50 results about "Complex disease" patented technology

The invention discloses a multi-omics data integration and classification method, system and device based on hierarchical attention, and is applied to the field of precise medical big data analysis. The method comprises the following steps: firstly, generating feature embedding and feature importance scores through a plurality of parallel feature-level attention modules; then, embedding and inputting all the characteristics of the omics into a unified omics-level attention module, and generating omics embedding and omics importance scores; and finally, a classification prediction task is executed based on omics embedding, and a classification result is output for disease classification. The invention completely abandons a traditional dependency graph convolutional network and an integration normal form of variants of the dependency graph convolutional network, and provides a universal hierarchical attention integration architecture. The framework supports classification tasks of any complex diseases, is not limited by omics data types and combination modes, not only is remarkably superior to a traditional integration normal form in classification performance, but also shows a unique negative generalization distance, and proves that the framework has excellent generalization ability. Meanwhile, features and omics importance scores automatically output by the model provide a powerful analysis tool for biomarker discovery and precise diagnosis and treatment of complex diseases.

The invention discloses an AI service development system based on an agent technology, which relates to the technical field of medical health and comprises a medical professional module, a digital duplication module, a continuous optimization module, an interactive service module, a safety management module, an ethical and compliance management module, a basic service module and an intelligent diagnosis module. According to the AI service development system based on the agent technology, a multi-dimensional medical knowledge graph which comprises a large number of medical entities and relation edges and a dynamically updated clinical path library is constructed by utilizing a semantic network, accurate and reliable medical knowledge support can be provided, and medical data is deeply analyzed in cooperation with an intelligent diagnosis module; the system provides auxiliary diagnosis of complex diseases, improves the accuracy and efficiency of diagnosis, is high in intelligent level, and can provide accurate and efficient diagnosis service for patients especially in the aspect of diagnosis and treatment decision support of the complex diseases.

The invention discloses a circRNA (Ribonucleic Acid) and miRNA (Micro Ribonucleic Acid) interaction prediction system and a circRNA and miRNA interaction prediction method of a graph Fourier pulse neural network. The method comprises the following steps: on the basis of high-throughput sequencing omics data of complex diseases, constructing a heterogeneous biological information network containing drugs, diseases, proteins, circRNA, miRNA and lncRNA; converting the topological features of the entities into a unified feature space by using a graph convolutional network; designing a pulse graph neural network in combination with Fourier coding and a pulse neural network, and extracting a topological structure and high-order semantic features in the network; fusing sequences, topologies and semantic features of circRNA and miRNA through a gate multilayer perceptron to obtain embedding features of circRNA and miRNA; and finally, the interaction of circRNA and miRNA is predicted by adopting a Bayesian classifier. According to the method, heterogeneous biological information is modeled from the perspective of network science, Fourier coding, spiking neurons and graph embedding learning are utilized, the action mechanism of circRNA and miRNA in complex diseases can be disclosed, and the method has good practicability and application prospects in the fields of artificial intelligence, life science, clinical medicine and the like.

Provided are methods, systems and computer program product embodiments for analyzing multi-omic data using a tensor regression model for genome-wide association studies in the life sciences. The unique structure of tensor covariates is leveraged to find associations between the omics data and complex diseases. Within this framework, the excessive dimensionality is reduced to a manageable level, leading to efficient estimations and predictions. The method is superior to using classical regression techniques in genome-wide association studies, which are challenged by analyzing multi-dimensional and uniquely structured data from the health and life sciences, in which covariates can take on more intricate forms such as multi-dimensional arrays. Embodiments have multiple uses in genomics, proteomics, metabolomics, multi-omics data integration, drug discovery, personalized medicine and predictive modeling, demonstrating the versatility and importance of tensor regression models to understand the associations between omics data and complex diseases.

The invention provides a method and system for gene expression quantitative trait locus analysis, and relates to the technical field of genomics, the method comprises the following steps: carrying out sample and locus level quality control on input genotype data, and generating a standardized genotype matrix; performing standardization processing and low expression gene filtering on the input gene expression data to generate a standardized expression quantity matrix; performing principal component analysis according to the standardized genotype matrix to obtain a group structure covariable; carrying out implicit factor analysis on the basis of the standardized expression quantity matrix to obtain a technical batch effect covariable; quantitative character site correlation analysis is carried out through a standardized genotype matrix, a standardized expression quantity matrix, a population structure covariable and a technical batch effect covariable. The method is suitable for functional genomics research, complex disease genetic mechanism analysis and precision medical related functional genetic variation mining scenes.

The invention discloses a high-stability reversible deformation carbon nanotube fiber sensor, and a device and a method thereof, and relates to the technical field of electrochemical biosensors. According to the sensor, fusiform carbon nanotube fibers are adopted as a sensing base body, sensitive materials are embedded into the fibers in the expansion state of the fusiform carbon nanotube fibers to form a stable sensitive layer, then the stable sensitive layer is recovered into a slender structure through screwing, and closed protection and minimally invasive implantation of the sensitive layer are achieved. The invention also provides a multi-parameter electrochemical detection device, which can realize in-vivo synchronous detection of various physiological indexes such as hydrogen peroxide, superoxide anions and glucose, and is suitable for continuous monitoring and early warning of a complex disease process. The device is novel in structure and high in function integration degree, has good mechanical protection performance and biological compatibility, is particularly suitable for multi-index in-vivo continuous monitoring, and has wide application prospects in the scenes of chronic disease early warning, postoperative monitoring, metabolic disorder evaluation and the like.

The application belongs to the technical field of medicine manufacturing, and discloses a through-the-du-to-soothe-the-nerves paste and a preparation method thereof. The raw material composition of the through-the-du-to-soothe-the-nerves paste comprises cornu cervi 10 g, zizyphus jujuba mill 30 g, notopterygium 10 g, panax 10 g, pueraria 30 g, astragalus 30 g, cassia bark 10 g, radix paeoniae alba 20 g, citrus grandis 10 g, poria cocos 20 g, licorice 6 g, radix scutellariae 6 g, and malt 20 g. The optimal preparation process parameters are as follows: water addition amount 1200 ml, soaking time 70 min, and decocting time 180 min. The application can relieve the low mood of patients, is used for treating insomnia, pain, and fatigue caused by depression, and blocks the development of depression. The application has the advantages of flexible prescription, individualized prescription, and temporary adjustment according to individual conditions, time, and place. The paste also has the characteristics of wide application range, main and secondary considerations, combination of treatment and prevention, and suitability for various complex diseases.

Provided is a medical assistance device including an acquisition unit configured to acquire personal data including examination data and medical interview data of a user, and an output unit configured to determine a disease state of the user for an individual disease forming a complex disease, based on the personal data, and configured to output an improvement target to be achieved by the user to improve a complex disease state, when a combination of the disease states of the individual diseases or a combination of the disease state of the individual disease and the personal data is the complex disease state.

The invention discloses a method for analyzing the action mechanism of a decoction of trichosanthes kirilowii maxim, allium macrostemon and pinellia ternate for treating heart failure (heart failure), and the multi-component, multi-target and multi-channel synergistic action mechanism of the prescription is systematically analyzed by integrating network pharmacology and metabonomics technologies. The method provides a systematic analysis strategy for research on a mechanism of treating complex diseases by a traditional Chinese medicine compound.

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.

A plurality of data pairs are created by matching an element from a first modality with an element from a second modality. Each element from the first modality and each element from the second modality are tokenized to obtain first modality tokens and second modality tokens. A model is trained based on the plurality of data pairs, the training comprising learning a first embedding from the first modality tokens via a first attention-based encoder for the first modality and a second embedding from the second modality tokens via a second attention-based encoder for the second modality, calculating a cosine similarity between the first embedding and the second embedding for each data pair and computing a loss between predicted items and ground truth based on the cosine similarity. The predicted items with a minimal loss are validated to obtain at least one candidate therapeutic.

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.

A method, computer system, and a computer program product for biomarker identification is provided. The present invention may include generating a plurality of higher-order joint cumulants based on an input data matrix. The present invention may include identifying one or more significant higher-order joint cumulant groups from the plurality of higher-order joint cumulants. The present invention may include embedding the one or more significant higher-order joint cumulant groups into a lower dimensional network. The present invention may include identifying one or more biomarkers.

The present disclosure provides novel self-amplifying RNA (saRNA) constructs that demonstrate enhanced protein expression, prolonged durability, reduced immunogenicity, and the ability to express multiple therapeutic proteins homogeneously. The saRNA constructs comprise a 5' untranslated region (5'UTR), non-structural protein genes derived from alphaviruses, at least one gene of interest encoding a therapeutic protein, a 3' untranslated region (3'UTR), and one or more modified nucleosides. Also disclosed are dual construct systems comprising a first construct encoding non-structural proteins and a second construct encoding one or more genes of interest. Methods of producing and using the saRNA constructs for engineering cells, particularly immune cells, for treatment of various conditions including cancer, inflammatory conditions, and infectious diseases are provided. The saRNA constructs enable the generation of "armored" immune cells expressing multiple therapeutic proteins, thereby providing a multi-pronged approach to complex diseases.

The application discloses a kind of volume calculation and geometric reconstruction methods, devices, equipment and medium inside road disease, which comprises: standardization processing to the three-dimensional simulation point cloud data of inside road disease, to obtain standardization simulation point cloud data;Pretreatment data and identify disease connected sub-region, determine the internal starting point set to construct starting point set, based on starting point set Construction space step direction and step search boundary point, generate boundary point set, fusion boundary point set obtains disease space profile, based on disease space profile completes disease volume calculation and three-dimensional geometric shape reconstruction;Since the application accurately locates the disease boundary by internal starting point constraint combined with multi-direction step search, restores complete disease geometry by fusing boundary points, accurately realizes the topological reconstruction and volume calculation of complex disease network, effectively solves the problem of incomplete disease point cloud boundary and complex topology of internal complex disease of road, and improves the integrity of disease reconstruction and the accuracy of volume calculation.

Many biologic processes taking place inside a living organism are unpredictable in time and space, and cannot be known exactly. These mechanisms and interactions among them are better modeled as physiological random processes, the statistics of which are fully described by joint characteristic functionals. The present invention provides methods for the estimation of joint characteristic functionals through imaging of multiple physiological random processes. This technology can be used to study complex diseases, such as tumors and viral infections, by imaging the biological processes involved with disease progression and response to treatment.

The invention discloses an underground pipeline structure health intelligent assessment method and system, and relates to the technical field of underground pipeline structure health assessment, and the method comprises the steps: constructing a disease recognition model employing an improved CNN-Transform hybrid architecture, and inputting a multi-modal feature vector into the disease recognition model; the feature vectors are fused and generated in real time in combination with edge computing equipment, the problem that traditional single data evaluation is one-sided is solved, and multi-source data are rapidly integrated; according to an improved CNN-Transform hybrid architecture, local features of cracks, corrosion and deformation are extracted through CNN, global disease association is captured through Transform, disease types and three-dimensional coordinate positions of the inner wall of a pipeline can be accurately output, and the complex disease misjudgment rate is reduced; the health recognition model outputs quantitative scores and health area positioning, and the defect that only diseases are recognized and overall health assessment is not available traditionally is overcome.

The invention provides a gene composition for screening or diagnosing amyotrophic lateral sclerosis and application of the gene composition, and belongs to the technical field of biology. The invention provides a gene composition for screening and / or diagnosing amyotrophic lateral sclerosis. The gene composition comprises at least one of the following groups: a hereditary c9ALS gene composition, a sporadic amyotrophic lateral sclerosis gene composition, a whole blood gene composition and a peripheral blood single cell gene composition. A system for screening or diagnosing amyotrophic lateral sclerosis is constructed based on the genotype or gene expression level of the gene composition, and a series-parallel logic circuit model (SP / PS) is creatively introduced as an analysis framework of the system, so that a powerful and explainable accurate diagnosis tool is provided for clinic; and a clear direction and a specific target spot are indicated for developing a targeted therapy capable of changing a disease process. The system has universality and is expected to be expanded to research of other complex diseases.

The embodiment of the application provides a complex disease function-specific core marker construction method and related equipment, and belongs to the technical field of biological detection. The method comprises the following steps: obtaining biological knowledge data related to a target disease; extracting candidate genes associated with the target disease, target function set data, disease-associated marker set and disease transcriptomics data from the biological knowledge data; performing disease association measurement on the candidate genes according to the target function set data, the disease-associated marker set and the disease transcriptomics data to obtain disease association measurement data of each candidate gene; performing screening processing on the candidate genes according to the disease association measurement data to obtain selected genes; and performing marker processing on the target disease according to the selected genes to obtain a specific function core marker of the target disease. The embodiment of the application can make the construction cost of the complex disease function-specific core marker lower and the accuracy higher.

The application relates to the technical field of biomedical engineering, and particularly discloses a method for extracting binary condensate immune signals, which comprises the following steps: screening disease-related high-expression genes by using a disease model, a normal model and a drug treatment model; screening important protein molecules based on the high-expression genes by using a nucleic acid-protein affinity analysis method; constructing a fusion carrier based on cytoskeleton proteins, important protein molecules and green fluorescent protein tracing; and identifying risk genes and possible downstream proteins associated with condensates by spatial omics imaging based on the fusion carrier of the disease model, the normal model and the drug treatment model, and quantifying the condensation process of the condensates. The application reduces the influence of problems such as condensate state destruction and spatial distribution information loss, and accurately realizes in-situ spatial omics imaging and analysis of an immune microenvironment evolution process of a complex disease.

The invention relates to application of 4 '-demethyl epipodophyllotoxin or a medicinal derivative thereof in depression resistance, and belongs to the technical field of biological medicines. Depression is a serious nervous and mental disease and affects millions of people in the world. Major depression is the most common mental disorder and is also one of the main causes of global disability. Depression is considered to be a complex disease caused by interaction of genetic, physiological, psychological and environmental factors. It is found that the novel antidepressant drug capable of treating depression has great significance. A per2- / -mutant zebrafish depression model is utilized to perform drug screening on traditional Chinese medicine monomers, and finally, 10 mu M of 4 '-demethylepipodophyllotoxin has an anti-depression characteristic and can effectively improve the depression-like phenotype of per2- / -mutant zebrafish. A medicinal plant podophyllotoxin from which 4 '-demethyl epipodophyllotoxin is extracted is safe and non-toxic, and no report about 4'-demethyl epipodophyllotoxin depression resistance exists at present.

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.

The invention relates to the technical field of health science popularization education, in particular to a knowledge graph-based interactive science popularization platform for old-age common-disease health management scenes, which is characterized in that a main body adopts a four-layer three-terminal architecture system, and the four-layer architecture comprises a data layer, a knowledge layer, a scene interaction layer and an application layer. The system has the advantages of breaking through practical problems such as disease complexity, insufficient health education and information overload so as to improve the health quality and self-management ability of the co-sick elderly patients, and in the actual use process, through the use of a four-layer three-terminal architecture system, the form of traditional health science popularization is fundamentally changed, and the development of health science popularization is facilitated. The knowledge graph is utilized to integrate the segmented single disease knowledge into the associated network, so that the spanning from fragmented informing to systematic understanding is realized, the elderly user is helped to clear complex interaction among common diseases, precise and personalized health guidance is provided, and the learning effect and initiative of the elderly user are effectively improved.

The invention provides a method ComplexDnet for identifying a disease target based on a network for the first time. According to the method, on the basis of a WGCNA result, a protein interaction network is constructed, and structural key analysis, robustness analysis and functional consistency analysis are further performed on the network to obtain three scores respectively. And constructing a comprehensive score algorithm according to the three scores, and sorting and screening the target spots according to the comprehensive score so as to obtain key treatment target spots. According to the method, the discovery period from data to candidate drugs can be shortened, the prediction accuracy is high, and the result is accurate.

The invention provides for the detection of a perturbed gene network, which includes highly expressed genes during fetal brain development, which is dysregulated in neuron models of autism spectrum disorder (ASD). High-confidence ASD risk genes are upstream regulators of the network modulating RAS / ERK, PI3K / AKT, and WNT / / β-catenin signaling pathways. The invention demonstrates how the heterogeneous genetics of ASD can dysregulate a core network to influence brain development at prenatal and very early postnatal ages and, thereby, the severity of later ASD symptoms. The invention provides a model for diagnosis, prognosis determination, and optionally treatment and monitoring, for any disease by comparing molecular marker patterns in non-affected tissues in a subject with healthy controls to determine a dysregulated network in the subject based on a co-expression pattern of interacting genes.

The invention relates to an apple leaf disease detection method based on a YOLOv10n-HC model, and belongs to the technical field of apple leaf detection. The method comprises the following steps: collecting a plurality of apple leaf images in different environments, and carrying out bounding box labeling and category labeling on disease targets in the apple leaf images to obtain an apple leaf disease data set; the method comprises the following steps: improving a traditional YOLOv10n model to obtain a YOLOv10n-HC model; the improvement comprises the following steps: introducing a coordinate attention mechanism into the backbone network; an ACmix convolution module is integrated in the backbone network; a C2f structure in a backbone network and a neck network is replaced by a C2f HyperConvNeXtBlock, and the C2f HyperConvNeXtBlock is used as a core network; a DCEIoULoss loss function is introduced to carry out bounding box regression optimization; using the apple leaf disease data set to train the YOLOv10n-HC model; and inputting an apple leaf image to be detected into the trained YOLOv10n-HC model, and obtaining an apple leaf disease detection result output by the trained YOLOv10n-HC model. The objective of the invention is to solve the technical problems of complex disease detection environment, tiny target, low precision and model redundancy in the prior art.

The clinical potential of multispecific antibodies like bispecific and trispecific antibodies shows great promise for targeting complex diseases. However, the generation of those molecules presents great challenges as in many cases it is desired to specifically drive the specific pairing of multiple polypeptide chains that are present in solutions. In the case of the heavy chains, there are two main regions that form a dimer interface. One of them is the CH3 region, which has been widely exploited by inserting either charge-pair mutations (CPMs) to steer the dimer interface or inserting large bulky residues into cavities (Knob in Hole) to physically favor and disfavor the dimer formation. However, each of these strategies may not be applied to every molecule and therefore there is the need for more tools. Here, we describe the engineering of the Hinge region with a small number of mutations that are capable to alone successfully drive the heavy chain dimerization.

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 invention relates to the field of biological information, in particular to a gene abnormality regulation and control detection method based on regulation and control pathway disturbance analysis, and the method comprises the following steps: collecting biological samples and pathway structure data; obtaining a weighted optimized pathway disturbance score through the gene dynamic behavior and the pathway context information; obtaining a gene dynamic influence factor through the difference expression significance and the consistency between the samples; obtaining a context association optimization factor of the pathway specificity through a pathway topological structure and expression coordination; optimizing a GSVA path disturbance score through the comprehensive contribution weight; and the abnormal regulatory pathway is identified based on the optimized score, so that the sensitivity and accuracy of the gene set variation analysis method in complex disease pathway disturbance identification are improved.