals, relating to the field of medical information processing technology. The method includes: collecting doctor session data and analyzing disease complexity and waiting intervals to identify high-risk abandonment sessions with non-linearly increasing waiting times; dynamically adjusting the upper limit of concurrent doctor sessions, pausing allocation for simple diseases and prioritizing complex disease sessions, and locking resources until a substantial response is completed; resuming allocation and continuously updating the upper limit when the waiting interval decreases. The beneficial effects of this application are: through non-linear growth trend identification and response slot locking mechanisms, it accurately intervenes in high-risk abandonment sessions, avoiding the contradiction between doctor overload and long patient waiting times in the traditional static allocation model, achieving a dynamic balance between doctor load and patient experience, and significantly improving the resource scheduling efficiency and service quality of online consultations.

This application discloses a method, apparatus, device, and medium for predicting pathogenic genes based on phenotypic fingerprinting. This method, executed by a computer, systematically integrates and quantifies the association information between genes and multi-dimensional phenotypes, constructing gene-specific phenotypic fingerprints at the population level. This allows for a more comprehensive capture of the complex effects of genes across different phenotypic dimensions. Furthermore, it calculates gene-centered multi-phenotypic scores using gene phenotypic fingerprints, transforming the multi-dimensional clinical phenotypic information of the target subject into gene-centered quantitative scores at the object level. Utilizing the observed phenotypes of the target subject, it achieves two key outputs: assessment of the risk of carrying pathogenic variants and priority ranking of candidate genes. This enables an objective and efficient evaluation of the fit between each candidate gene and the actual phenotype of the target subject. By introducing phenotypic fingerprinting and combining it with a multi-phenotypic scoring mechanism, this application significantly improves the efficiency and objectivity of identifying pathogenic genes for complex diseases, possessing significant clinical application value.