88 results about "Disease progression" patented technology

This invention relates to the field of medical information management technology, including an AI-based endometriosis management system. The system comprises a thickness dynamic monitoring module, an inflammation level assessment module, a lesion spread analysis module, a periodic lesion tracking module, and a disease progression analysis module. In this invention, AI is used to segment ultrasound image data, enabling dynamic monitoring of lesion areas. Lesion boundaries are extracted, and changes in local inflammatory marker concentrations are calculated, making the screening of abnormal inflammatory areas more accurate and enabling early identification of lesion development. The tracking of periodic lesions, combined with analysis of lesion contour changes and area increases / decreases, enhances the quantitative control of disease progression. The future development trend of lesions, combined with the calculation of periodic inflammatory pathways, makes the prediction of disease evolution more consistent with physiological changes. Multi-dimensional lesion analysis combined with AI intelligent calculation expands the diagnosis and treatment of endometriosis from static assessment to dynamic trend prediction, improving the adaptability of personalized treatment plans.

A method of treating male hypogonadism in a subject in need thereof includes administering to the subject testicular organoids generated from isolated male progenitor cells. The method can be used to slow or halt the progression of age-related diseases, and increase healthspan and longevity, in hypogonadal males. The method can be used with subjects that have not or are not receiving testosterone replacement therapy. The method does not require that the subject is pre-treated with CG. The testicular organoid includes cells expressing GFRA1, VEGF, VEGFR2, PDGF-Rα, Cyp11A1, 3β-HSD, 17β-HSD, LHCGR, α-SMA, CD11b, MHC CII, CD64, CD95, Sox9, AR, inhibin β-B, and FSHR.

This invention discloses a method for constructing a gene methylation diagnostic model for differentiated thyroid cancer (PTC). The method includes the following steps: S1, obtaining a test sample; S2, extracting and storing DNA from the test sample; S3, performing methylation analysis; S4, establishing a diagnostic model based on DNA methylation, where the model shows that patients with cg03596178, cg06033721, cg06688989, cg07209244, cg07485775, cg14484681, cg19979108, and cg20943461 are more likely to experience disease progression and are considered risk factors; S5, cross-validating and evaluating the performance to obtain the gene methylation diagnostic model. This model, by detecting and statistically analyzing specific methylation sites, can differentiate patients with recurrent or metastatic characteristics in differentiated thyroid cancer. The relevant model can assist in the clinical diagnosis and follow-up of thyroid patients.

This disclosure describes how to treat and / or improve pancreatic cancer using a novel proteomic profile of the pancreatic cancer. In some embodiments, the treatment includes removing cancerous tissue after taking a biopsy from the patient. Optionally, the treatment includes administering anticancer agents to suppress the progression of pancreatic cancer. In further or alternative embodiments, the novel proteomic profile is used to monitor disease progression or remission.

This invention relates to the interdisciplinary field of smart healthcare and digital twin technology, specifically to a dynamic medical twin system and interactive prediction method based on a physiological double helix driven and four-dimensional linkage modalities. The system continuously collects and fuses multi-source biophysical data through a physical entity helix, while a digital virtual helix solves a network of physiological equations coupled with metabolic and stress fields in real time. Both systems achieve endogenous synchronous mapping of gene loci and expressed proteins through a built-in base pairing engine. The four modalities of mirroring, deduction, intervention, and knowledge are responsible for high-fidelity real-time presentation, disease progression prediction, virtual intervention deduction, and clinical knowledge accumulation, respectively, forming a complete closed loop of "observation-deduction-intervention-learning." This invention elevates the system architecture from a static hierarchical stack to a dynamic life-body metaphor, supporting users to perform virtual operations on the twin and receive real-time feedback on multi-scale physiological responses across the entire system, achieving a leap from "morphological simulation" to "life mechanism simulation."

This invention relates to the fields of medical data analysis and artificial intelligence, proposing a dual-latent variable decoupled data analysis method based on plasma Netrin-1 and clinical scale features. First, plasma Netrin-1 test data and clinical characteristics such as UPDRS scale scores and disease duration are acquired from the subjects. Batch effect correction is applied to the raw test values, and an input feature vector is constructed. Then, a variational autoencoder model is established. By setting orthogonal constraints, Netrin-1 regression constraints, and disease duration regression constraints, the latent space is structurally constrained, enabling the model to learn latent variables D representing disease progression and C representing adaptive changes during training. Based on this, an efficiency index η composed of latent variables C and D is calculated, and relevant trend indicator parameters are obtained by combining longitudinal follow-up data. This method can achieve decoupled representation of disease-related features and adaptive change features in multi-source clinical data, providing a new data analysis tool for statistical analysis and research of neurodegenerative disease-related data.

This application relates to the field of speech signal processing technology, providing an acoustic feature processing method, apparatus, computer device, readable storage medium, and program product. The method includes: extracting acoustic features from speech data of healthy individuals and Huntington's disease patients to obtain acoustic feature sets; evaluating the stability of the acoustic features in the acoustic feature sets to obtain candidate acoustic feature sets; evaluating the significant independent contribution of the candidate acoustic features in the candidate acoustic feature sets to obtain multiple target acoustic features and their corresponding weights; performing weighted and scaled processing on the multiple target acoustic features based on their weights to obtain speech index features; and using the speech index features to characterize the severity of Huntington's disease. This method can integrate the multidimensional language impairments of Huntington's disease into clinically interpretable speech index features that are significantly correlated with the severity of Huntington's disease, improving the robustness of tracking Huntington's disease progression.

The application relates to application of a reagent for detecting expression levels of a PSIP1 gene or a coded protein LEDGF / p75 in preparation of a product for evaluating systemic lupus erythematosus disease activity, which makes up for the problems of insufficient sensitivity and specificity of existing evaluation indexes; the application also discloses a key role of the PSIP1 gene in a pathogenesis mechanism of systemic lupus erythematosus, and clearly shows the relationship between the PSIP1 gene in T cells and cell cycle arrest, cell aging and abnormal immune function, so as to provide a theoretical basis for target regulation of the PSIP1 to improve T cell function and delay or block disease progression; the application firstly proposes that the expression levels of the PSIP1 / LEDGF / p75 are detected to be used for evaluating systemic lupus erythematosus disease activity, auxiliary diagnosis, prognosis prediction and treatment effect monitoring, so as to provide a brand-new tool and direction for precise diagnosis, activity evaluation, prognosis judgment and future target treatment development of systemic lupus erythematosus.

This application relates to the field of computer technology and provides a method and related equipment for generating intelligent agents for diagnosis. The method generates a virtual object containing temporal pathological feature data based on a preset disease evolution template; controls an initial diagnostic agent to interact with the virtual object in multiple rounds to obtain a temporal diagnostic strategy output by the initial diagnostic agent; generates feedback results for the initial diagnostic agent based on the diagnostic strategy and the target diagnostic scheme corresponding to the virtual object; and optimizes the parameters of the initial diagnostic agent based on the feedback results to generate a target diagnostic agent. By constructing a disease evolution template, this application can generate virtual patient data with continuous temporal features at low cost and on a large scale, enabling the diagnostic agent to be exposed to the complete disease progression logic from the latent stage to the critical stage during the training phase, thereby improving the model's generalization ability.

A machine learning approach is herein provided for preparing a model for assessing the progression of a lung disease, comprises receiving a first set of segmented images of lungs from different patients with the lung disease. The first set of images is segmented and used to train the model. The trained model is applied to a set of unsegmented images to generate a second set of segmented images. The model is updated with the second set of segmented segmentation. From the model, at least one result associated with progression of the lung disease is outputted.

A novel CHI3L1 inhibitor provided in the present invention can efficiently inhibit or treat disease progression of nontuberculous mycobacteria-infected lung diseases by inhibiting CHI3L1. In particular, it has been identified that K284-6111 can inhibit or treat disease progression of nontuberculous mycobacteria-infected lung diseases by inhibiting CHI3L1, and by newly synthesizing derivatives of K284-6111, novel compounds having a CHI3L1 inhibitory ability superior to that of K284-6111 have been identified. Therefore, the novel CHI3L1 inhibitors provided in the present invention can inhibit or treat disease progression of nontuberculous mycobacteria-infected lung diseases by inhibiting CHI3L1.

Provided herein are engineered T-celi receptors (TCRs) having nanomoiar affinity for the immuno-dominant pp65 peptide residing between residues 495-503 (NLV) in complex with HLA-A2*02:01. The TCRs may be membrane-hound TCRs, soluble TCRs, chimeric TCRs, or chimeric antigen receptors. Also provided are methods of using the engineered TCRs to treat diseases, monitor disease progression, monitor vaccine efficacy, and detecting NLV / A2 presentation on the surface of cells.

The present application provides a kind of PRMT5 inhibitor, PRMT5 inhibitor includes: recombinant adeno-associated virus vector AAV-shPrmt5 and GSK3326595;Recombinant adeno-associated virus vector AAV-shPrmt5 includes the shRNA of targeting PRMT5, the nucleotide sequence of shRNA is as shown in SEQ ID No:1.The present application also provides the application of the above-mentioned PRMT5 inhibitor in the preparation of drug for improving primary biliary cholangitis, PRMT5 inhibitor is all PRMT5 gene / protein as target point, by inhibiting the expression of PRMT5 gene / protein, reduce liver tissue and spleen tissue Tfh cell and GC B cell, serum autoantibody and transaminase level, can effectively delay the disease progression of primary biliary cholangitis.

A user device configured to monitor use of an injection device and monitor disease progression. The user device may include a scanning device, a wireless transceiver, and a touch-sensitive display. The user device may be configured to: scan a package containing multiple injection devices or individual injection devices using the scanning device; transmit and receive a first set of signals representing data associated with the multiple injection devices or the individual injection devices using the wireless transceiver; receive user input via the touch-sensitive display confirming that the multiple injection devices or the individual injection devices are suitable for storage in a storage device; remind the patient to use a specific injection device on a specific date; and remind the patient to capture images of skin lesions, where each image is associated with a date stamp and a body area; compile data associated with the set of skin lesion images, date stamps, and related information; and transmit the data to a third party.

This application relates to a method, device, equipment, and medium for dynamic prediction of Alzheimer's disease course. The method includes: acquiring multidimensional detection data of a patient; determining the patient's disease subtype and disease stage based on the multidimensional detection data and clinical diagnostic criteria; performing time-series analysis on the multidimensional detection data according to the disease subtype and the disease stage to obtain the patient's disease progression pattern; predicting the patient's future disease trajectory using a preset disease trajectory prediction model based on the disease progression pattern to obtain the patient's Alzheimer's disease course prediction result; and generating a recommended intervention plan for the patient based on the Alzheimer's disease course prediction result and preset clinical intervention rules. This method enables dynamic prediction of the Alzheimer's disease course and provides personalized intervention suggestions for patients, improving clinical treatment outcomes.

The application belongs to the technical field of the Internet of Things and old-age health care, and particularly relates to a cognitive impairment chronic disease health management system and method based on the Internet of Things. The method is applied to a smart old-age care scene and comprises the following steps: a data acquisition step, in which an active acquisition and passive acquisition combination is adopted; a data transmission step, in which a 5G, WiFi and LoRa multi-network fusion transmission mode is adopted; an intelligent analysis step, in which a weighted fusion algorithm is adopted to fuse multi-source data and identify cognitive function abnormality features; a grading evaluation step, in which a four-dimensional weighted scoring model is used to calculate grading evaluation; and a grading intervention step, in which corresponding intervention measures are executed according to the grading evaluation result. The application realizes full-process automation of cognitive impairment chronic disease management, improves early screening coverage, delays disease progression, reduces care burden and improves the quality of life of the elderly.

The present application relates to the technical field of spinal disease management, and specifically discloses a spinal degenerative disease management path method and system based on big data, which comprises the following steps: collecting multi-source heterogeneous data of spinal degenerative disease in different departments and preprocessing, obtaining a standard data set of multi-source heterogeneous data and extracting structural features, generating a standard structural representation of spinal degenerative disease, setting an adaptive screening mechanism, screening and classifying the standard data set of spinal degenerative disease in combination with the standard structural representation to obtain a classified data set, constructing a knowledge graph of spinal degenerative disease, integrating new patient feature data, constructing a time series monitoring network model for reasoning and prediction, obtaining a patient disease progression trend and a potential risk vector, generating a personalized management path based on the disease progression trend and the potential risk vector output by the time series monitoring network model, which is conducive to improving the accuracy of spinal degenerative disease diagnosis and treatment and providing an adaptive disease management path scheme.

This invention relates to the technical field of nanomedicine synthesis and discloses a method for synthesizing nanomedicines for the treatment of rheumatoid arthritis, comprising the following steps: S10: obtaining or preparing high-purity, small-particle-size CaO2 nanoparticles; S20: encapsulating the CaO2 nanoparticles in a ZIF67 system to obtain CaO2@ZIF67; S30: dissolving methotrexate in methanol to obtain an MTX additive solution; dissolving CaO2@ZIF67 in methanol, then adding the MTX additive solution dropwise, and stirring thoroughly; finally, purifying, washing, and drying the obtained product to obtain CaO2@MTX@ZIF67. Targeting the two main pathological features of rheumatoid arthritis, namely inflammation and hypoxic microenvironment, this invention uses ZIF67-loaded calcium peroxide combined with methotrexate to treat the inflammatory sites of rheumatoid arthritis. The constructed product can inhibit the expression of tumor necrosis factor-α / interleukin-6, overcome the hypoxic nature of rheumatoid arthritis, and achieve the technical characteristics of alleviating inflammatory symptoms and altering disease progression, low cost, few side effects, and long-term treatment.

A cell-free DNA fragmentome method (DELFI-TF) is described to evaluate circulating tumor fraction during therapy. DELFI-TF rapidly determined disease progression in patients receiving immunotherapy.

This invention relates to the field of neural network construction technology, and more particularly to a method and system for constructing a dynamic prediction model for cerebrovascular diseases based on artificial intelligence. The invention first constructs a graph network model with continuous time slices and defines multimodal feature nodes. Based on the evolutionary correlation of historical data, pruning and sparsity constraints are applied to the fully connected matrix between layers to construct a sparse topology model. Subsequently, fact evolution nodes are activated based on patient clinical data, and reverse path backtracking is performed with these nodes as endpoints. A masking mechanism is used to block non-predetermined fact branches, locking the unique historical path. Finally, this path is transformed into a context vector and injected into subsequent layers to achieve model state reconstruction and dynamic weight updates. This invention, through a global pruning-path locking-dynamic reconstruction mechanism, effectively filters clinical noise in disease progression, solves the path drift problem of traditional time-series prediction, and significantly improves the accuracy of individualized predictions in the mid-to-late stages while greatly reducing the number of parameters.

The application discloses application of a group of clinical indexes in preparation of a product for subtyping of ARDS patients complicated with AKI, and constructs a Transformer model for subtyping of ARDS patients complicated with AKI and an ARDS patient complicated with AKI subtyping device based on the Transformer model in combination with the clinical indexes. The ARDS patient complicated with AKI subtyping device can realize accurate and automatic subtyping of ARDS patients complicated with AKI, effectively divides the ARDS patients complicated with AKI into three subtypes with different disease progressions, and then realizes individualized treatment of ARDS patients complicated with AKI with different disease progressions, and the subtyping result has excellent generalization and clinical interpretability.

This invention relates to the field of biomedical technology and provides the application of taquimod in the preparation of drugs for regulating lymphatic inflammatory responses. Through bioinformatics analysis and cell experiments, this invention demonstrates that taquimod can specifically inhibit the abnormally activated S100A9 / RAGE signaling pathway in lymphatic inflammation, significantly reduce the expression of inflammatory factors, and effectively restore the proliferation, migration, and tube formation function of lymphatic endothelial cells. This provides a novel drug treatment approach based on molecular mechanisms for treating diseases related to lymphatic inflammatory responses such as lymphedema. Furthermore, taquimod, as an oral formulation with proven safety in clinical trials, exhibits good patient compliance and is suitable for long-term use. Compared to existing symptomatic treatments, it holds promise for fundamentally interrupting the vicious cycle of inflammation that drives disease progression, and thus has significant clinical application potential.

The present disclosure describes an analyte monitoring system that tracks a host's glycemic control levels over time to determine disease progression. The analyte monitoring system may then administer a treatment to address the disease progression.

The application discloses an oral polypeptide preparation for preventing and treating rheumatoid arthritis. The application provides an oral polypeptide preparation for treating or preventing rheumatoid arthritis, and an active ingredient of the oral polypeptide preparation is a polypeptide shown in SEQ ID No. 1, and the polypeptide shown in SEQ ID No. 1 is loaded in dextran particles to form the oral polypeptide preparation. The oral polypeptide preparation can effectively inhibit secretion of inflammatory cytokines in a local rheumatoid arthritis, and delay or prevent disease progression. The oral polypeptide preparation has important application prospects in treating rheumatoid arthritis.

This invention discloses the application of fruquintinib combined with PD-1 monoclonal antibody in maintenance therapy for HER-2 negative advanced gastric cancer / geoesophageal junction adenocarcinoma, belonging to the field of tumor treatment technology. This invention targets HER2-negative advanced gastric cancer or gastroesophageal junction adenocarcinoma, specifically patients who achieve disease control after 3 months of first-line induction therapy with PD-1 monoclonal antibody combined with oxaliplatin, followed by maintenance therapy with fruquintinib combined with PD-1 monoclonal antibody without chemotherapy. The maintenance therapy regimen provided by this invention improves patients' quality of life and prolongs overall survival while delaying disease progression, and has significant clinical implications.

The application relates to the field of biomedical technology, and discloses a Parkinson disease subtype staging inference system based on a corneal nerve image and a SuStaIn algorithm, which comprises an image data acquisition module, a health control module, an abnormality division module, a SuStaIn model construction module and an output module. The image data acquisition module acquires an original image data set, and extracts core effective features after preprocessing the original image data set. The health control module calculates the Z scores of each feature in the corresponding core effective features of Parkinson disease patients through a standardization method. The abnormality division module divides abnormality grades based on the calculation results of the Z scores, and constructs a feature event matrix. The SuStaIn model construction module constructs a disease progression model based on the feature event matrix, inputs the patient feature Z score matrix, sets related parameters, completes model training, and then determines the optimal subtype number through multi-index evaluation. The output module outputs the model prediction results. The application realizes accurate identification of Parkinson disease patient subtypes and objective inference of disease stages, and provides strong support for early diagnosis, individualized treatment and drug research and development of diseases.

The present disclosure provides a method of determining a condition of a biological tissue sample. The method comprises receiving a target image depicting spatial single-cell omics data of cells in the tissue sample, constructing a multicellular network of interconnected nodes where each node represents a cell in the target image, enumerating subgraphs within the multicellular network to identify a set of spatial motifs where each spatial motif represents a multicellular pattern of interconnected cells of specific types, analyzing the set of spatial motifs to generate one or more motif-based features characterizing the biological tissue sample, and applying one or more trained machine learning models to the motif-based features to predict the condition of the biological tissue sample. The spatial motifs may include discriminative motifs that distinguish between different biological conditions such as cancer metastatic potential, survival prognosis, treatment response, or disease progression.

This invention discloses a graph convolutional neural network (GCN) disease prediction system based on multimodal brain MRI images, belonging to the field of medical image analysis and disease prediction technology. The system includes: an image preprocessing module that acquires and standardizes raw functional and structural magnetic resonance imaging (fMRI) data to generate a corresponding dataset; a dynamic supernet construction module that extracts spatiotemporal fusion variability features of brain regions; a multimodal fusion module that fuses features and constructs an adjacency matrix; a GCN prediction module that calculates prediction results such as disease progression grading; and a results visualization module that locates abnormal brain regions and generates a clinical reference report. This invention integrates multimodal brain MRI data from functional and structural magnetic resonance imaging to build a full-process disease prediction system. Through various algorithms, it mines the spatiotemporal dynamic characteristics of brain regions and integrates multimodal features to achieve dual-task prediction of Alzheimer's disease progression grading and brain age. Furthermore, through standardization processing and feature screening, it generates clinical reports to assist in disease diagnosis and treatment.

The application belongs to the technical field of biological medicine, and particularly relates to application of VSIG4 as a biomarker in evaluation of risk of lung fibrosis progression. The application first discovers and verifies that VSIG4 is significantly highly expressed in ILD patients with active inflammatory response, and the high expression is closely related to a signal of continuous progression of the disease by integrating peripheral blood exosome proteomics data and lung tissue single-cell transcriptome data. Based on the discovery, the application provides a kit and a detection method for detecting a VSIG4 protein level in peripheral blood. Data show that the PPF patients can be effectively identified by detecting the content or level of the exosome protein VSIG4 in peripheral blood, and the method has high sensitivity and specificity. The application provides a new non-invasive solution for early risk prediction and disease progression monitoring of lung fibrosis, and has important clinical value.