23 results about "Therapy response" patented technology

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 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 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.

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.

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.

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.

The invention provides a method and system for predicting time sequence evolution of a brain metastasis tumor stereotactic radiotherapy curative effect, and the method comprises the steps: a time sequence image generation step: inputting the data information of a target brain tumor region into a generative adversarial network, and obtaining a time sequence image corresponding to the data information; a fusion prediction step: extracting image features of the time sequence image to form an image feature sequence, and fusing the image feature sequence with text features extracted by medical text description information in the data information to obtain a fused feature sequence; and inputting the fusion feature sequence into a learning model, and outputting a probability sequence with image content change characteristics at each time point in a future time sequence. According to the method, high-reality and coherent multi-modal MRI prediction images at a plurality of time points in the future can be generated, and a dynamic visual basis is provided for evaluating treatment response.

The invention provides an intelligent medicine taking management system, which relates to the technical field of medical management, and is characterized in that a self-adaptive medicine taking plan is generated based on an AI prescription analysis engine, an electronic medical record and real-time physiological parameters, and monitor data and medicine taking behaviors are collected in real time by using a multi-protocol adapter; and tracking therapy and dynamically updating a medication plan based on the dynamic drug therapy response index, including normalizing multi-source data to generate a standardized medical score, dynamically distributing weights through an attention mechanism, and weighting to generate the dynamic drug therapy response index. The problems that real-time fusion analysis of multi-source data is difficult, the medication plan cannot be updated in time to adapt to the actual situation of the patient, and efficient and intelligent patient medication management is difficult to achieve are solved.

The invention provides Salivary Biomarker Panel for Therapeutic Monitoring and Relapse Prediction in Head and Neck Cancers, comprising synergistic combination of miR-1307-5p, CD44v6, KRT4, and PD-L1 quantified from salivary extracellular vesicles. Biomarker expression values are processed in which fold-change thresholds are computed, individual ChemoScore, RelapseScore, and ImmunoScore are derived, and patients are stratified into therapeutic response, relapse risk, immunotherapy suitability, and prognostic categories. Individual scores are further integrated into Overall Risk Score to classify patients into low, moderate, high, or very high risk. Limitations of tissue biopsies and delayed imaging-based assessments are overcome, enabling repeatable, real-time molecular monitoring to support personalized treatment strategies. The panel embodies into diagnostic kit comprising reagents, primers, reference controls, integrated scoring system, facilitating standardized detection and interpretation for dynamic clinical decision-making in head and neck oncology.

The invention relates to a method for predicting the response to an anti-cancer treatment of patients suffering from cancer, said method being based on the detection of biomarkers. The invention also relates to methods of treatment and methods for selecting a cancer patient to be treated or methods for selecting a suitable therapy for a cancer patient based on the detection of said biomarkers.

Methods and compositions disclosed herein generally relate to methods of identifying, validating, and measuring clinically relevant, quantifiable biomarkers of diagnostic and therapeutic responses for blood, vascular, cardiac, and respiratory tract dysfunction, particularly as those responses relate to septic shock in pediatric patients. Certain aspects of the disclosure relates to identifying one or more biomarkers associated with septic shock in pediatric patients in combination with one or more endothelial-derived biomarkers, obtaining a sample from a pediatric patient having at least one indication of septic shock, then quantifying from the sample an amount of said biomarkers, wherein the level of said biomarker correlates with a predicted outcome.

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 invention relates to the technical field of medical data processing, in particular to a liver cancer prognosis evaluation method and system based on multi-modal data, and the method comprises the steps: collecting and preprocessing the multi-modal data of a liver cancer patient before and after treatment, the multi-modal data including MRI image data, clinical feature data and metabonomics data; inputting the MRI image data into a first prediction model constructed based on a Transform encoder, and extracting image features in the first prediction model; inputting the image features, the clinical feature data and the metabonomics data into a second prediction model constructed based on a feature fusion module to obtain fusion features; and inputting the image features and the fusion features into a therapeutic response mode prediction model for feature decoupling, and outputting a therapeutic response mode prediction result. Compared with the prior art, more comprehensive and accurate prognosis evaluation is realized based on multi-modal data.

The present invention provides a computer-implemented method for predicting the treatment response of a mammalian cancer patient to anti-cancer immunotherapy, the method comprising: a) providing a plurality of features comprising gene expression levels of at least 8 of the following cancer promoting genes PTGS2, CXCL1, CXCL2, CXCL5, CXCL6, IL1A, HAS2, IL11, IL1B, EREG, IL6, CXCL8 (IL8), MMP12, NRG1, VCAN, AREG previously measured in a sample obtained from the tumour of a mammalian cancer patient; b) computing a score using the features provided in a); and c) predicting the treatment response of the mammalian cancer patient based on the score computed in step b). Also provided are related methods and systems for predicting treatment response.