606 results about "Disease progression" patented technology

The AI-enhanced cellular modeling and simulation platform is a computational system designed to enhance biomedical research and development and personalized medicine and wellness. This platform integrates simulation modeling, machine learning and artificial intelligence, multi-omics data, and sophisticated data fusion and decision-support techniques to create comprehensive models of cellular systems and processes across multiple scales. It enables researchers and clinicians to simulate complex biological interactions, predict disease progression, and design or optimize treatment strategies or medical devices with improved accuracy and efficacy. The system's architecture allows for integration of various components, including real-time data processing, federated learning, and quantum computing enhancements. From personalized drug discovery and cancer therapies to synthetic biology and epidemiological analysis, this platform offers powerful tools for understanding and manipulating cellular systems and bioengineered systems. By bridging the gap between molecular-level interactions between cells and materials and organism-wide effects, it enables significant advancements in healthcare and biological sciences.

The invention discloses a brain disease classification method and system. Precise diagnosis is realized through multi-modal data fusion and dynamic modeling. The method comprises the following steps: collecting multi-modal brain image information and cognitive behavior information of a user; performing dynamic function connection analysis on the resting state functional magnetic resonance time sequence signal to obtain a time-varying brain network feature matrix, and performing white matter fiber bundle topology reconstruction on a structure connection matrix; constructing a four-dimensional correlation tensor by using the time-varying network features, the structural connection weights and the anatomical features through a neurodynamic model; performing multi-task learning on the four-dimensional correlation tensor based on a time-varying graph neural network model, and outputting a quantitative diagnosis result; and finally generating a clinical classification report integrating the individualized brain network remodeling target, the disease progress risk layering and the treatment response prediction. By dynamically fusing the structure and functional features, comprehensive characterization of the pathological mechanism of the brain disease is realized, and decision support with both accuracy and interpretation is provided for clinical diagnosis.

The invention discloses a nerve disease detection and analysis method and system, and the method comprises the steps: obtaining a bracelet collection signal, a sphygmomanometer collection signal, a movement behavior image and behavior test data, and extracting tremor intensity features, gait symmetry features and autonomic nerve rhythm features through multi-band decomposition of the bracelet collection signal; analyzing the motion behavior image and the standardized motion test to obtain a motion function score; carrying out heart rate variability analysis to identify a neural function abnormality mode; constructing a neural function state map and calculating a feature weight; predicting a disease progress trend in combination with historical monitoring data; and dynamically adjusting a prediction result through subsequent feedback correction information. Through a mode of combining short-time intensive monitoring and long-term intermittent acquisition, long-term trend prediction and dynamic correction based on initial data are realized, and the reliability and practicability of nerve disease risk assessment in a home scene are remarkably improved.

Systems and methods are disclosed for evaluating a patient with vascular disease. One method includes receiving patient-specific data regarding a geometry of the patient's vasculature; creating an anatomic model representing at least a portion of a location of disease in the patient's vasculature based on the received patient-specific data; identifying one or more changes in geometry of the anatomic model based on a modeled progression or regression of disease at the location; calculating one or more values of a blood flow characteristic within the patient's vasculature using a computational model based on the identified one or more changes in geometry of the anatomic model; and generating an electronic graphical display of a relationship between the one or more values of the calculated blood flow characteristic and the identified one or more changes in geometry of the anatomic model.

The invention discloses a glioma T cell prediction and prognosis evaluation method based on pathological images, particularly relates to the field of patient prognosis health evaluation, and aims to solve the problems that existing pathological evaluation is difficult to combine with tumor structure heterogeneity and immune infiltration distribution, and the future progress risk of a patient cannot be predicted based on follow-up visit pathological data. A spatial heterogeneity map of a tumor core area and an invasion edge is constructed in a digital pathological section, density gradients of T cells in different areas are calculated to generate a distribution heterogeneity coefficient, interaction processing is performed on the two types of characteristics in combination with historical follow-up visit records, and a time sequence neural network constructed based on a gating structure is combined to obtain a high-efficiency characteristic of the tumor. And outputting disease progress probabilities of a plurality of follow-up visit time points in the future to form a prognosis trajectory prediction curve, calculating a quantitative recurrence risk score according to curve slope change and an immune fluctuation mode, and finally generating an individualized management scheme, thereby realizing quantitative prediction evaluation and risk management of the prognosis trend of the glioma patient.

The invention belongs to the technical field of early warning of diseases, and discloses a knee joint disease early warning system based on biomechanical analysis, which comprises a data acquisition module, a data analysis module, a main control module and an early warning module, the knee joint disease early warning system based on biomechanical analysis has the expected effects that the knee joint disease early warning system based on biomechanical analysis is provided, and a patient is helped to discover and take intervention measures in the early stage of knee joint diseases through real-time monitoring and early warning, so that the disease progress is delayed, and the life quality of the patient is improved. Meanwhile, the system provides a powerful tool for clinicians and rehabilitators and is used for formulating personalized rehabilitation treatment schemes.

The invention discloses an analysis and prediction system based on disease data, which belongs to the technical field of disease data analysis and comprises a cross-modal distillation data fusion module, a time domain causal chain analysis module, a multi-level disease risk prediction module, a disease critical state real-time monitoring module, a self-adaptive intervention strategy generation module and a clinical collaboration and feedback optimization module. A time decay factor and a smoothing coefficient are introduced through the time domain causal chain analysis module, the causal strength between variables is updated in real time, nonlinear changes of disease progression are adapted, and individual risk scores are generated through the multi-level disease risk prediction module in combination with the dynamic weight and the time decay factor of the causal atlas. A spatial smoothing function is introduced to suppress the influence of an abnormal value, group risks are quantified through a modal mean value and a standard deviation, individual weights are dynamically adjusted through a feedback mechanism, the long-term stability of prediction is improved, causal strength and feature importance are used for joint modeling, and the problem that a traditional statistical model ignores a potential causal path is avoided.

The invention discloses a hypertensive heart disease early screening system fusing ultrasonic multiple parameters, which comprises the following steps: acquiring ultrasonic image data and physiological index monitoring parameters of a patient at different time points, performing difference comparison on heart structure parameters to generate time sequence change data, and establishing a multi-dimensional data fusion scheme based on the time sequence change data and physiological indexes; according to the fusion scheme, acute risk trigger factors are identified, blood flow abnormal parameters are generated, and emergency threshold comparison is carried out to generate acute risk early warning signals; extracting a myocardial strain degradation rate to determine a chronic degradation acceleration time point, carrying out disease progress stage association to generate a chronic risk layering curve, and establishing a clinical index comparison table; carrying out double-layer correlation on the acute risk early warning signal and the chronic risk layering curve, and carrying out calibration judgment to generate a comprehensive risk judgment sequence; the sequence is decomposed into an acute judgment layer and a chronic judgment layer, a risk threshold value is configured to generate an instant early warning signal, a layered screening report is formed, and technical support is provided for early discovery of the hypertensive heart disease.

A wearable electronic medical device that stores initial baseline biometric data in memory and determines patient-specific thresholds for monitored biometric parameters. Biometric sensors detect changes in these parameters in response to therapeutic treatment or disease progression, and exceeded thresholds activate actions such as patient notifications, changes in treatment, and transmission of data. Trusted receivers receive suggested diagnoses and treatment options determined by an AI-powered web crawler. The treatment includes electrocuetical and pharmaceutical treatment, and the physiological change includes an indication of a change in the cardiovascular condition. The device determines patient-specific thresholds by applying statistical weighting to monitored biometric parameters and determining an acceptable data set.

The invention discloses a medical image analysis processing method based on AI, and relates to the technical field of image analysis, and the method comprises the steps: collecting multi-modal medical image data of a patient from an image device, and carrying out the preprocessing of the multi-modal medical image data; fusing the preprocessed multi-modal medical image data, extracting biological tissue features, constructing a biological tissue feature library, and marking abnormal biological tissue features in the biological tissue feature library; according to a medical image analysis task, combining with the marked biological tissue feature library to train an AI visual detection model so as to identify and classify different abnormal biological tissues; and automatically detecting an abnormal biological tissue area in the image by using an AI visual detection model. According to the method, high-precision analysis can be carried out on medical images through the AI technology, tiny abnormal changes can be detected, the severity of lesions can be evaluated and the development trend of diseases can be predicted through quantitative analysis on image features, and the risk of disease progression can be timely found and reduced when the lesions do not cause obvious symptoms.

The invention belongs to the technical field of traditional Chinese medicine preparations, and particularly relates to a traditional Chinese medicine preparation for treating Parkinson's disease and a preparation method thereof. The traditional Chinese medicine preparation for treating Parkinson's disease comprises the following raw materials in parts by weight: 20-30 parts of bischofia polycarpa, 17-25 parts of eucommia ulmoides, 12-16 parts of artemisia sphaerocephala, 10-15 parts of lycopodium clavatum, 8-13 parts of elm leaves, 7-14 parts of pericarpium citri reticulatae, 5-10 parts of semen cuscutae and 2-7 parts of liquorice. In the formula, monarch, minister, assistant and guide are reasonably matched, the curative effect is exact, and the effects of regulating qi and blood, nourishing liver, warming kidney, activating blood and dissipating blood stasis are achieved. The compound can effectively improve core symptoms of Parkinson's disease and delay disease progression, is expected to be further developed into clinical drugs for treatment of Parkinson's disease, and provides an innovative scheme for traditional Chinese medicine treatment of neurodegenerative diseases.

A medical system useful in the determination of future disease progression in a subject. More specifically the present invention applies machine learning techniques to aid prediction of disease pathology and clinical outcomes in subjects presenting with symptoms of cognitive decline and to expedite clinical development of novel therapeutics.

The invention relates to the technical field of hepatic fibrosis treatment strategy optimization, in particular to a hepatic fibrosis treatment strategy optimization system and method based on deep reinforcement learning. The strategy generation module is in communication connection with the database module; the simulation module is in communication connection with the strategy generation module and is used for simulating the application effect of the treatment strategy through a simulation system; the evaluation module is in communication connection with the simulation module and is used for determining an effect value of treatment strategy application through the simulation module and transmitting the effect value to the strategy generation module; the scheme recommendation module is in communication connection with the strategy generation module and is used for recommending the treatment strategy to a doctor; the invention constructs a comprehensive, dynamic and intelligent hepatic fibrosis treatment decision support system. The system not only can process high-dimensional and non-linear medical data, but also can capture dynamic characteristics of disease progression, so that a major span from static decision to dynamic optimization is realized, and the overall efficiency and expandability of the system are greatly improved.

The invention discloses a data-driven early-diabetic future diabetes-related disease progress risk prediction model and a construction method thereof, and the method comprises the steps: collecting clinical index data, including age, gender, BMI, WHR, HOMA-IR, HDL-C, TG, SBP, DBP, SCR and ALT, of early-diabetic patients in a training and verification queue; using an unsupervised soft clustering method combining dimension reduction based on UMAP, graph clustering and a Gaussian mixture model to identify the phenotypic heterogeneity of the prediabetes mellitus; obtaining the probability of the individual phenotype characteristics, evaluating the association between the probability and the development risk of the future diabetes related diseases in the early stage of diabetes, and constructing a development risk prediction model of the future diabetes related diseases in the early stage of urine diseases; and performing model optimization and robustness verification in the verification queue. According to the method, the heterogeneity of the prediabetes mellitus can be effectively identified, and accurate risk stratification and personalized prevention are realized.

The invention provides a CKD nutrition management visual monitoring method and system based on big data, and relates to the technical field of data processing, and the method comprises the steps: carrying out the data preprocessing according to real-time health data, and obtaining a standardized health data set; according to the standardized health data set, evaluating nutritional requirements of the patient, and combining disease progression, individual differences and diet preferences of the patient to generate a personalized nutrition management scheme, the personalized nutrition management scheme including recommended diet, a nutrition supplement scheme and an intake behavior adjustment scheme; according to the personalized nutrition management scheme, a visual display interface is generated, and the visual display interface displays the nutrition state, diet suggestions, potential health risks and predicted effects of the patient; adjusting a personalized nutrition management scheme in real time according to feedback data and health state changes provided by the patient in the use process, and presenting an updated scheme through a visual display interface; the autonomy and accuracy of nutrition management are improved.

The invention relates to application of a DDX39B protein inhibitor in preparation of acute leukemia treatment medicines, and belongs to the technical field of biological medicines. In order to solve the problems of single acute leukemia treatment scheme and lack of novel medicines at present, the invention provides application of a DDX39B protein inhibitor in preparation of acute leukemia treatment medicines. Targeted inhibition of DDX39B can significantly hinder proliferation of leukemia cells, promote apoptosis and differentiation of cells, retard the process of cell cycles, delay disease progression in vivo and prolong the lifetime of model animals. On the basis of an RNA (Ribonucleic Acid) binding pocket structure of DDX39B, through high-throughput drug screening, the small molecule compound HMU-4051C is firstly identified as a DDX39B targeted inhibitor to effectively kill leukemia cells, and a new application of the DDX39B protein inhibitor in preparation of acute leukemia treatment drugs is developed.

The invention provides a clinical decision knowledge graph construction method and system, and the method comprises the steps: extracting standardized entities corresponding to diseases, symptoms and diagnosis and treatment elements from medical knowledge data; constructing a clinical concept knowledge graph for representing a medical concept logic relationship and a causal relationship according to the semantic association relationship and the causal dependency relationship among the standardized entities; a diagnosis event, an examination event and a treatment event related to the patient are extracted, link evidences among the events are determined based on the event chain relation among the events, and a clinical event knowledge graph used for representing the disease course evolution process of the patient is constructed according to all the link evidences; and performing knowledge element fusion based on an entity association relationship between the clinical concept knowledge graph and the clinical event knowledge graph, and generating a target knowledge graph for clinical decision analysis. By adopting the scheme of the invention, the cross-map fusion of the static medical concept knowledge and the dynamic disease course event chain relationship can be realized, and the clinical decision knowledge structure with the reasoning ability can be constructed.

Provided herein are methods and compositions for treating and / or inhibiting the development or progression of conditions caused by, or associated with, Herpes Simplex Virus (HSV) infection. In particular, provided are compositions comprising lopinavir alone, or lopinavir and ritonavir, methods for their manufacture; and the use of said pharmaceutical compositions as a medicament. In particular, pharmaceutical compositions are provided for use in treating and / or inhibiting the progression of HSV related conditions.

The application discloses an exosome small non-coding RNA molecule marker and application thereof, and belongs to the field of tumor liquid biopsy and molecular diagnosis. The exosome small non-coding RNA molecule marker is at least one of tRNA-GlyGCC-5 and sRESE; the nucleotide sequence of the tRNA-GlyGCC-5 is shown as SEQ ID NO:1; and the nucleotide sequence of the sRESE is shown as SEQ ID NO:2. The sRESE in the application is a brand new small non-coding RNA sequence, and is first discovered by the inventor, which is important for the marker of esophageal cancer and the research on disease progression mechanism. The tumor diagnosis kit provided by the application, which comprises the exosome small non-coding RNA molecule marker, is a liquid diagnosis monitoring kit with non-invasiveness, high sensitivity, high specificity and applicability to general survey or screening.

The invention relates to the technical field of medical image processing, and particularly discloses a lesion image generation method for ultrasonic diagnosis, which comprises the following steps of: 1, acquiring initial state data of a lesion; 2, obtaining intervention factor data; 3, performing feature coding on the initial state data and the intervention factor data to obtain an initial feature vector and an intervention feature vector; and 4, inputting the initial feature vector and the intervention feature vector into a pre-trained time sequence generation model, and generating a continuous image sequence of the focus changing along with time. By constructing the time sequence generation model fusing the multi-modal feature coding and the space-time attention mechanism, the continuous image sequence of the focus changing along with time can be accurately generated, the disease progress or treatment response process can be accurately simulated, and a powerful reference basis is provided for clinical prognosis evaluation.

The invention belongs to the technical field of biological medicine, and particularly relates to an extracellular vesicle drug delivery system, a preparation method thereof and application of the extracellular vesicle drug delivery system in treatment of Alzheimer's disease. According to the invention, one or more of stilbene glucoside, emodin, kaempferol, resveratrol and quercetin are used for regulating and controlling the expression of miRNA (micro Ribonucleic Acid) related to the disease progress in the extracellular vesicles, and the result shows that one or more of miR-let-7c-5p, miR-486-5p, miR-132-3p, miR-160b and miR-129-5p is remarkably up-regulated, and one or more of miR-342-3p, miR-16-5p and miR-125b-5p is remarkably down-regulated. The regulated extracellular vesicles can be used for treating Alzheimer's disease, relieving Tau protein abnormal phosphorylation, reducing amyloid protein beta deposition, inhibiting microglial cell activation, protecting neuronal cells and relieving memory deficits.

Implementations are presented for predicting likelihood of a patient progressing to an advanced stage of a particular medical condition, e.g., a known medical disease. The implementations provide a predictive model and a cluster analysis method. The predictive model and the cluster analysis method can be performed in parallel, or in sequence. Alternatively, only one of the predictive model and the cluster analysis method may be performed, for example, to increase the processing speed and reduce the use of hardware resources.

The invention discloses a short time sequence Alzheimer disease progress prediction method based on multi-relation mixed perception, and belongs to the field of progress prediction, and the method comprises the steps: providing a time sequence evolution generative adversarial network, enhancing the dynamic fitting capability of data evolution, and improving the generalization of a long time sequence learning model to a short time sequence. Secondly, a multi-relation mixed sensing module embedded with global noise filtering is designed, complex relations among subjects can be captured, and pairwise and high-order interaction relations can be efficiently learned. Specifically, the module realizes joint modeling of a pairwise relationship and a high-order relationship by constructing a noise filtering population graph and a population hypergraph. Then, the mixed representation of the multiple relationships is fed to an extended long-short term memory module for time series learning.

The invention relates to the field of intelligent detection, in particular to a periodontal bone loss positioning and grading system based on a deep learning CT image, and the system comprises the steps: obtaining image data of a periodontal panoramic film and an apical film, carrying out the classified storage, and marking key anatomical points; inputting the preprocessed image data into a deep learning model for training and verification to obtain a periodontal bone loss positioning detection model; and based on the key anatomical points positioned by the periodontal bone loss positioning detection model, calculating a typing evaluation ratio, and based on the typing evaluation ratio, carrying out quantitative typing and site deviation judgment on the periodontal bone loss. According to the method, the key anatomical points can be automatically identified and analyzed from the high-resolution CT slice image, and the diagnosis precision and efficiency are remarkably improved. According to the method, the severity of periodontitis can be quantitatively evaluated, the disease progress can be tracked in real time, and reliable data support is provided for formulating a personalized treatment scheme.

The invention discloses an early gastric cancer intelligent screening and risk layering method and system based on multi-modal data fusion and deep learning. The method comprises the following steps: acquiring a gastroscope image, a pathological image, a medical image and clinical data of a patient, performing standardization processing and forming unified feature representation; training the multi-branch neural network model group, and generating a virtual queue tag based on each mode; fusing a multi-modal queue division result to generate a patient comprehensive queue label; adaptively selecting a decision path according to the label, and executing feature fusion and analysis of corresponding depth; generating a structured report containing gastric cancer risk levels and individualized monitoring suggestions; and deploying the system and dynamically tracking the change of a patient queue to realize continuous monitoring of disease progression. Cross-modal semantic alignment, uncertainty evaluation and a Markov prediction model are introduced into the system, the screening accuracy, credibility and dynamic risk evaluation capability are improved, and the system is suitable for early screening and personalized management of gastric cancer.

The invention provides a pharmaceutical composition for treating Alzheimer's disease. The pharmaceutical composition is prepared from the following raw materials in parts by weight: 10-15 parts of ginseng, 10-15 parts of rhizoma acori graminei, 10-15 parts of polygala tenuifolia, 15-20 parts of rhizoma polygonati, 15-20 parts of ginkgo leaves and 5-10 parts of schisandra chinensis. Therefore, the ginseng and the ginkgo leaves jointly regulate a cholinergic system and cerebral blood flow, and the cognitive function is enhanced; the rhizoma acori graminei and the ginkgo leaves cooperate to inhibit aggregation of beta-amyloid protein, improve cerebral circulation and relieve neurotoxicity. Polygonatum sibiricum and Chinese magnoliavine fruit protect nerve cells through anti-oxidation and anti-inflammatory effects and reduce nerve injury. Schizandrin further stabilizes a neurotransmitter system and enhances the synergistic effect of other medicinal materials, a multi-layer treatment network of Abeta deposition inhibition, cholinergic system regulation, anti-inflammation and anti-oxidation and nerve protection is integrally formed, and the core pathological link of the Alzheimer's disease is comprehensively intervened, so that the cognitive function is improved, and the disease progress is delayed.

The present invention relates to use of expression vectors and other compounds in methods to disrupt the LInker of Nucleoskeleton and Cytoskeleton (LINC) complex, uncoupling the nucleus from its linkage to the cytoskeleton, resulting in amelioration of diseases caused by one or more Lmna mutations, so-called laminopathies. More particularly, the invention relates to the expression of dominant negative SUN domain protein and / or dominant negative KASH domain protein to disrupt, for example, the LINC complex in cardiomyocytes for suppressing disease progression in dilated cardiomyopathy (DCM).

Biomarkers can be assessed for a variety of uses, including screening, detection, diagnosis, prognosis, risk prediction, disease progression, recurrence, selection of treatment, therapy response, to evaluate a subject's health status, whether the subject presents with no evidence of disease, or a benign or malignant condition such as cancer. Compositions (antibodies, polypeptide and polynucleotide markers) and methods are provided herein, which find application in the early detection of cancer, in the early detection of disease relapse and in monitoring therapy response.

The invention discloses a generic cancer prognosis biomarker and an application thereof in evaluating the poor prognosis of the generic cancer, which can predict the outcome of a generic cancer type cancer, can be used as a reliable biomarker of a treatment target, and helps clinicians to predict the disease progression and survival rate of patients. The VANGL1 gene is a VANGL1 molecule and comprises a VANGL1 gene and a protein coded by the VANGL1 gene.

The invention provides a pediatric respiratory disease diagnosis system and method based on pattern recognition. The pediatric respiratory disease diagnosis method comprises the following steps: constructing a multi-modal pathological space by combining pathophysiological features of children in various medical institutions; performing breathing mode matching on the breathing time sequence data of the target patient and the subtype distribution of the breathing disease in the multi-mode pathological space to obtain pathological state constraints, and determining a pathological association relationship between the target patient and the known pediatric breathing disease according to the pathological state constraints and gray texture features of the lung medical image of the target patient; constructing a disease progress evolution graph of the pediatric respiratory disease; and carrying out map fusion on the disease progress evolution map and the pathology association relationship to obtain a multi-dimensional decision index of the respiratory disease of the target patient, and carrying out multi-modal identification on the respiratory disease of the target patient based on the multi-dimensional decision index to obtain a disease auxiliary identification report. By adopting the scheme of the invention, a comprehensive diagnosis system considering multi-center feature alignment and multi-modal information fusion can be constructed to identify pediatric respiratory diseases.