79 results about "Personalized therapy" patented technology

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 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 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.

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 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 relates to the technical field of medical instruments, and discloses a transcranial magnetic stimulation therapy-based stimulation coil positioning system and method.The system comprises a biological signal sensing module and a positioning decision module, the biological signal sensing module acquires multi-modal physiological data including cortical electric signals, hemodynamic data and magnetoencephalogram data in real time, and the positioning decision module determines whether the cortical electric signals, the hemodynamic data and the magnetoencephalogram data are in real time; and a target spot positioning instruction is generated through dynamic noise suppression and feature fusion of the modeling processing layer. The modeling processing layer comprises a signal preprocessing module and a collaborative optimization module, the signal preprocessing module carries out space-time segmentation, anomaly filtering and standardized registration on original data, and the collaborative optimization module realizes frequency domain denoising, anatomical correlation modeling and multi-dimensional fusion of multi-modal data through a biological noise filtering layer, a target point correction layer and a positioning execution layer. The method comprises the steps of data acquisition, noise suppression, feature fusion and positioning instruction generation. Through multi-modal data fusion and dynamic modeling, the positioning accuracy and clinical adaptability are improved, and the method is suitable for nerve regulation personalized treatment.

Embodiments of the instant disclosure relate to an in vitro system for generating 3D skin equivalent, compositions, and methods for modeling and analyzing potential therapeutic treatments of Ehlers-Danlos Syndrome (EDS), including personalized therapies and regimens. In certain embodiments, the in vitro system can be utilized to facilitate diagnosis, drug discovery, investigation of disease mechanisms, and treatment of EDS and other connective tissue diseases. In certain embodiments, compositions, and methods for making and using matrix metalloproteinase 9 (MMP-9) inhibitors are disclosed to treat EDS. In some embodiments, a subject has hypermobility type EDS (hEDS).

A CSF management method is used with a patient having a body with CSF having a natural flow rate. The method determines determining a target volume for a CSF-containing target compartment of a patient from a volumetric image. The method determines a personalized therapeutic infusion volume of a therapeutic material as a function of the target volume and a total therapeutic infusion volume. A CSF circuit is formed to control flow of CSF in the body. The personalized therapeutic infusion volume is added to the CSF via the CSF circuit at a first time. The therapeutic material is directed, via the CSF, toward the target compartment of the patient. The flow of the CSF in the CSF circuit is adjusted to localize the CSF at the target compartment of the patient.

The present disclosure relates to Sigma-1 receptor agonists, and their target engagement and capacity to correct multiple EEG biomarkers of cortical dysfunction. The present disclosure further provides Precision Medicine or personalized therapy of using a Sigma-1 receptor agonist in treating neurodevelopmental and / or neurodegenerative disorder through engaging with EEG biological pathways. Also provided are kits for practicing the methods.

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.

Immunotherapeutic Monoclonal Antibody Complexes or Conjugates (IMAC) comprising readily accessible antibodies designed and approved for clinical use are provided using a one-step method that combines killing of existing cancer cells in parallel with induction of long-lasting anti-cancer vaccination. Methods for their use, alone or in combination with cancer killer cells including intentionally mismatched donor T cells, NK cells concomitantly with additional anti-cancer or immune activating agents, or activation of patient's own immune system for personalized treatment of cancer and elimination of undesirable non-malignant cells are also provided. In addition, treatment method based on IMAC can be applied for in vivo vaccination against cancer using an existing malignant lesion as internal anti-cancer vaccine by engagement of patients antigen presenting cells for induction of long-lasting anti-cancer vaccination in situ against residual or recurrent disease.

The invention relates to the technical field of organoid culture, in particular to a construction method and application of a kidney organoid model. Comprising the following steps: S1, carrying out digestion and flow sorting on renal cortex tissues to obtain renal stem cells; s2, carrying out plane culture on the kidney stem cells to obtain kidney epithelial progenitor cells; s3, performing three-dimensional culture on the renal epithelial progenitor cells to obtain the kidney organ model. According to the method, an adult renal cortex tissue is taken as a starting cell source, a kidney organ model with mature near-end and far-end renal tubule phenotypes is efficiently constructed through an optimized plane amplification and three-dimensional induction culture system, and the formation of kidney organs can be efficiently induced in a short time; the finally obtained kidney organ model can be used for simulating acute kidney injury in vitro, and an efficient tool is provided for kidney disease mechanism research, drug toxicity screening and personalized treatment model construction.

The invention discloses a risk grading prediction system for a perioperative period symptom group of senile non-small cell lung cancer, and belongs to the field of intelligent medical treatment. The risk grading prediction system for the perioperative period symptom group of the senile non-small cell lung cancer comprises a multi-source data acquisition unit, a data analysis unit and a hierarchical prediction unit. The problem that in the prior art, it is difficult to accurately predict the risk level of a symptom group possibly occurring in the perioperative period of a patient is solved, the incidence relation among dyspnea, pain, fatigue and anxiety symptom groups is analyzed by integrating pre-operative, intra-operative and post-operative multi-source data and then constructing a symptom correlation topological graph, and the risk level of the symptom group possibly occurring in the perioperative period of the patient is accurately predicted. According to the method, direct and indirect mutual influences between symptoms are mined, key information is provided for follow-up accurate risk prediction, the XGBoost classifier and the sequential network model are utilized, accurate grading prediction of the risk of the symptom group of the old non-small cell lung cancer patient in the perioperative period can be achieved, and a basis is provided for a clinician to formulate a personalized treatment scheme.

The application discloses a device for predicting the recurrence / metastasis risk of a non-small cell lung cancer patient after operation and application thereof, adopts multiple machine learning algorithms to combine the clinical characteristics, genomic sequencing data and immune microenvironment infiltration characteristics of real world non-small cell lung cancer patients with disease-free survival, and constructs a postoperative recurrence prediction model. Through a Lasso coefficient path diagram, it is confirmed that five characteristics including tumor size, tumor interstitium CD8+ T cell positive rate, tumor parenchyma M2 type tumor-related macrophage positive rate, TP53 gene mutation state and postoperative ctDNA-MRD state are included in model construction, and a MIMIR prediction model is constructed through a random survival forest. The model can help clinicians to accurately evaluate the postoperative disease-free survival of non-small cell lung cancer patients, guide the development of individualized treatment and follow-up schemes, and bring better survival benefits to patients.

The invention discloses a chronic heart failure phlegm syndrome biomarker and application thereof. The biomarker is selected from at least one of ENO1, H2BC12, PCOLCE and TUBA1B. The chronic heart failure phlegm syndrome biomarker provided by the invention has high sensitivity and specificity when being applied to clinical diagnosis or auxiliary diagnosis of chronic heart failure phlegm syndrome patients, and can effectively distinguish the chronic heart failure phlegm syndrome patients from healthy people and chronic heart failure non-phlegm syndrome patients; precise distinguishing and diagnosis of chronic heart failure phlegm syndrome patients are achieved, and the requirements for clinical fine diagnosis and treatment and personalized treatment scheme making are met.

A neuromodulation system disclosed herein provides adaptive stimulation such as time-dependent adaptive stimulation, personalized therapy, and improves battery management while maintaining desired therapeutic effect. Mixed urinary incontinence and other conditions are treated using electrical stimulation, according to several implementations.

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.

The present invention provides methods for personalized therapeutic management of a disease in order to optimize therapy and / or monitor therapeutic efficacy. In particular, the present invention comprises measuring an array of one or a plurality of biomarkers at a plurality of time points over the course of therapy with a therapeutic agent to determine a mucosal healing index for selecting therapy, optimizing therapy, reducing toxicity, and / or monitoring the efficacy of therapeutic treatment. In certain instances, the therapeutic agent is a TNFα inhibitor for the treatment of a TNFα-mediated disease or disorder.

This system provides a consensus molecular subtype classification system for esophageal squamous cell carcinoma based on histopathological images. [Solution] In a molecular subtype classification system that achieves high-precision molecular subtype classification for ESCC and provides important evidence for personalized therapy, molecular subtypes are classified into four types. As for the characteristics of each type, ECMS1 involves metabolic pathway abnormalities, NFE2L2 activation, and the selection of drugs to be used as NFE2L2 inhibitors. ECMS2 involves upmodulation of the classical signaling pathway of the tumor and low methylation. ECMS3 has few copy number change events, low tumor mutation burden, high PD-1 expression, and is beneficial for immunosuppressant treatment. ECMS4 involves activation of the epithelial-mesenchymal transition pathway. The system utilizes a spatial algorithm to extract spatial tissue features from the results of automatic contour extraction of histological images and constructs a machine learning model that performs subtype classification for ESCC based on these features.

The invention belongs to the technical field of disease prognosis prediction, and provides a prognosis prediction method for multidrug-resistant tuberculosis in order to solve the problem that an existing prognosis monitoring method for a multidrug-resistant tuberculosis patient has certain limitation. By integrating handmade radiology features and deep learning derived imaging features extracted from baseline, two-month and six-month continuous CT scanning, processing data by adopting a gated recurrence unit (GRU) network, and combining with a multi-drug-resistant tuberculosis prognosis prediction system, a treatment result of a multi-drug-resistant tuberculosis patient is predicted. According to the prediction method provided by the invention, the high-risk patient can be accurately identified in the early stage of the treatment process, and powerful support is provided for personalized treatment decision making, intervention opportunity optimization and reasonable public health resource allocation. Moreover, the kit has higher sensitivity and specificity, and the potential in the aspects of multi-drug-resistant tuberculosis treatment result prediction and risk stratification accuracy is improved.

Oncology faces a digital chasm in its quest for personalized treatments. Despite the adoption of electronic health records (EHR), most hospitals are ill-equipped for data science research. Embodiments herein describe a continuously learning infrastructure through which multimodal health data are systematically organized and data quality is assessed with the goal of applying artificial intelligence to address unmet clinical needs. Embodiments describe systems and methods for improved cancer prognostics, including by obtaining electronic medical records and performing natural language processing thereon. Additional embodiments apply term frequency inverse document frequency to identify terms that are predictive for cancer survival. Additional embodiments are capable of performing in silico clinical trials based on information comprised in a collection of health records.

A method and system generate personalized therapeutic protocols adapted to a user's physiological state. Biomarkers derived from ECG data are used to identify priorities and resources, then select and combine elements from libraries of over four thousand custom voice recordings, ten thousand audio frequency components, ten thousand micro-current frequency profiles, visual elements and textual information. Protocols adapt daily via biofeedback, incorporating guided sessions or frequency-only harmonic boosts, with sound techniques (binaural beats, noises) and optional professional / AI customization over a 21-day cycle for coherence restoration.

The invention provides a culture medium and a method for constructing a diffuse endogenous brain bridge glioma (DIPG) organ model, the culture medium comprises a DMEM / F12 culture medium, a Neurobasic culture medium, an L-glutamine substitute, a non-essential amino acid supplement, an N2 supplement, a B-27 supplement, 2-mercaptoethanol, penicillin, streptomycin and insulin, and the DIPG organ model is prepared from the DMEM / F12 culture medium, the Neurobasic culture medium, the L-glutamine substitute, the non-essential amino acid supplement, the N2 supplement, the B-27 supplement, the 2-mercaptoethanol, the penicillin, the streptomycin and the insulin. In the method, a DIPG sample is cultured into an organoid by using the culture medium. The invention also provides an application of the culture medium in preparation of a DIPG (Diisopropylidyne) organ-like model, the organ-like model prepared by the method, and an application of the DIPG organ-like model in cancer pathogenesis research, cancer treatment drug screening and development, cancer personalized treatment and / or cancer immunotherapy evaluation.