54 results about "Individualized treatment" patented technology

This invention discloses a breast cancer classification method based on Piezo1 expression. After obtaining tumor tissue samples from patients, the method detects the Piezo1 protein expression level using immunohistochemistry and classifies them into high-expression and low-expression types according to a validated scoring standard. This classification result guides individualized treatment, with high-expression patients recommended for treatment regimens including inhibitors of mechanotransmission pathways. By integrating tumor mechanotransmission characteristics into the clinical classification system, this invention can identify a subgroup of breast cancer driven by an abnormal mechanotransmission microenvironment that cannot be distinguished by traditional molecular classification. This not only improves and supplements the existing classification framework, making the understanding of breast cancer heterogeneity more comprehensive, but also more accurately predicts the invasiveness, metastasis potential, and poor prognostic risk of these patients.

The application discloses a joint cartilage stress dynamic monitoring system based on flexible sensing and AI and belongs to the technical field of medical health monitoring.The application solves the problem that the prior art can only measure single-point pressure or strain and cannot obtain full-field three-dimensional stress distribution of a cartilage contact surface, and the system function stops at monitoring and does not form a monitoring-to-warning closed loop, quantifies uncertainty through a Bayesian neural network, provides a reliable basis for clinical decision-making, combines multidimensional biomechanical characteristics and an attention mechanism, accurately focuses on a stress key area, significantly improves the precision and individualization level of joint cartilage health evaluation, helps early detection and prevention of injury, classifies stress abnormalities, implements accurate early warning, and formulates an individualized treatment scheme according to uncertainty results and cartilage health indexes, and customizes a rehabilitation training plan according to user conditions, and improves the scientific nature of joint injury prevention and rehabilitation.

This invention provides a combination of metabolic biomarkers for T-staging assessment of colorectal cancer, comprising four groups of metabolite combinations specifically distinguishing different T stages of colorectal cancer, each group containing 10 endogenous small molecule metabolites. This invention also provides detection methods and applications for the above-mentioned metabolic biomarker combinations. Furthermore, this invention provides a method for T-staging colorectal cancer for non-diagnostic and therapeutic purposes, as well as a combination of metabolic biomarkers related to the evolution of T-staging and tumor invasion depth in colorectal cancer. The advantages of this invention are: by combining iEESI-MS detection with multivariate statistics and machine learning, specific metabolic biomarkers related to T-staging of colorectal cancer are screened, a T-staging discrimination model is constructed, and objective, accurate, and rapid assessment of T-staging of colorectal cancer is achieved, providing molecular support for preoperative individualized treatment.

The invention belongs to the technical field of biological medicine, and discloses a portal cholangiocarcinoma postoperative risk prediction model based on a gamma-glutamyltransferase (GGT) dynamic change track and a construction method thereof. The method comprises the following steps: (1) collecting clinical characteristic data and survival characteristic data (including survival time and survival state) of a patient suffering from porta hepatis cholangiocarcinoma subjected to radical resection; 2) on the basis of GGT detection results at different time points in a perioperative period, dividing patients into different GGT dynamic change track types by using a latent category hybrid model; and 3) taking the clinical characteristic data and the GGT dynamic change track type as independent variables, taking survival data as response variables, screening prediction factors through LASSO regression, and constructing a multi-factor Cox regression model, namely a postoperative risk prediction model. According to the method, the dynamic evolution rule of the GGT is introduced, so that the limitation of single-time static detection is made up, the quantitative evaluation of the survival risk is realized, and a scientific basis is provided for individualized treatment and follow-up visit management.

The application relates to the field of biomedical technology, and discloses a diagnostic agent for identifying melanoma molecular subtype typing and application, wherein the diagnostic agent comprises a first antibody specifically combined with a SOX10 protein and a second antibody specifically combined with an EGR1 protein; the diagnostic agent can be applied to preparation of a diagnostic product for evaluating the prognostic effect of a melanoma patient, preparation of a diagnostic product for predicting the treatment sensitivity of melanoma to a BRAF inhibitor, and preparation of a diagnostic product for guiding an individualized treatment scheme of melanoma. The diagnostic agent for melanoma molecular subtype typing can be directly transformed into clinical practice, and can assist in realizing real individualized treatment.

This invention belongs to the field of tumor treatment, specifically relating to the application of a CHK1 inhibitor in the preparation of drugs that weaken radiotherapy and chemotherapy resistance in nasopharyngeal carcinoma. This invention is the first to clearly demonstrate that F-circRM mediates radiotherapy and chemotherapy resistance by activating the CHK1 signaling pathway, enhancing the DNA damage repair capacity of nasopharyngeal carcinoma cells, and thus mediating the core mechanism. The CHK1 inhibitor can effectively block this pathway, significantly enhancing the sensitivity of nasopharyngeal carcinoma to radiotherapy and chemotherapy and reversing radiotherapy and chemotherapy resistance in in vitro multi-cell lines, patient-derived primary cells, and in vivo animal models. This invention also clarifies that F-circRM can serve as a biomarker for predicting the efficacy of radiotherapy and chemotherapy, providing a basis for stratification and individualized treatment plans for nasopharyngeal carcinoma patients, and has significant clinical value and broad application prospects.

The application belongs to but is not limited to the field of biomedical technology, and discloses application of anti-ACSL6 autoantibody as a prognostic evaluation marker for lung adenocarcinoma. Compared with traditional markers, the circulating biomarker anti-ACSL6 autoantibody has the characteristics of specificity, stability, early production, non-invasiveness, easy detection and the like, and has more potential value in reflecting the prediction of patient prognosis. High expression of anti-ACSL6 autoantibody is often related to poor survival of LUAD patients, and patients with low expression show relatively better prognosis. And anti-ACSL6 autoantibody remains significant in multivariate Cox regression, indicating that it can independently predict the survival outcome of patients after adjusting various confounding factors such as gender, age, stage, metastasis status and the like. This enables anti-ACSL6 autoantibody to provide more accurate risk stratification basis for clinicians, and help to develop individualized treatment and follow-up plans.

The utility model belongs to the technical field of medical auxiliary instrument, concretely relates to a percutaneous electric medicine synergic treatment device, including host computer and with the electric medicine treatment unit of host computer electric connection, the electric medicine treatment unit includes annular adhesive member, ring electrode, drug loading disc and medicine gel piece, annular adhesive member, ring electrode and drug loading disc coaxial arrangement, ring electrode is fixed between drug loading disc side surface and annular adhesive member inboard, the thickness of drug loading disc is less than the thickness of ring electrode, and the inboard of ring electrode and the surface of drug loading disc form the recessed cavity for accommodating medicine gel piece, and when medicine gel piece sets up in recessed cavity, medicine gel piece side surface and ring electrode inboard are pasted. The utility model can improve medicine transdermal absorption rate on the basis of guaranteeing electric stimulation itself effect, thereby more effectively realizes electric stimulation and medicine synergic effect, to improve synergic curative effect, realizes the flexible replacement use of different function medicine gel piece, satisfies the individualized treatment demand.

The application discloses a paclitaxel treatment response prediction method and system for triple-negative breast cancer patients, and the method comprises the following steps: obtaining single-cell transcriptome data, bulk transcriptome data and paclitaxel sensitivity data of triple-negative breast cancer patients before and after treatment and preprocessing; constructing a regression model of immune microenvironment and tumor response, calculating the response score of immune cell subgroups, screening key immune cell subgroups related to paclitaxel treatment response, performing differential expression analysis on the pre-treatment sample, constructing a paclitaxel IC50 prediction model, and dividing the sample into a high-sensitivity group and a low-sensitivity group; constructing a treatment response prediction model and outputting a prediction result. The technical scheme integrates the dynamic changes of the immune microenvironment, the transcriptome characteristics and the drug sensitivity data, realizes accurate prediction of the paclitaxel treatment response of the triple-negative breast cancer patients before treatment, and provides a scientific basis for formulating an individualized treatment plan.

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.

The application discloses a cancer molecular marker prediction method based on multi-omics and intelligent feature mining, and belongs to the technical field of bioinformatics and artificial intelligence; the method comprises the following steps: collecting multi-omics data of cancer samples, including genomic, transcriptomic, epigenetic, proteomic and metabolomic data; performing quality control and pretreatment on the data; performing intelligent feature mining by adopting difference analysis, dimension reduction algorithm and feature fusion network; integrating biological networks to construct a multi-view graph neural network prediction model; identifying key molecular markers and performing verification; the application can effectively integrate multi-level omics data, mine molecular markers with biological significance, and establish a high-precision and high-explainability prediction model; the application provides an effective technical means for early diagnosis, molecular typing, prognosis evaluation and individualized treatment of cancer, and has important scientific significance and clinical application value.

This invention discloses a multi-dimensional quantitative assessment method and system combining meibomian gland morphology and function, belonging to the fields of ophthalmic diagnosis and artificial intelligence technology. It acquires color images of the eyelid margin, infrared images of the meibomian glands, and dynamic videos of gland expulsion upon pressure, inputting these into a dedicated AI analysis engine for processing. Employing an innovative anatomically guided backbone network and a meibomian gland region adaptive module, it achieves parallel automatic quantitative scoring across four dimensions: meibomian gland opening abnormality score, secretion characteristics score, expulsion capacity score, and morphological abnormality score based on gland segmentation loss rate. The four scores are summed to obtain a comprehensive total score, used for the graded diagnosis of meibomian gland dysfunction. By comparing changes in the patient's scores over time, it achieves macroscopic and microscopic evaluation of treatment efficacy. By integrating functional and morphological indicators into a unified automated quantitative system, it solves the problems of subjectivity and fragmentation in existing assessment methods, significantly improving the objectivity, comprehensiveness, and accuracy of diagnosis, and providing a reliable tool for individualized treatment and long-term management of MGD.

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 relates to a COPD ventilator dependence risk prediction method and device, an intelligent decision-making method and system, the method comprising: constructing a quantitative index system of a respiratory supply and demand balance model; obtaining multi-modal data of a patient, calculating features of the multi-modal data according to the quantitative index system to obtain a supply-side feature vector, a demand-side feature vector and a load-side feature vector; processing the feature vectors using a trained deep learning model to obtain a ventilator dependence risk prediction result; obtaining the ventilator dependence risk prediction result and a respiratory homeostasis index of the patient; performing cluster analysis on the feature vectors to identify a ventilator dependence phenotype and calculate a subtype probability distribution; and determining an individualized treatment intervention strategy according to the ventilator dependence risk prediction result, the respiratory homeostasis index and the ventilator dependence phenotype. The present disclosure can realize accurate assessment of ICU ventilator dependence risk and intelligent generation of intervention strategies.

The application aims to provide a HER2-positive breast cancer clinical auxiliary decision system based on multi-modal analysis, comprising: a data acquisition layer; a data processing and storage layer for cleaning, format standardization and feature extraction of the patient multi-modal data; an AI model analysis layer comprising a multi-modal deep learning model of a fusion convolutional neural network (CNN), a graph convolutional network (GCN), a multi-layer perceptron (MLP) and a Transformer text encoder, which outputs risk assessment of patient prognosis and treatment response according to multi-source features obtained from the data processing and storage layer; a decision support layer for generating individualized treatment path recommendations according to the risk assessment results generated by the AI model analysis layer, combining clinical guidelines and a rule engine, and presenting the recommended scheme and prediction basis to the clinician through a visual interactive interface; a feedback learning layer for application in the actual clinical execution process.

The application discloses a clinical reasoning intelligent agent method and system based on prompt enhancement and collaborative feedback, relates to the technical field of medical artificial intelligence, and comprises the following steps: S1, triage and inquiry: constructing a basic case context; S2, data preprocessing: generating a structured medical image description candidate and confidence thereof; S3, medical image analysis: generating accurate image semantic information; S4, continuous task reasoning: sequentially carrying out clinical diagnosis and treatment planning; S5, continuous reasoning and feedback: checking diagnosis results and treatment plans; and S6, an output stage: outputting a final diagnosis conclusion and individualized treatment suggestions. The clinical reasoning intelligent agent method and system based on prompt enhancement and collaborative feedback can enhance the semantic understanding ability of a clinical diagnosis and treatment intelligent agent for medical images, reduce the risk that errors generated in a previous task are gradually propagated in subsequent diagnosis and treatment planning tasks, and improve the accuracy and reliability of continuous clinical reasoning.

The application discloses a specific metabolite for dynamically monitoring the curative effect of gastric cancer NAIC, a screening method and a prediction method, and belongs to the technical field of tumor treatment prediction. The screening method is as follows: obtaining plasma samples of gastric cancer patients before treatment and at key treatment nodes, detecting metabolite abundance data; based on imaging or pathological evaluation, the patients are divided into a treatment response group and a non-response group; comparing the metabolite changes of the two groups before and after treatment, screening metabolites that only change significantly in the response group and have opposite change trends between the two groups, and constituting a key metabolite set; using the metabolite abundance data in the set as features and the treatment response as variables, a LASSO regression model is trained, and finally specific metabolites for distinguishing curative effects are screened out. The curative effect prediction model and the prediction method based on the specific metabolites can realize early and non-invasive dynamic monitoring and prediction of curative effects, and provide a reliable basis for clinical individualized treatment.

The application belongs to the technical field of biological medicine, and provides a drug screening method based on biliary tract cancer organoids. First, biliary tract cancer tumor tissues are used to construct biliary tract cancer tumor organoids with high activity and complete structure through an organoid culture method, a tumor environment is simulated, and a 384-well plate high-throughput drug screening system is used to screen effective drugs from candidate drugs through the biliary tract cancer tumor organoid model. The method uses an organoid model as a means of efficacy evaluation, constructs tumor organoids through intraoperative or biopsy specimens, and applies a small molecule compound library to treat the tumor organoids in vitro, so that the sensitivity of biliary tract cancer cells can be determined simply, quickly and economically, and a basis for individualized treatment is provided. Moreover, the method can quickly screen drugs sensitive to patients, and reduce the great pain of patients caused by multiple chemotherapies.

The present application relates to the technical field of biological medicine, and particularly relates to a tumor immunogenic death biomimetic nanovesicle with high membrane breaking efficiency, a preparation method and application thereof.The tumor immunogenic death biomimetic nanovesicle with high membrane breaking efficiency comprises a tumor cell membrane and a photothermal reagent; the photothermal reagent is compound A or compound B.The biomimetic nanovesicle provided by the present application has a killing rate of more than 95% on various tumor cells, can be prepared into ICMs for different tumors by flexibly matching cell membranes of different cancer types, supports individualized treatment and combined immunotherapy, and provides an innovative scheme with high efficiency, wide spectrum and low toxicity for solid tumor treatment through synergistic action of targeted delivery and photothermal-induced cell membrane destruction, and has high clinical application potential.

The application discloses an individualized phosphorus reduction calculation system, which has the following characteristics: a dietary phosphorus intake evaluation module for quantitatively evaluating dietary phosphorus intake in a period of time; a drug phosphorus binding evaluation module for evaluating the amount of phosphorus binding of drugs; a hemodialysis phosphorus removal evaluation module for obtaining the amount of dialysis phosphorus removal by analyzing the dialysis waste liquid of a maintenance hemodialysis patient; and a phosphorus load evaluation module for calculating the phosphorus load condition of the patient or formulating an individualized intervention scheme according to the phosphorus load zero requirement based on the output data of the dietary phosphorus intake evaluation module, the drug phosphorus binding evaluation module and the hemodialysis phosphorus removal evaluation module and the personal data of the patient. The application can guide hemodialysis patients to formulate individualized treatment schemes from the aspects of diet, phosphorus binding agent and dialysis according to the individual phosphorus load state and the target of phosphorus load zero, so that the phosphorus reduction effect of the hemodialysis patients is improved.

The application relates to the field of molecular biomedical technology, and discloses a method, system and device for evaluating recurrence risk and prognosis of a resectable non-small cell lung cancer (NSCLC) patient based on plasma circulating free DNA (cfDNA) fragmentomics. The method comprises the following steps: obtaining plasma cfDNA of the NSCLC patient and performing high-throughput sequencing; extracting fragment end mode characteristic values (CsFMA) of GATA3 and CEP57 genes from the sequencing data, which are CsFMA.GATA3 and CsFMA.CEP57 respectively; inputting the characteristic values into a risk score model; calculating an individual risk value; stratifying the patient into a high-risk group and a low-risk group according to the risk value, so as to predict disease-free survival and overall survival. The application discloses a strong correlation between specific fragmentomic characteristics of GATA3 and CEP57 genes and recurrence and prognosis of NSCLC, and the constructed model can effectively predict recurrence risk and prognosis. The application provides a non-invasive and accurate new tool for resectable NSCLC recurrence and prognosis stratification and individualized treatment decision-making.

The application discloses a kind of based on multimodal fusion's thyroid nodule BRAF gene mutation prediction system and method, belong to medical auxiliary diagnosis field, including: data acquisition module, for obtaining the conventional ultrasound image of target thyroid nodule, real-time shear wave elastography quantitative data and puncture cytology image;Multimodal feature extraction module includes ultrasound feature extraction unit, elastic feature extraction unit and cytology feature extraction unit;Feature fusion module is configured to by based on the deep learning model of attention mechanism, the ultrasound image depth feature, the elastic feature and the cytology depth feature are adaptively fused in feature level, and obtain fusion feature;Prediction module is configured to the fusion feature is input into classifier, and the prediction result that whether the target thyroid nodule exists BRAF gene mutation is output.For the precise diagnosis and treatment of thyroid nodule, individualized treatment decision and clinical process optimization provide innovative technical support.

This invention discloses a high-focusing deep transcranial magnetic stimulation (TMS) coil based on asymmetric topology, comprising at least two mutually coupled coil loops, wherein the parameters of at least one coil loop differ from the others, causing the magnetic induction peak of the TMS coil to deviate from its geometric symmetry center. The parameters of the coil loop include at least one of geometric dimensions, number of winding turns, and shape topology. This invention fundamentally reshapes the spatial distribution of the induced magnetic field, optimizing the directional shift, depth, and focusing performance of the stimulation hotspot without significantly increasing system complexity or introducing new constraints. It achieves better matching for specific nerve fiber pathways, realizing directional and more precise brain stimulation, and reducing the intensity of stimulation in non-target areas. This meets the diverse and individualized treatment needs of different diseases, patients, and brain regions, providing a new technical path for high-precision TMS treatment.

The present application relates to a kind of methods based on transcriptome sequencing, machine learning and molecular docking analysis of evodiamine treatment vestibular schwannoma (VS), the present application is analyzed by integrating transcriptome sequencing technology and GEO database, combined with machine learning algorithm, molecular docking and molecular dynamics simulation method, the potential key target and signal pathway of evodiamine treatment VS are systematically analyzed, provide technical support for the mechanism research of evodiamine, and provide reference basis for future individualized treatment strategy of VS.

This invention discloses an AI-based differentiated follow-up health management method based on big data, belonging to the field of health management information technology. The method includes: based on a time-series health dataset, performing window segmentation and feature extraction around each health management period to generate a multi-timescale feature set; adapting and combining differentiated follow-up elements for the dominant risk time window and dominant risk dimension to generate candidate differentiated follow-up and intervention action combinations, and performing similar case matching and response mapping with a historical case database to generate action gain information; inputting the action gain information into a causal forest model to estimate the individualized treatment effect of the same target object under each candidate differentiated follow-up and intervention action combination, calculating the net benefit value of the action, and generating a target differentiated follow-up health management plan. This invention achieves dynamic analysis of the evolution law and core driving factors of health risks from a composite time dimension, improving the temporal accuracy and dimensional specificity of risk insight.

The application discloses a non-small cell lung cancer anti-PD-1 individualized treatment decision support system and a method thereof. The system comprises a data acquisition and standardization module, which adopts standard integration of multi-source heterogeneous data; a dynamic knowledge graph construction module, which stores structured knowledge based on a Neo4j storage structure; an incremental learning module, which realizes real-time updating of a knowledge base; an intelligent decision support module based on a domestic large language model, which performs indication screening, efficacy and side effect prediction and conflict detection through technologies such as multi-modal fusion, hierarchical decision model, time series Transformer and graph neural network; an individualized treatment plan and clinical trial matching module, which optimizes a treatment path in combination with economic analysis and semantic matching algorithm; a patient education and interaction module, which provides personalized content generation, intelligent question answering and toxicity management; and a clinical verification and optimization module supporting lightweight adaptation of primary medical institutions. The application realizes intelligentization of the whole process from data integration to clinical decision, and improves the precision and accessibility of immunotherapy.

This invention provides a biomarker for collateral circulation in CTO patients, MIA3, and its application, belonging to the field of biotechnology. This invention reveals and verifies a significant positive correlation between plasma MIA3 levels and the quality of collateral circulation formation in CTO patients, and its predictive efficacy for good collateral circulation is superior to that of the known key angiogenesis factor VEGFA. This allows MIA3 to serve as a high-value, minimally invasive molecular marker for early, non-invasive identification of a patient's collateral circulation formation potential before interventional surgery, thereby enabling risk stratification and providing an objective basis for individualized treatment decisions (such as whether more aggressive intervention is needed). This invention clarifies that MIA3 is a key link in regulating collateral angiogenesis; by targeting and upregulating MIA3 activity, collateral angiogenesis and maturation in ischemic areas can be precisely promoted, providing a novel non-pharmacological, non-surgical treatment strategy for CTO patients.