83 results about "Drugs response" patented technology

A computer-implemented system for personalized health optimization constructs a confidence-weighted personal health knowledge graph (PHKG) from heterogeneous data, including wearable sensors, medical devices, lab results, medication logs, and conversational inputs. A multi-stage causal-inference stack identifies modifiable drivers of outcomes using layered methods (e.g., MI, GAM, Neural Granger, DAG-GNN), and simulates candidate interventions. A recommendation engine ranks lifestyle or pharmacologic actions using a benefit-to-friction score, selecting a personalized intervention aligned with user readiness and clinical safety constraints. Interventions may include a minimum effective dose (MED), optimal level, adaptive low-dose, or behavioral challenge. Optional modules include reinforcement learning for timing adaptation and privacy-preserving on-device inference. The system operates across domains including metabolic, cardiovascular, renal, sleep, stress, and medication response, enabling cross-condition synergy evaluation. The architecture is modular, supports runtime plug-in targets, and adapts in real time with or without continuous clinical oversight, depending on deployment.

A federated distributed computational system enables secure drug discovery and resistance tracking through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates molecular dynamics simulations with machine learning models for drug discovery 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 molecular dynamics simulation and resistance pattern detection. Through a distributed graph architecture, the system enables real-world clinical data integration, resistance evolution tracking, and multi-scale tensor-based analysis with adaptive dimensionality control. The system implements real-time drug response prediction through multi-modal data analysis, enabling pharmaceutical companies and research institutions to collaborate on complex drug discovery projects while maintaining strict data privacy controls.

The invention belongs to the field of bioinformatics, and relates to an anti-cancer drug response prediction method based on an attention mechanism. The method comprises the following steps: firstly, capturing uniform-dimension drug and cancer cell line characteristics through a multi-layer perceptron; secondly, fusing drug characteristics by adopting a Transform encoder, and constructing a cell encoder for cancer cell line characteristic polymerization; then, designing a cross-modal cross fusion module to promote information interaction between the two; and finally, predicting a semi-suppressed concentration value subjected to logarithmic transformation between the two through a multi-layer perceptron. Experimental results show that compared with an existing optimal method, the method has the advantage that the RMSE is reduced by 2.9%. According to the method, accurate prediction of the anti-cancer drug response is achieved by integrating drug and cancer cell line data, screening of potential anti-cancer drugs can be accelerated, personalized treatment schemes can be optimized, the cure rate of cancer patients is further increased, and the method has great significance in cancer treatment.

The invention relates to the technical field of intelligent anesthesia precise regulation and control, and discloses an anesthetic dosage optimization method based on artificial intelligence. According to the method, a dynamic treatment interval is constructed, and the boundary of the dynamic treatment interval is adaptively adjusted according to the real-time sedation depth and the nociceptive stimulation level. And in the interval, performing pattern recognition on the continuous electroencephalogram signals and the hemodynamic parameters, and marking abnormal events deviating from a standard anesthesia state. And establishing a correlation network of the drug effect chamber concentration and the abnormal events, and generating a virtual drug response curve for predicting the trend of the abnormal events under different doses. Whether dose strategy reconstruction is started or not is determined by comparing the goodness of fit between the prediction curve and the actual physiological trajectory. During reconstruction, contribution weights of historical drug infusion points to abnormal events are backtracked and analyzed, and adjustment coefficients are distributed and integrated into a new infusion sequence. According to the invention, individualization and self-adaptive optimization of anesthesia administration are realized, and the accuracy and safety of anesthesia depth control are improved.

The invention is applicable to the technical field of clinical medicine, provides a clinical multi-modal cancer drug response prediction method based on feature reconstruction, constructs a clinical multi-modal model for drug response prediction of diffuse large B-cell lymphoma, and aims to predict the drug response of diffuse large B-cell lymphoma by integrating gene sequencing and clinical multi-modal data. And accurate drug reaction prediction is realized. The model adopts an end-to-end multi-stage processing flow: firstly, extracting gene features through TransP-Net, and processing multi-modal clinical data by using a clinical information encoder; then, pseudo-gene features are generated through a clinical-genome filling module to deal with the data missing problem; and finally, multi-modal deep fusion is realized through a clinical information decoder, and a prediction result is output. According to the method, data characteristics and working processes in a real clinical environment are fully considered, two conditions of complete gene data and missing gene data can be processed at the same time, and the method has a good clinical transformation prospect and application value.

The invention provides an organoid drug reaction whole-course monitoring system, and relates to the technical field of data monitoring, and the system comprises an acquisition module which is used for acquiring a three-dimensional image sequence of an organoid at a set time interval, and monitoring the concentration data of metabolites in a culture solution to obtain multi-source monitoring data; the processing module is used for performing voxelization processing on the three-dimensional image sequence to form a time sequence three-dimensional voxel data set; and the calculation module is used for calculating barycentric coordinates of the organ-like structure based on the three-dimensional space distribution characteristics of the organ-like structure in the time sequence three-dimensional voxel data set, and establishing two mutually orthogonal space analysis axes in the maximum cross section of the organ-like structure by taking the barycentric coordinates as the center. According to the method, comprehensive and dynamic monitoring and evaluation of the organoid drug reaction process are realized through multi-source data collaborative acquisition, standardized spatial analysis, accurate form dynamic extraction, form and metabolism time sequence correlation modeling and real-time time sequence registration evaluation.

The invention discloses a dual-channel fusion cancer drug response prediction method, and belongs to the technical field of biological information. The method aims at solving the problems that an existing prediction method is only limited to intra-modal feature extraction, and a complex nonlinear cooperative relation between a drug and cancer cells is difficult to capture. Multi-omics data, drug molecular structure information and cancer cell line drug reaction data are integrated from databases such as CCLE, GDSC and PubChem, and unified input features are formed through standardization and feature construction. Introducing a hierarchical double-attention conversion network into the first channel to carry out characterization learning on the multi-modal features of the drug and the cell line, and constructing a high-order attention neighbor interaction graph convolutional network in the second channel to capture graph structure information of cancer drug response. And carrying out adaptive weighting on output results of the two channels by using a PPO-based fusion module so as to realize a dynamic optimal decision. And finally, generating a drug sensitivity prediction result of the cancer cell line through a classification predictor.

Various methods and systems for efficiently providing pharmacogenetic guidelines is disclosed. A drug prescription and a patient genotype can be analyzed to provide a drug therapy recommendation. The analysis can include comparing the patient genotype and the drug name to a drug profile database, a molecular biomarker database, hospital drug reaction records and omics databases, and calculating the patient response to the drug.

The present disclosure provides an attention-based MIL model for use in extracting and characterising cell features, at both the cell level and the population level. To characterise cells, cells in a well are initially fed into the model. The cells in a well include a points cloud of cells and a point cloud of corresponding cell nuclei. The cells in the well are passed through a pretrained DFN encoder which extrapolates the cell and nuclei features from the cells in the well. These extracted features are then passed through a transformer based encoder to produced transformed versions of the extracted cell and nuclei features. These transformed features are then fed into at least two classifiers. One of the classifiers is a cell-level MLP classifier which is used to compare the cell features with known drug response to attempt to match a phenotype signature with a known drug response. One other classifier is the bag classifier which is used to compare the cell population features with that of known drug responses, again to attempt to match to a phenotype signature. The outputs of the cell classification and the bag classification are used to label the cells in the well with a drug label or a potential drug label / use case.

The invention relates to the technical field of knowledge maps, in particular to a veterinary treatment big data knowledge map construction method, which comprises the following steps of: acquiring animal case symptom characteristics, physical indexes, medical history records and intervention stage data, normalizing the symptom characteristics and encoding medical history to generate a case characteristic vector set; mapping symptoms and medicine nodes to establish a semantic relationship to calculate association strength, embedding physique and medical history to update node confidence to generate a personalized knowledge graph model, dynamically correcting edge weights in combination with feedback and medicine response, and extracting an effective intervention path to construct an association index to generate a veterinary treatment big data knowledge graph. According to the method, through normalization and sequential processing of multi-source case data, feature quantification and tracking are achieved, dynamic association is established based on semantic mapping and graph attention, confidence attenuation and an attribute weighting mechanism are fused, a node relation is optimized and self-adaptive evolution is carried out, individual difference and drug response capture is enhanced, and the updating performance of diagnosis and treatment knowledge is improved; and accurate and intelligent diagnosis and treatment analysis is promoted.

The application discloses a prediction method, system and storage medium for patient drug reactions. The method comprises: constructing a data architecture of a drug reaction prediction model according to pre-input source domain data, target domain data and drug attribute information; based on the data architecture of the drug reaction prediction model, performing training on the drug reaction prediction model according to a target training function to determine execution parameters of the drug reaction prediction model; and determining drug reaction prediction parameters according to the execution parameters, the source domain data and the target domain data to determine a prediction result of the patient drug reaction. The application places the representation alignment process of the patient sample and the cell line sample in the specific drug context fusion information through drug conditionalization alignment, thereby reducing the cross-domain distribution difference. In addition, the drug priority prediction result is generated in units of patients by using the drug reaction prediction parameters, the risk of model degradation into drug identity memory is reduced, and the generalization prediction ability for drugs is enhanced.

The application relates to a single-cell drug reaction prediction method and device based on dynamic distribution adaptation and multi-source domain feature weighting. The method comprises the following steps: acquiring batch RNA-seq and single-cell RNA-seq; processing to obtain a high-dimensional gene expression profile; mapping the high-dimensional gene expression profile to a low-dimensional potential representation, learning nonlinear features by minimizing the difference between the input and the reconstruction result, and supervising the training of a predictor on a source domain; in the case of a multi-source domain, a multi-source domain feature weighting network is used to generate a weight vector and adjust the source domain features by weighting; a dynamic distribution adaptation method is used for cross-domain knowledge transfer, and a maximum mean difference and an adaptive factor are used for dynamic balance distribution alignment; based on the dynamic distribution adaptation method and the multi-source domain feature weighting, a classifier model is transferred to a target domain for unsupervised drug reaction prediction. The application can improve the prediction performance of the model on the target domain.

The utility model relates to the technical field of medicine production equipment, and discloses medicine production mixed reaction equipment with a self-cleaning structure, which comprises a tank body, a feed hopper is fixedly arranged at the upper end of the right side wall of the tank body in a penetrating manner, and a discharge pipe with a valve is fixedly arranged at the bottom of the tank body in a penetrating manner; a rotating rod penetrates through a longitudinal bearing in the center of the top of the tank body, and stirring blades are uniformly mounted on the surface of the rotating rod. According to the mixing reaction equipment with the self-cleaning structure for medicine production, the inner wall of the tank body can be scraped and cleaned in the stirring and mixing reaction process of medicines, and the situation that the medicines are adhered and cannot participate in the mixing reaction is avoided, so that the medicine mixing reaction effect is guaranteed, the medicine effect is guaranteed, and after the medicines react and are discharged, the medicine production efficiency is improved. By spraying water and cooperatively scraping the interior of the tank body, the interior of the tank body is cleaned, cleanliness and sanitation are guaranteed, follow-up medicine mixing reaction is facilitated, operation is easy, convenient and flexible, and practicability is high.

Provided herein are methods of determining a molecular response score for use in predictive models. The molecular response score may be used to monitor and guide administration of treatment to a subject.

The application provides a cross-institutional referral intelligent routing method and system based on a medical intelligent agent middleware, relates to the technical field of medical informatization, and comprises the following steps: establishing a sign data monitoring window, mapping sign index changes and medication record time sequences, constructing a drug reaction sensitivity curve to calculate a treatment effective coefficient; adopting a dynamic time warping algorithm to match a drug-sign correlation graph, and generating a treatment decision tree; and predicting disease development, identifying the best referral opportunity and target institution based on the treatment effective coefficient and specialty characteristics. The application improves cross-institutional referral efficiency, optimizes medical resource allocation, and enhances patient treatment effect.

The invention relates to a drug effect evaluation method for fusing target molecules and time-concentration dependent cell phenotypes, which comprises the following steps: S1, preparing samples including a modeling sample and a to-be-detected sample; s2, carrying out FRET imaging and cell fluorescence imaging; s3, carrying out FRET image processing and FRET efficiency calculation; s4, cell fluorescence image processing and feature extraction; s5, calculating a phenotype characterization value; s6, calculating an FRET characterization value; s7, basic drug response value calculation; and S8, comprehensive drug effect evaluation. Target molecule information and time-concentration dependent cell phenotypic response are fused, a comprehensive drug effect evaluation model is constructed, the effect of drugs on whole cells is reflected, and whether the drugs target specified molecules or not is specifically indicated.

The present invention relates generally to genomics. In particular, the specification teaches a method of predicting subjects at risk of rheumatoid arthritis and the responsiveness of a subject towards methotrexate (MTX) treatment.

The invention discloses a single cell chemical perturbation transcription response prediction method based on a generative neural network and application thereof, the generative neural network of a prediction model constructs a learnable conditional mapping function, and multi-modal embedding is constructed during prediction model training. A training result is optimized by adopting a zero-expansion Gaussian negative logarithm likelihood loss function; the multi-modal embedding is the key input of a conditional mapping function and comprises gene expression semantic features, multi-source biological priori knowledge and drug molecular structure semantic features, so that the learning ability of the conditional function on the true disturbance law of the known drug structure on the gene expression of the known cell type can be remarkably improved; the trained prediction model can break through the limitation that traditional drug reaction research is highly dependent on experimental conditions, low in flux and high in cost, and prediction single cell perturbation transcription response spectrums without drugs are obtained according to structural semantics of new drugs or expression semantics of new cells in a zero sample scene.

The invention provides a digital organoid construction method, equipment, a medium and a program product, and further provides a method, equipment, a medium and a program product for developing and predicting therapeutic schedule and / or therapeutic drug sensitivity based on a model, and relates to the field of intelligent medical treatment. The constructed digital organoid is widely applied to daily clinical practice, and the model can help to select the most suitable scheme from candidate schemes; in clinical trials, the model can help select patients who may respond to new test regimens, even previously abandoned regimens, and the model can predict drug response even if the treatment regimens contain new drugs not contained in the training set.

Methods are described that include an integrated, multi-scale, artificial intelligence-based system that reconstructs drug-specific pharmacogenomic networks and their constituent functional subnetworks. The system uses features of the functional topology of the three-dimensional architecture of drug-modulated spatial contacts in chromatin space. Discovery of drug pharmacogenomic networks is performed by selecting candidate SNPs with the aid of imputation, determining predictive causal relationships of the SNPs using machine learning and deep learning, probing spatial genomes as determined by chromosome conformation capture analysis using causal relationship SNPs, combining targeted genes controlled by the same cell and tissue-specific enhancers, and using different data sources and metrics to reconstruct pharmacogenomic networks based on results of genome-wide association studies. The pharmacogenomic networks are deconstructed into their constituent functional and adverse event subnetworks using a knowledge-based segmentation approach for application in clinical decision support, drug repurposing, and in silico drug discovery.

The invention provides a cross-institution referral intelligent routing method and system based on medical agent middleware, and relates to the technical field of medical informationization, and the method comprises the steps: building a physical sign data monitoring window, carrying out the time sequence mapping of physical sign index changes and medication records, constructing a drug response sensitivity curve, and calculating a treatment effective coefficient; adopting a dynamic time warping algorithm to match the medicine-physical sign association map, and generating a treatment decision tree; disease progression is predicted based on the effective treatment coefficients and specialized specialities, and the optimal referral opportunity and target mechanism are identified. According to the invention, the cross-mechanism referral efficiency is improved, the medical resource configuration is optimized, and the patient treatment effect is enhanced.

The invention relates to the technical field of molecular biology and precision medical treatment, and relates to a molecular marker for predicting FOLFOX chemosensitivity and application thereof. A patient-derived colorectal cancer organoid biological sample library is constructed, the heterogeneity of in-vitro drug reaction is analyzed and evaluated through transcriptome, clinical groups sensitive to FOLFOX chemotherapy can be accurately recognized, a molecular marker prediction system containing 11 genes is obtained through further screening, and the molecular marker prediction system is used for predicting the FOLFOX chemotherapy. The method effectively overcomes the technical defect of lack of accurate prediction of colorectal cancer chemosensitivity at present, and has important value in the aspects of revealing disease mechanisms and guiding personalized treatment.

The application discloses a tumor cell line drug response prediction method integrating multiple omics and essential gene information, relates to the field of tumor drug response prediction, and is a DROEG prediction method (Drug Response based on Omics and Essential Genes). Four kinds of omics data, including gene expression data, copy number variation data, methylation data and somatic mutation data, and CRISPR gene effect data (CERES Score) are introduced to construct a drug response model, and the effect of the model is comprehensively evaluated by a qualitative evaluation method and a quantitative evaluation method. The application introduces CRISPR essential gene information, establishes a drug response prediction model, is more accurate than existing methods, has the functions of quantitative and qualitative prediction and evaluation, is more suitable, and is more flexible to operate.

This disclosure is in the field of cardiac diseases. For example, the disclosure provides a new platform comprising iPSC and cardiomyocytes carrying one or more gene mutations, and models to study the effect of those gene mutations on cardiomyocytes, and on drug toxicity. This platform is identified herein as PREDICT PLATFORM.

The invention discloses a construction method and application of an adriamycin-induced zebrafish osteoporosis-like model, and the construction method comprises the following steps: by utilizing the characteristics of transparency and rapid bone development of zebrafish embryos and juvenile fishes, selecting the zebrafish embryos of 2dpf, and culturing the zebrafish embryos to 6dpf in an adriamycin aqueous solution of 20 mu m, so as to obtain the adriamycin-induced zebrafish osteoporosis-like model. Therefore, the visual zebrafish osteoporosis-like model is efficiently constructed, juvenile fish bodies are transparent, imaging is visual, bone development changes can be observed in vivo in real time, and visual evaluation of bone development injuries is achieved. The built zebrafish osteoporosis-like model clearly shows the systematic inhibition effect of chemotherapy drugs on bone formation and mineralization, has the advantages of being short in building period, easy and convenient to operate, high in repeatability and sensitive to drug reaction, can be used for rapid dose screening and intervention experiments, and has good application prospects. The method has great application potential in research of pathogenesis of CIOP, screening of anti-osteoporosis drugs and evaluation of curative effect of the anti-osteoporosis drugs.

The invention provides a culture medium and an in-vitro induction method for in-vitro erythroid induction of trace primary erythroid terminally differentiated cells, and belongs to the technical field of erythrocyte induction. The invention provides a culture medium for inducing primary erythroid terminal differentiated cells into erythrocytes in vitro. The culture medium comprises a first culture medium added with a plurality of inducing factors and a second culture medium not added with the inducing factors. The invention also develops a method for inducing micro primary erythroid terminal differentiated cells into erythrocytes in vitro, the method is suitable for primary cells, and the problem of drug response distortion caused by large difference between an immortalized cell line and cells in a living body is effectively avoided. According to the in-vitro induction method disclosed by the invention, the maturing and differentiating process can be completed in vitro only by using a very small amount of primary cells, so that the dependence on precious clinical samples is greatly reduced, and repeated experiments and high-throughput screening by using limited samples become possible.

Provided herein is a method for determining molecular response score for use in predictive model.Molecular response score can be used to monitor and guide the administration of treatment to subject.The molecular response score can be generated by a method comprising: for at least one variant among a plurality of variants classified into somatic cells, determining the weighted average of the first variant allele fraction (MAF) and the weighted average of the second MAF based on the first MAF and the second MAF.

AMD is a complex, multifactorial disease lacking curative therapy. Understanding of AMD pathophysiology highlights mechanisms such as mitochondrial dysfunction, visual cycle defects, autophagy impairment, and unresolved inflammation and oxidative stress. However, current therapies only target select aspects of AMD pathology. As described herein, variants in the ELOVL2 gene are utilized to predict the progression of age-related central nervous system conditions, including AMD, and to assess susceptibility to treatment. This approach enables more effective early intervention and personalized therapeutic strategies.

1. Name of the product in this design: Main body of a erythrocyte sedimentation rate analyzer. 2. Purpose of this design: The erythrocyte sedimentation rate (ESR) analyzer is used as laboratory equipment to measure and analyze blood as an indicator of patient inflammation, anemia, and patient response to drugs such as chemotherapy. The main body of the erythrocyte sedimentation rate analyzer is used as part of the erythrocyte sedimentation rate analyzer. 3. The key design features of this product are the shape of the portion shown by the solid line. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The present application relates to the field of intelligent medical auxiliary decision-making technology, and discloses a ladder treatment scheme recommendation method based on asthma control level and drug response phenotype. The present application aims to solve the problem that the existing scheme cannot identify the deep reason of uncontrolled condition, leading to blind step-up treatment. The present application excludes non-compliance interference by checking the drug execution rate, and improves the authenticity of condition assessment. The present application uses environmental exposure characteristics to adaptively correct the exhaled nitric oxide determination threshold, and eliminates the deviation of smoking on inflammation recognition. The present application combines the logic tree voting of multi-dimensional biological indicators to lock the drug response phenotype. Finally, based on the double index of condition state and phenotype, the present application locates the upgrade or transformation treatment path and generates a recommended scheme. The present application realizes the precise typing and differentiated drug administration of asthma treatment, effectively reduces the drug exposure risk of hormone-insensitive patients while ensuring the clinical efficacy.