132 results about "Personalized therapy" patented technology

Systems, devices, and methods for electrically stimulating peripheral nerve(s) to treat various disorders are disclosed, as well as signal processing systems and methods for enhancing diagnostic and therapeutic protocols relating to the same. Personalized therapy based on algorithms and aggregated data are also provided.

The invention belongs to the field of rehabilitation medical treatment and intelligent computing, and particularly discloses a cerebral apoplexy rehabilitation personalized treatment recommendation system based on artificial intelligence. Comprising a multi-modal data acquisition module, a feature extraction and clustering modeling module, a personalized path making module and a recommended path generation and feedback optimization module, and can realize dynamic monitoring and intelligent analysis of rehabilitation data of a stroke patient; a multi-modal aggregation neural network model is constructed by fusing multi-dimensional data such as images, biomarkers, clinical behaviors and cognitive emotions, a rehabilitation path is formulated according to a medical data clustering result, and a recommended path is dynamically adjusted and optimized in combination with treatment feedback of a patient; compared with a traditional method, the method has the advantages that scientificity and adaptability of personalized treatment are improved, the method has the advantages of real-time updating of rehabilitation paths, closed-loop management of a system, high model robustness and the like, rehabilitation intervention efficiency and accuracy can be remarkably improved, and technical support is provided for intelligent development of rehabilitation medicine.

The invention discloses a control method and system suitable for a massage physiotherapy instrument, and relates to the technical field of physiotherapy equipment, and the method comprises the steps: collecting the physical sign parameters of a user in real time, analyzing the muscle, meridian and pain states, and generating matched personalized treatment parameters; based on the personalized treatment parameters, the negative pressure adsorption force of the target area is dynamically adjusted, and the adsorption state is monitored in real time; applying an intermediate-frequency electric pulse to a target area on the basis of negative-pressure adsorption, and dynamically adjusting an electric pulse parameter in combination with an adsorption state; on the basis of feedback data of the target area after the medium-frequency electric pulse effect, controllable heat is transferred to the deep tissue through a heat energy device in the negative pressure adsorption area; after controllable heat transfer is completed, high-frequency sound waves are released in the negative-pressure adsorption area for cell gap micro-vibration; according to feedback data, a heat energy transfer mechanism accurately acts on deep tissues, blood flow is improved, and muscle relaxation is promoted.

The invention discloses a gravitational field model-based lattice space segmentation radiotherapy lattice point optimization method, and relates to the technical field of radiotherapy. According to the method, by combining a PET-CT functional image, a key region, especially a hypoxic region (functional target region), of a tumor is recognized more accurately, and the positions and the distances of lattice points are dynamically adjusted according to biological characteristics of the tumor; arrangement of lattice points is dynamically adjusted through a gravitational field model, it can be guaranteed that a high-dose area accurately covers a key area of a tumor, unnecessary side effects can be reduced to the maximum extent, the risk of tumor recurrence is reduced, the method can better adapt to functional heterogeneity of the tumor, personalized treatment is achieved, and the method has good application prospects. And the influence on normal tissues is reduced while the treatment accuracy is improved, a more reliable treatment scheme is provided for treatment of complex tumors, and accurate dose distribution aiming at different tumor biological characteristics is realized.

The invention discloses a lung adenocarcinoma prognosis risk prediction model and a construction method and application thereof, and belongs to the technical field of biomedical detection. The lung adenocarcinoma (LUAD) prognosis model is successfully constructed by systematically analyzing the immune infiltration related gene (CAFRG) of tumor-associated fibroblasts (CAF), and the application value of the lung adenocarcinoma (LUAD) prognosis model in prediction of patient prognosis is verified. The CAFRG risk score is negatively related to the immune microenvironment score and is remarkably related to immune checkpoint gene expression, which suggests that a high-risk patient may have the characteristic of immune escape. Furthermore, based on TIDE tool analysis, the patients in the low-risk group have more active T cell immune response. The risk score is also closely related to the sensitivity of antitumor drugs, especially Doramapimod. Therefore, a new theoretical basis is provided for personalized treatment of lung adenocarcinoma, and a new direction is provided for future clinical research.

The invention provides an evaluation-intervention integrated system and equipment oriented to personalized rehabilitation scheme optimization. The evaluation-intervention integrated system comprises a personalized scheme recommendation module, a curative effect evaluation module and an intervention scheme optimization and adjustment module. The personalized scheme recommendation module generates a personalized rehabilitation treatment scheme by collecting multi-dimensional data and combining with a machine learning algorithm. And the curative effect evaluation module monitors and comprehensively evaluates the rehabilitation effect of the patient in real time, and generates a detailed curative effect report. And the intervention scheme optimization and adjustment module dynamically adjusts the treatment scheme and optimizes the treatment content, intensity and frequency according to the curative effect evaluation result and patient feedback. Through intelligent analysis and a feedback mechanism, an accurate and customized rehabilitation treatment scheme is provided for each patient, dynamic adjustment is performed according to a real-time evaluation result, and the rehabilitation treatment accuracy and the treatment effect are remarkably improved. The system also supports real-time data sharing and cooperation among patients, doctors and rehabilitation experts, and can perform cross-region and cross-professional personalized treatment management.

The invention discloses a nanometer delivery device for a gastric cancer targeted drug and a use method of the nanometer delivery device, and belongs to the technical field of drug targeted delivery. The device comprises a molecular recognition and optimization module which is responsible for accurately recognizing a tumor cell specific molecular marker and providing personalized treatment strategy recommendation; the nano-carrier design module is used for designing multifunctional lipid-polymer hybrid nano-particles and carrying out surface targeting ligand modification; the intelligent drug loading module is used for improving the stability and controllability of drug loading based on a pH / temperature sensitive release mechanism and effectively reducing the toxic influence of non-targeted tissues; the molecular imaging and monitoring module integrates a nanoprobe imaging technology and a quantum dot tracing technology, constructs a treatment-imaging integrated system, realizes real-time dynamic monitoring of a treatment process, and provides instant feedback of a molecular level; and the risk assessment module is used for establishing a safety database and providing dynamic risk assessment of the treatment process through a biological safety assessment system and long-term tracking management.

In some embodiments, a set of biomarkers is provided, selected from one or more of the group consisting of ITGB3, HTRA1, SERPINH1, TIMP3, ITGAV, COL11A1, ITGB7, CIITA, ZBP1, GZMA, IL18BP, and RNF213. The biomarker is used for predicting the response of a subject receiving immunotherapy to immunotherapy, has good performance in subjects suffering from different tumor types, has high universality, has the advantages of being low in cost, easy and convenient to use and the like, and has good application prospects. And the method is of great significance for predicting response to immunotherapy of cancers and personalized therapy.

The present invention relates to a multi-modal colorectal cancer pre-diagnosis information processing method, system, medium, and device. By converting image information into first-modal features, first clinical information into second-modal features, and family history information into third-modal features, and then performing feature fusion and inputting it into a neural network model of deep learning. Finally, the first pre-diagnosis report output can provide information on the disease risk level, stage feature prediction, canceration probability, and treatment plan of the current user. The multi-modal technology is applied to integrate various information sources such as the patient's personal information, image information, first clinical symptoms, and family history information, improving the comprehensive analysis of the colorectal diseases of the current user, enabling doctors to obtain a more comprehensive disease assessment, and thus achieving a more accurate risk level classification, disease stage prediction, and personalized treatment plan formulation, improving the efficiency and accuracy of the diagnosis and treatment process.

The invention discloses a colorectal cancer prognosis model construction method based on fatty acid metabolism related genes, and belongs to the technical field of biomedicine, and the colorectal cancer prognosis model construction method comprises the following steps: S1, obtaining transcriptome data of a colorectal cancer diagnosis individual from a TCGA database; s2, performing difference analysis on fatty acid metabolism related gene expression data of the colorectal cancer tissue and the normal tissue, and screening out differentially expressed genes; s3, performing single-factor Cox regression analysis on the differentially expressed genes, and screening out genes related to colorectal cancer prognosis; according to the construction method of the colorectal cancer prognosis model based on the fatty acid metabolism related genes, provided by the invention, the tumor immunotherapy reactivity is evaluated by introducing TIDE scores, and the prior art is improved, so that the immune escape potential and the immunotherapy effect of tumor subtypes are analyzed. The prognosis of a colorectal cancer patient is effectively predicted, and a new molecular marker is provided for personalized treatment of the colorectal cancer.

The invention discloses a liver cancer post-transplantation muscle loss prediction method and device, and belongs to the technical field of biomedicine, and the method comprises the steps: constructing an unbalanced random forest model comprising a plurality of decision trees, independently predicting through the plurality of decision trees to obtain a plurality of high-risk or low-risk dichotomy prediction results, taking the proportion of the decision tree with the prediction result being high risk in all decision trees as the prediction probability of muscle loss after liver cancer liver transplantation finally output by the model; performing risk classification according to the prediction probability, and judging the postoperative muscle loss level of the patient as high risk, medium risk or low risk; constructing a decision tree model to layer prognosis of patients, and dividing the patients into a high-risk group, a medium-risk group and a low-risk group; and carrying out survival rate prediction in combination with liver transplantation standards and prognosis layering results of patients. According to the method, the muscle loss level after the liver transplantation can be accurately predicted, and a clinician can make a personalized treatment scheme and perform scientific management after the transplantation.

In some aspects, the present disclosure describes a method of predicting an expected treatment outcome of a subject, comprising administering a gamma oscillation-inducing non-invasive sensory stimulus to the subject, measuring a response from the subject, and predicting, using a machine learning algorithm, the expected treatment outcome of the subject based at least partially on the measured response. In some aspects, the present disclosure also provides methods for personalizing gamma therapy treatment by adjusting parameters associated with the gamma oscillation-inducing non-invasive sensory stimulus based on a subject's response to the gamma oscillation-inducing non-invasive sensory stimulus.

The invention relates to a hepatocellular carcinoma gene knockout target library based on multiple omics and a screening method of the hepatocellular carcinoma gene knockout target library, and the gene knockout target library for precise treatment of hepatocellular carcinoma is finally obtained through data collection and integration, data screening and target verification in sequence. According to the invention, through multi-omics data integration and bioinformatics analysis, key driving genes of hepatocellular carcinoma are systematically screened, and the important effects of the genes in occurrence, development, metastasis, drug resistance and immune escape of hepatocellular carcinoma are disclosed; the genes not only deepen the understanding of the hepatocellular carcinoma molecular mechanism, but also provide important theoretical basis and potential intervention targets for the development of targeted therapy and personalized therapy strategies.

The invention provides a lung cancer brain metastasis risk prediction method and system, and the method comprises the steps: obtaining the brain MRI image data of a user, and carrying out the data preprocessing; whole-brain feature extraction and partition feature extraction based on an AAL template are carried out on the preprocessed data, and whole-brain features and partition features are combined to obtain a feature matrix; according to the preprocessed brain MRI image data of the user suffering from lung cancer brain metastasis, training an auto-encoder to obtain a trained auto-encoder; inputting the feature matrix into the trained auto-encoder, and calculating a reconstruction error; according to the reconstruction error, an isolated forest method is adopted to further screen abnormal areas, and an abnormal score is generated for each sample; according to the reconstruction error and the abnormal score, the abnormal score of the whole brain feature and the abnormal score of the partition feature are calculated, the high-risk region of the brain can be accurately marked by combining the abnormal detection of the whole brain feature and the partition feature, and a basis is provided for personalized PCI treatment.

The invention provides a chronic kidney patient cardiovascular adverse event prediction and clinical treatment system based on deep learning, and the system comprises a multi-source information integration module which obtains multi-modal fusion data; the risk prediction module is used for constructing a risk prediction model and outputting the risk prediction probability, the risk level and the risk report of each patient; the decision support module is used for generating a plurality of interpretable layers, calculating high-risk significant factors and automatically recommending clinical decision suggestions; the personalized diagnosis and treatment module is used for constructing a process knowledge graph, carrying out personalized treatment recommendation and distributing corresponding rehabilitation execution tasks for different roles; and the doctor-patient interaction module is used for establishing a multi-terminal collaborative micro-service architecture and carrying out multi-terminal collaborative management and early warning information real-time pushing. The cardiovascular adverse event prediction accuracy and efficiency of the chronic kidney disease patient can be effectively improved, personalized treatment schemes are pushed to different patients, and early diagnosis and early intervention of chronic kidney disease related complications are achieved.

The invention relates to the technical field of biological detection, and particularly discloses a microbial marker combination for predicting neoadjuvant chemotherapy sensitivity of colorectal cancer and application of the microbial marker combination, and the microbial marker combination comprises treponema, staphylococcus, streptococcus, facultative diplococcus and fusobacterium. The invention further discloses a method for predicting the neoadjuvant chemotherapy sensitivity of colorectal cancer and application of the microbial marker combination for predicting the neoadjuvant chemotherapy sensitivity of colorectal cancer and application of the microbial marker combination for predicting the neoadjuvant chemotherapy sensitivity of colorectal cancer, and the microbial marker combination comprises treponema, staphylococcus, streptococcus, facultative diplococcus and fusobacterium. According to the application, by analyzing the microbial community composition in the tumor microenvironment of the colorectal cancer patient, the microbial marker related to the neoadjuvant chemotherapy sensitivity is identified, the patient insensitive to neoadjuvant chemotherapy can be accurately predicted based on the microbial marker combination disclosed in the application, the treatment risk is reduced, the invalid treatment is reduced, and the treatment effect is improved. And a reliable basis is provided for formulating precise personalized treatment.

Disclosed herein are formulations and methods of administering formulations to starve cells of nutrients, such as amino acids. The formulations can be substantially devoid of one or more non-essential amino acids. A formulation disclosed herein can be given to a subject to treat a disease, for example, cancer. Removing a particular amino acid or a combination of amino acids from the diet of a subject can lead to improved treatment of various diseases, including cancer. Reduction of amino acid levels in a subject reduces the production of proteins, metabolites, lipids, or nucleic acid that can promote cancer growth and metastasis.

The invention relates to the technical field of hyperspectral image processing, and particularly discloses a tumor classification method and system based on a tumor hyperspectral image, which can provide richer and more meticulous tumor tissue feature information including spectral features, morphological features, metabolic features and the like through a multiband reflection spectral image acquired by hyperspectral imaging equipment. The comprehensive information acquisition is helpful for doctors and researchers to comprehensively understand the characteristics of tumor tissues, and an important basis is provided for accurate diagnosis and personalized treatment. The invasiveness is one of the important indexes of the tumor malignancy degree, and knowing the invasiveness characteristics of the tumor tissue is beneficial to evaluating the growth and diffusion capabilities of the tumor tissue, so that important reference is provided for formulating a treatment scheme and predicting the prognosis of a patient. Meanwhile, the dynamic influence of spatial distribution on the tumor tissue metastasis capacity is quantified, and the metastasis potential of the tumor tissue can be evaluated more accurately. Doctors can be helped to formulate a treatment scheme better, the transfer process is intervened in time, and the treatment effect and the survival rate are improved.

The present invention relates to the field of intelligent emergency technology, specifically to a cardiovascular disease prognosis prediction method and system based on a nomogram and an attention model, the method comprising: S1: data access, collection and preprocessing; S2: using an attention model to generate a patient's prognosis prediction; S3: constructing a prognosis prediction nomogram for acute cardiovascular disease patients; S4: real-time prediction and system deployment. The system comprises a data acquisition module, a data processing module, a prediction module, an operation module and an update module; the present invention provides an analysis model for scoring prognosis-related factors of acute cardiovascular disease patients by combining an attention model with a nomogram, which helps to improve prediction accuracy, interpretability and personalized treatment guidance, thereby promoting clinical decision-making efficiency and the development of future research.

Genetically modified zebrafish, in which mutation combinations frequently identified in human AML are stably expressed in the stem cell population of the fish, are provided. The combination of mutations result in morphologic, cytochemical and molecular changes of its blood cells that are remarkably similar to those in human AML. The zebrafish model provides a foundation for the study of AML initiation and progression and a high throughput in vivo drug screening platform to identify personalized therapies for AML based on specific mutation combinations. The method of drug screening includes contacting embryos or adult fish containing mutations as disclosed herein, with a test agent, at test concentrations and test intervals to determine the therapeutic effect if any, of the test agent.

A method for treating a language disorder in a patient includes receiving therapist input specifying a speech target and engagement indicator priority, capturing audio of a speech response to a therapy prompt, and identifying the language of the response using a multilingual language identification model. The method further comprises analyzing the speech response with a language-specific recognition model to extract speech features and classify errors across multiple linguistic and acoustic dimensions. Engagement indicators are extracted and used to compute an engagement score, which, along with the error classifications and speech target, informs a decision model that selects a therapy task. The selected task is presented to the patient, and a subsequent speech response is captured to update error classifications. The decision model is iteratively refined based on therapist input and revised error data, enabling adaptive, personalized therapy progression.

The present application relates to the field of medical immunology technology, and more particularly to biomarkers and their application in the assessment of liver damage in end-stage liver disease. In the present application, the proportion of CCR7+CD8+ T cells in CD8+ T cells is used as a marker for the assessment of liver damage in end-stage liver disease, which can reflect the liver immune status of end-stage liver disease, especially the assessment of the degree of liver damage, has high specificity and sensitivity, and provides a scientific basis for the development of clinical individualized treatment plan, and has good practical application value. At the same time, while reflecting the liver damage process of the patient, it can also reflect the immune status of the patient's liver, and can provide certain theoretical basis for slowing down the disease progression of subsequent biliary atresia and other end-stage liver disease patients with liver damage.

The present invention provides methods relating to the discovery of olfactomedin 4 (OLFM4) as a biomarker for acute kidney injury (AKI) and need for renal replacement therapy, and further as a biomarker for responsiveness to the furosemide stress test (FST). The methods described here are useful in clinical decision support and personalized therapy for AKI, as well as for clinical trial design.

The invention belongs to the technical field of biological medicines, and particularly relates to application of a cancer suppressor gene SLC26A11 in hyperleukocyte acute B lymphocytic leukemia. The invention discloses an application of a cancer suppressor gene SLC26A11 as a specific molecular marker in preparation of induction treatment reaction, prognosis and risk assessment of high leukocyte acute B lymphocytic leukemia of children, and an application in improvement of sensitivity of chemotherapeutic drugs for high leukocyte acute B lymphocytic leukemia of children. The SLC26A11 is used as a potential target, so that a personalized treatment scheme is provided for HALL patients, the toxic and side effects of chemotherapeutic drugs are reduced, and the treatment effect is improved.

The invention belongs to the technical field of tumor prognosis prediction, and relates to a colorectal cancer allotropic liver metastasis prediction method and system.The method comprises the steps that colorectal adenocarcinoma cases are collected, the volume of an intra-tumor region-of-interest is delineated and automatically expanded to generate the volume of a peritumor region-of-interest, image omics characteristics are extracted through a pyradiomics packet, core omics characteristics are screened out, and the colorectal cancer allotropic liver metastasis prediction result is obtained. The method comprises the following steps: respectively constructing an intratumoral model and a peritumoral model, analyzing and integrating intratumoral and peritumoral core omics characteristics through logistic regression to form a combined radiomics model, integrating the combined radiomics model and clinical risk factors, establishing a column diagram for predicting the non-hepatic metastasis lifetime, and dividing patients into a low-risk group and a high-risk group according to a column diagram score threshold value; according to the method, LMFS prediction results of 1-5 years can be quickly output, and 0.6911 is set as a standardized risk stratification cut-off value so as to support risk stratification and personalized treatment decision of a patient.

The present invention provides a wearable conformal ultrasonic therapy system and a use method. The system comprises an acquisition module, a control module, and an ultrasonic therapy apparatus. The acquisition module is in communication connection with the control module and is used for monitoring physiological parameters of a human body in real time and sending the physiological parameters to the control module. The control module is in communication connection with the ultrasonic therapy apparatus and is used for determining a first therapeutic parameter based on the physiological parameters, adjusting a second therapeutic parameter according to the first therapeutic parameter to obtain a target therapeutic parameter, and sending a target parameter signal to the ultrasonic therapy apparatus, the second therapeutic parameter comprising a personalized therapeutic scheme of a therapeutic target area. The ultrasonic therapy apparatus is used for transmitting ultrasonic waves in response to the target parameter signal and carrying out ultrasonic therapy on the therapeutic target area. The system integrates the acquisition module, the control module, and the ultrasonic therapy apparatus, the whole system is wearable and conformal, ultrasonic closed-loop therapy is achieved, the accurate therapy of the therapeutic target area is achieved by adjusting therapeutic parameters, and the physiological parameters are controlled to be in a normal state.

The invention provides an RNA composition for diagnosing idiopathic inflammatory myopathy or evaluating treatment of idiopathic inflammatory myopathy, a kit and application thereof. The RNA composition comprises the following components: hsa-miR-590, hsa-miR-181a, DEFA1 (diethylaminofluoroacetic acid) and RP3-4489.2 (recombinase protein 3-4489.2). In patients with the idiopathic inflammatory myopathy, hsa-miR-590, hsa-miR-181a and RP3-4489.2 can be obviously reduced, and DEFA1 can be obviously increased, so that the RNA composition can be used as a biomarker for diagnosing the idiopathic inflammatory myopathy or evaluating the treatment effect of the idiopathic inflammatory myopathy, has the advantages of high sensitivity, high specificity and high accuracy, and can be used for preparing the biomarker for treating the idiopathic inflammatory myopathy. And a reliable scientific basis and a personalized treatment idea can be provided for treatment and clinical diagnosis of idiopathic inflammatory myopathy.

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.

The invention provides a personalized prediction system for the clinical curative effect of a phlegm-heat clearing injection. The personalized prediction system comprises an input module, a curative effect prediction module and an output module. The input module is used for acquiring the basic condition of a to-be-predicted patient, the combined medication information and the injection time of the phlegm-heat clearing injection; the curative effect prediction module is used for predicting the ideal probability value of the clinical curative effect of a to-be-predicted patient under different combined medication treatment schemes by using the established personalized prediction model for the clinical curative effect of the phlegm-heat clearing injection by taking the C-reactive protein as an outcome index; and the output module is used for outputting probability values of ideal clinical curative effects of the to-be-predicted patient under different combined medication treatment schemes. Based on the screened 13 indexes, the XGBoost model is adopted to construct the personalized prediction model and the prediction system for the clinical curative effect of the phlegm-heat clearing injection, and the optimal personalized treatment scheme of the phlegm-heat clearing injection and the combined drug thereof is rapidly, accurately and efficiently determined.