59 results about "Therapy response" patented technology

A federated distributed computational system enables secure biological data analysis and genomic medicine through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates classical numerical simulations with machine learning models for biological system analysis, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for biological data analysis and privacy preservation protocols. The system implements cellular machinery assembly analysis, real-time patient data integration, and multi-modal image integration with spatiotemporal health data annotation. Through a distributed graph architecture, the system enables cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration with adaptive dimensionality control. The system implements real-time therapeutic response prediction through multi-modal data analysis, enabling research institutions to collaborate on complex biological analyses while maintaining strict data privacy controls.

Methods and systems for performing a clinical prediction are provided. In one example, the method comprises: receiving first molecular data of a patient, the first molecular data including at least gene expressions of the patient; receiving first biopsy image data of the patient; processing, using a machine learning model, the first molecular data and the first biopsy image data to perform a clinical prediction of the patient's response to a treatment, wherein the machine learning model is generated or updated based on second molecular data including at least gene expressions and second biopsy image data of a plurality of patients; and generating an output of the clinical prediction.

The invention discloses an endometrial cancer nursing treatment response prediction method based on multi-dimensional pathology assessment, and relates to the technical field of intelligent diagnosis prediction, and the method comprises the steps: collecting and standardly managing patient information data, and constructing a dynamically updated and quality-controllable multi-modal clinical pathology database. Performing multi-scale processing on the image and immune features, extracting tumor structures and immune cell distribution, and constructing a structured immune feature database; and multi-modal data are fused, an intelligent evaluation model is established, nursing treatment response is predicted, and dynamic optimization of the model is realized through clinical feedback. According to the invention, a standardized and multi-modal clinical pathology database is constructed, image, immune and molecular features are fused, multi-scale spatial information is accurately extracted, and a high-precision intelligent prediction model is established. And by introducing a dynamic feedback mechanism, the model can be adaptively optimized, and the accuracy and clinical practicability of nursing treatment response prediction are remarkably improved.

An automated adaptive radiotherapy system based on machine learning for designing and adapting personalized dosing plans and a corresponding system, the system comprising: • a data acquisition module to collect multimodal patient data such as anatomical images, genomics, physiological signals and electronic health records; • a preprocessing department that performs the normalization, alignment, segmentation and transformation of the acquired data into data structures suitable for predictive modeling; • a dose prediction engine containing at least one machine learning model trained to predict personalized three-dimensional radiotherapy dose distributions and dose-volume histograms from the preprocessed data; • an adaptation module to receive updated clinical data and automatically adapt the dosing schedule to intra-fraction and inter-fraction changes in patient anatomy and response to treatment; • a clinician dashboard that displays predicted dosing schedules, allows clinician interaction, and can make the model interpretable through explainable AI; • a data security and compliance layer that protects data through encryption, access control, and regulatory compliance.

The invention relates to the technical field of medical image analysis, in particular to an image-based tumor curative effect prediction method and system, and the method comprises the following steps: based on CT image data of multiple time points before and after treatment, extracting inter-frame pixel position coordinates of the CT image data, calculating an offset vector of an inter-frame pixel position, and implementing inter-frame position calibration; and generating time sequence image correction data. According to the method, accurate calibration of inter-frame positions is achieved through inter-frame pixel position coordinate extraction and offset vector calculation of multi-time-point CT image data, and through a set expansion threshold value and area boundary fitting, the outer contour of a tumor area is recognized, and a pixel point coordinate set of the tumor area boundary is dynamically analyzed, so that accurate calibration of the tumor area is achieved. According to the method, the position change of the tumor in the time sequence is evaluated, key data and density distribution characteristic analysis are provided for predicting the treatment response of the tumor, evaluation of the structural uniformity of the tumor area is supplemented, a basis is provided for individualized treatment prediction, and the change trend of tumor treatment can be effectively predicted.

Subject response to one or more headache preventive medications is predicted using one or more machine learning models trained on training data to predict treatment response to a particular headache preventive medication based on features in subject health data. Subject health data and one or more machine learning models are accessed with a computer system. The subject health data includes at least one of headache questionnaire data received from the subject or subject symptom data received from the subject. The subject health data are input to the one or more machine learning models to generate classified feature data that indicates a likelihood of the subject having a positive response to the particular headache preventive medication associated with each of the machine learning models.

The present invention relates to a system and a method for measuring and analyzing minimal residual disease (MRD) in pediatric B-cell precursor acute lymphoblastic leukemia (B-ALL) using multiparameter flow cytometry (MPFC). The invention finds application in clinical diagnostics and hematology-oncology for quantifying MRD in B-ALL patients with high sensitivity and specificity, needed for risk stratification, monitoring treatment response, and informing therapeutic decisions. The system comprises interconnected subsystems including an acquisition subsystem with an MPFC instrument, a control and file generation subsystem, and an analytical subsystem. The analytical subsystem incorporates modules for sequential data reduction, automated data cleaning, automated unsupervised data clustering, and interactive cluster analysis. Key advantages include high MRD detection sensitivity (e.g., 10⁻⁵ or 0.001%) and high specificity, without reliance on reference samples or supervised machine learning models, making it applicable in laboratories with different measuring equipment and using different panels of antibodies for identification of leukemic cells.

The invention concerns methods for predicting changes in a tissue, comprising obtaining snap shot information on individual cells of at least one cell type in a section of a tissue biopsy, wherein the information comprises: (i) spatial localization, (ii) cell type and (iii) cell division state at a specific point in time; calculating cell division probabilities for each of said individual cells; determining cell population dynamics based on the cell division probabilities and predicting future changes in the tissue based thereon. Particularly, the method concerns predicting tissue dynamics in cancer biopsies relevant for prognosis, and assessment of response to therapy.

The disclosure relates to methods of detecting Host Cell Proteins (HCPs) in biopharmaceutical samples containing a drug product. More specifically, the disclosure relates to methods of assaying HCPs in biopharmaceutical samples using enzymatic digestion protocols, liquid chromatography (LC) gradients, immunoaffinity capture protocols, and High-Resolution Mass Spectrometry (HRMS) to enhance the recovery, detection, and profiling of HCPs, which can impact the efficacy, shelf-life, and therapeutic response of drug products.

The application relates to the technical field of drug efficacy prediction, in particular to a multi-modal data prediction model for diabetes gene therapy response, which comprises an unmedicated data collection module, a medicated data collection module and a data prediction module.The unmedicated data collection module collects unmedicated data samples of a user; the medicated data collection module collects data samples of the user after taking medicine; the data samples comprise explicit data and implicit data; the explicit data is body information and medicine information related to blood glucose change; and the implicit data is blood glucose values corresponding to the explicit data; the data prediction module inputs fused features into a convolutional network model to generate drug efficacy prediction values.The application generates a prediction residual corresponding to the explicit data after taking medicine through a blood glucose value prediction module, identifies blood glucose fluctuation characteristics caused by non-drug factors by using the prediction residual, separates the net influence of medicine on blood glucose, and thus improves the accuracy of drug efficacy prediction.

Described herein are gene panel compositions and methods of use thereof for spatial omic analysis in biological samples. The gene panels may be organized into distinct modules that target various aspects of immune response, tumor biology, and intercellular communication within the microenvironment. These methods involve spatial omic techniques, such as spatial transcriptomics, to localize gene expression across tissue sections, providing detailed insights into immune cell infiltration, immune checkpoint activation, and tumor-immune interactions. The disclosed compositions and methods are applicable for diagnostics, monitoring therapeutic responses, and guiding personalized treatment strategies. The modular design of the gene panels allows for customization based on specific research or clinical objectives.

A system for monitoring arterial pressure of a patient determines a score that is predictive of responsiveness of the patient to a therapy. Sensed hemodynamic data representative of an arterial pressure waveform of the patient are received by a hemodynamic monitor. Magnitude data and trend data are derived from the hemodynamic data. The score that is predictive of the responsiveness of the patient to the therapy is determined based on the magnitude data and the trend data of the hemodynamic parameter. A representation of the score is output.

The present disclosure relates to methods for determining whether a subject with cancer, specifically metastatic colorectal cancer, will respond to treatment using bevacizumab and a fluoropyrimidine chemotherapeutic agent and / or predicting overall survival. The disclosure also relates to methods for selecting a treatment for cancer in a subject and compounds used in said treatment for cancer in said subject. The disclosure also relates to kits that can be used for these methods.

An electroencephalography (EEG) system comprises: an EEG device; a display; one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for: receiving measurement data; extracting, from the measurement data, a first set of features and a second set of features; inputting the first set of features and the second set of features into a first treatment-specific machine-learning model and a second treatment-specific machine-learning model, respectively; generating a data structure based on the predicted treatment responses; and rendering, on the display, the generated data structure to provide the predicted treatment responses to the first candidate treatment of the CNS disease and the second candidate treatment of the CNS disease.

High levels of nuclear NMT1 are associated with longer relapse free survival in ERα positive breast cancer patients. Both low levels of cytosolic and nuclear NMT1 correlated to very poor clinical outcomes. NMT2 also plays an important function in breast cancer signalling, regulated through phosphorylation. For example, NMT2 phosphorylation status is a key element in the progression of ER+ breast cancer cells. Specifically, nuclear localization of NMT2 is associated with poor outcomes in breast cancer patients.

The invention relates to the technical field of drug effect prediction, in particular to a multi-modal data prediction model for diabetes gene therapy response, which comprises an untaken medicine data acquisition module for acquiring a data sample of untaken medicine of a user; the medicine taking data acquisition module is used for acquiring data samples after the user takes medicine, the data samples comprise dominant data and recessive data, the dominant data are body information and medicine taking information related to blood glucose changes, and the recessive data are blood glucose values corresponding to the dominant data; and the data prediction module inputs the fusion features into a convolutional network model to generate a drug effect prediction value. The prediction residual error corresponding to the dominant data after medicine taking is generated through the blood glucose value prediction module, the blood glucose fluctuation characteristic caused by non-medicine factors is recognized through the prediction residual error, the net influence effect of the medicine on blood glucose is separated, and therefore the medicine effect prediction accuracy is improved.

An adaptive radiotherapy system (100) with biophysical real-time monitoring of the tissue response, wherein the system (100) comprises: a module (1) for real-time biophysical tissue response monitoring, configured to acquire live data on the physiological response and tissue response from a treatment area of ​​a patient during irradiation; a module (2) for localizing patient anatomy and motion detection, configured to detect position changes, organ movements and target shifts in real time; an engine (3) for adaptive treatment planning and dose recalculation, configured to dynamically update a radiotherapy treatment plan based on data received from the module (1) for real-time biophysical monitoring of tissue response and from the module (2) for localizing patient anatomy and motion detection; an engine for predictive modeling of tissue response (4) configured to estimate short-term tissue response, tumor response and normal tissue tolerance using patient data from current and previous sessions; an interface for controlling and modulating the beam (5) which is functionally coupled to a radiation delivery device and configured to modify one or more beam parameters in real time; a safety check and treatment lock control (6) configured to compare current treatment conditions with one or more allowable thresholds and selectively interrupt, stop, or release radiation delivery; and a module (7) for clinical review, data logging and treatment evaluation, configured to store treatment response data, generate session-related adjustment protocols and provide one or more recommendations for subsequent fractions, wherein the system (100) adaptively modifies the radiation output in response to continuously monitored biophysical tissue conditions of the patient.

To provide methods, systems and apparatuses for classifying a patient as a responder or a non-responder.SOLUTION: A method comprises: determining first gene data associated with a plurality of genes; determining second gene data associated with the genes, where the genes are sequenced from a plurality of tumor samples, where each of the tumor samples is labeled as a responder or a non-responder; determining, on the basis of the first gene data and the second gene data, a plurality of features for a predictive model; training, on the basis of a first portion of the second gene data, the predictive model according to the features; testing, on the basis of a second portion of the second gene data, the predictive model; and outputting, on the basis of the testing, the predictive model.SELECTED DRAWING: Figure 1

The invention discloses a child antinuclear antibody positive immune thrombocytopenia patient database establishment method and device. The child antinuclear antibody positive immune thrombocytopenia patient database establishment method comprises the following steps: acquiring a child patient basic screening data set and child patient subdivision clinical data provided by multiple centers; acquiring a structured full-dimensional data set of the child patient; carrying out data standardization and unified coding processing on the data so as to form a child patient standardization data set; performing permission setting distribution on each piece of data in the child patient standardized data set to form an encrypted security data set; and constructing a child antinuclear antibody positive immune thrombocytopenia patient database according to the encrypted security data set. According to the database established by the method disclosed by the invention, immune characteristics such as antinuclear antibody spectrum, anti-ENA spectrum, complement, lymphocyte subpopulation and the like as well as platelet level, clinical manifestation and treatment reaction are comprehensively recorded, so that a doctor can pay attention to immunology abnormality of a patient earlier during recording.

The present invention relates to a method for evaluating or predicting a therapeutic response to treatment with an IL7R modulator, such as an IL7R antagonist or agonist, in a patient, and more particularly to the identification of biomarkers for evaluating or predicting whether an IL7R modulator is effective in treating a patient. The present invention also relates to a method for screening for a compound that is effective in treating a patient. The biomarkers are BCL2, CISH, SOCS2, FLT3LG, PTGER2, and DPP4.

An electroencephalography (EEG) system comprises: an EEG device; a display; one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for: receiving measurement data; extracting, from the measurement data, a first set of features and a second set of features; inputting the first set of features and the second set of features into a first treatment-specific machine-learning model and a second treatment-specific machine-learning model, respectively; generating a data structure based on the predicted treatment responses; and rendering, on the display, the generated data structure to provide the predicted treatment responses to the first candidate treatment of the CNS disease and the second candidate treatment of the CNS disease.

The present invention relates to an isolated peptide comprising an active fragment derived from an integrin alpha or beta subunit and capable of specific binding to the extracellular matrix of a target organ or a target cell or a target tissue in said target organ. The targeted delivery system constructed using the isolated peptide of the present invention exhibits enhanced tissue specificity, enabling precise delivery to the target organ, enhancing the therapeutic response by concentrating the therapeutic payload at the site of action, while reducing the required dosage and mitigating systemic side effects.

The invention discloses a cloud-based linkage type remote collaborative consultation treatment method for acute and critical diseases of plateau medicine, and particularly relates to the technical field of artificial intelligence. A multi-parameter acquisition terminal is deployed, plateau specific physiological indexes of a patient are acquired, altitude environment parameters are acquired and encrypted and transmitted to a cloud, and a doctor logs in a cloud platform; the system receives multi-dimensional data of a patient, analyzes the uploaded data by using an intelligent analysis module in a cloud platform, constructs an immersive remote consultation environment in combination with a large-model scientific research inquiry function, a recommended treatment scheme and emergency treatment measures, and enables experts to communicate and analyze with attending doctors in a video conference mode. The method comprises the steps that a patient is monitored, the condition of the patient is discussed, a treatment scheme is formed, in the treatment process, vital signs and the condition of the patient are uploaded to a cloud end in real time, a doctor carries out accurate adjustment according to the condition change of the patient and treatment response, and all treatment records and patient data are stored and synchronized through a cloud platform.

The invention provides a liver cancer prognosis evaluation method, system and equipment, a medium and a program product, and relates to the field of intelligent medical treatment. The invention discloses heterogeneity of liver cancer cell subpopulations, in particular characteristics and molecular mechanisms of MVI-related cell subpopulations (such as Hep-C4); a specific cell subset related to poor prognosis of a liver cancer patient is identified, the effect of the specific cell subset in tumor growth and metastasis is explored, metabolic reprogramming of liver cancer cells, particularly the effect of a methionine metabolic pathway in promotion of tumor cell invasion and metastasis, is researched, and a theoretical basis is provided for metabolic intervention of liver cancer. Potential molecular markers (such as a combined marker of STMN1 and EZH2) are identified and used for prognosis evaluation and treatment response monitoring of liver cancer patients, and personalized medical treatment and precise treatment can be achieved.

Clinical decision support systems and methods to predict clinical response of a target tumor to immune checkpoint blockade therapy (ICB), by obtaining a training dataset comprising training transcriptome records of tumor tissue samples of a plurality of known responders and a plurality of known non-responders to ICB; performing deconvolution of the training dataset to identify differentially expressed genes (DEGs) in the training dataset, wherein the DEGs are treated as features of the training dataset and associated responder, non-responder status is treated as a labels of the records in the training dataset; performing regression analysis of the features to select a subset of the features as most predictive of the responder or non-responder labels and associated feature weights; incorporating the subset of features and associated feature weights in the response estimation model; receiving transcriptome data of a target tumor tissue sample; processing the transcriptome data using the generated response estimation model to generate an estimated response metric indicating a likely response of the target tumor to ICB.

A clinical decision support system and method for predicting a clinical response of a target tumor to an immune checkpoint blocking therapy (ICB) by: obtaining a training dataset, the training dataset comprising training transcriptome records of tumor tissue samples of a plurality of known responders and a plurality of known non-responders to the ICB; deconvolution is carried out on the training data set to identify differential expression genes DEG in the training data set, the DEG is regarded as features of the training data set, and related responder and non-responder states are regarded as labels recorded in the training data set; performing regression analysis on the features to select a feature subset which can predict the responder tag or the non-responder tag and related feature weights; incorporating the feature subset and the associated feature weight into a reaction estimation model; receiving transcriptome data of the target tumor tissue sample; the transcriptome data is processed using the generated response estimation model to generate an estimated response indicator indicative of a possible response of the target tumor to the ICB.

The invention discloses a CAR-T therapy response prediction model optimization method and system based on interpretability analysis. The method comprises the following steps: acquiring a trained CAR-T therapy response prediction model; performing global interpretability analysis on the trained CAR-T therapy response prediction model to obtain a global analysis result; performing local interpretability analysis on the trained CAR-T therapy response prediction model to obtain a local analysis result; and performing model optimization on the trained CAR-T therapy response prediction model based on the global analysis result and the local analysis result to obtain an optimized prediction model. According to the method, global interpretability analysis and local interpretability analysis are performed on the trained CAR-T therapy response prediction model, and model optimization is performed on the trained CAR-T therapy response prediction model based on the analysis result, so that the interpretability and prediction accuracy of the CAR-T therapy response prediction model are improved.

Embodiments described herein provide methods and systems for predicting a subject response to a therapeutic. The methods and systems generally operate by using a machine learning (ML) component trained using time-series responses and image features to generate time-series responses of a subject to a therapeutic that has been administered to the subject.

The invention relates to an integrated multimodal artificial intelligence platform designed to function as an autonomous hospital system providing end-to-end health prevention, screening, diagnosis, treatment optimization, and longitudinal digital-twin-based monitoring. The platform introduces a closed-loop clinical architecture that continuously analyzes heterogeneous patient data including radiology, pathology, genomics, laboratory findings, longitudinal clinical records, wearable streams, and environmental exposures. A multimodal transformer (MT-X) generates a unified patient-specific representation, enabling high-precision diagnostic and prognostic inference. A novel Autonomous Screening Interval Generator (ASIG) dynamically determines individualized screening schedules based on calibrated risk models and temporal disease-evolution forecasting. A Digital Twin Engine (DTE) simulates tumor progression, metastasis probability, toxicity trajectories, and therapy response. An Adaptive Therapy Optimization Engine (ATOE), based on reinforcement learning, identifies optimal treatment strategies tailored to patient biology and system-level constraints. The invention is industrially applicable to hospitals, centers, national screening programs, tele-networks, and Al-enabled health systems. The integrated nature of the invention, the closed-loop framework, and the combination of digital-twin simulation with intelligent screening and therapy design constitute a substantial improvement beyond conventional medical Al solutions.

For prediction of response of radiation therapy, radiomics are used for unsupervised machine training of an encoder-decoder network to predict based on input of image data, such as computed tomography image data and from segmentation. The trained encoder is then used to generate latent representations to be used as input to different classifiers or regressors for prediction of therapy responses, such as one classifier to predict response for an organ at risk and another classifier to predict another type of response for the organ at risk or to predict a response for the tumor.