36 results about "Personalized therapy" patented technology

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 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.

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 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 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.

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.

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.

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 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.

This invention provides a method and system for cancer subtype identification based on multi-omics data, belonging to the field of bioinformatics processing technology. The method includes: constructing a DILCORE model that integrates a multi-branch variational autoencoder, contrastive learning, and cross-view attention mechanisms. The multi-branch variational autoencoder decomposes omics observation data into common components for subtype classification and view-specific noise components, achieving noise suppression; the InfoNCE contrastive loss is introduced to bring common representations of the same sample closer across different views, enhancing cross-omics consistency; the cross-view residual self-attention mechanism is used to adaptively weight and fuse common vectors; finally, a self-supervised clustering fine-tuning optimization strategy is introduced to jointly improve representation quality and clustering performance in the latent space. This achieves deep and effective integration of multi-omics data, significantly improving the accuracy of cancer subtype identification and providing a powerful tool for personalized cancer treatment and prognostic assessment.

The invention belongs to the technical field of computer vision and artificial intelligence, discloses an intelligent face skin state evaluation system and method based on multi-angle dynamic feature fusion and deep learning, and aims to solve the problems that an existing evaluation method is high in subjectivity and single in evaluation dimension, and dynamic wrinkles cannot be quantified. The system comprises a multi-angle data acquisition module, a data preprocessing module, a multi-branch deep neural network feature extraction and fusion module and a comprehensive evaluation module. Through synchronous acquisition of 2D images, 3D point cloud data and dynamic video sequences, the system extracts static and dynamic features, fuses information of different modalities, and accurately evaluates skin textures, chromatosis, elasticity, pores, static wrinkles and dynamic wrinkles. The method can be widely applied to the fields of skin care, anti-wrinkle therapy, beauty effect evaluation, cosmetic efficacy verification and the like, provides objective and accurate skin health analysis and personalized treatment scheme support, and has high application value.

The invention discloses a micro-fluidic chip technology, which realizes accurate control and detection of trace fluid by constructing structures such as a micro channel, a valve, a pump and the like on a chip. In the field of cancer detection, the micro-fluidic chip technology can realize efficient control and detection of trace biological samples. According to a cancer biomarker detection technology based on a micro-fluidic chip technology, through design and optimization of a unique flow path and a reaction area, detection condition optimization, a signal amplification technology, chip material manufacturing, antibody probe fixation and the like, the detection sensitivity is improved; according to the present invention, the sample and the specific biomarker detection reagent can be mixed and subjected to the reaction in the microfluidic chip, and the high sensitivity and the high specificity detection on the cancer biomarker can be further improved by detecting the signal (such as the fluorescence, the electrochemical signal and the like) generated by the reaction. The method has important application value in the aspects of early diagnosis, illness monitoring, personalized treatment and the like of cancers.

The application is suitable for the technical field of data analysis, and provides a data analysis system and method for gene regulatory network based on deep regression algorithm, the method comprises the following steps: converting gene identity and gene expression value into vector representation; calculating attention score between gene vector representations as a judgment standard for the relationship between genes; and predicting specific gene expression value. The application enhances the anti-noise performance by introducing a Gaussian layer, captures the complex regulatory relationship between genes by using embedding and attention mechanism, and realizes individualized GRN prediction at the single cell level, which can overcome the defects of the existing method in the personalized treatment of cancer patients, and further support the gene expression analysis and prediction in the personalized treatment of cancer patients.

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.

Disclosed are a delivery system based on sensitized mast cells, a preparation method therefor, and an application thereof, as well as a pharmaceutical composition and a drug administration device involving the delivery system of sensitized mast cells. The delivery system utilizes the specific recognition of tumor antigens by sensitized mast cells to targetedly deliver drugs to tumor cells, while protecting the drugs from the clearance of the circulating immune system. Sensitized mast cells rapidly degranulate under the activation of tumor antigens to release drugs, thereby enhancing infection, exerting an oncolytic effect, lysing tumor cells and releasing antigens, and activating an anti-tumor immune response. The delivery system can be used as a promising platform technology for personalized therapy.

The invention discloses an individualized gene therapy target prediction method and a related device, and the method comprises the steps: screening a cell line sample set through gene expression data and gene dependence effect data, then selecting N genes from the co-occurrence frequency of a plurality of genes with the lowest gene effect value in each cell line sample as target genes, and carrying out the prediction of the individualized gene therapy target. The target gene is a gene having important influence on cell growth inhibition, then a data set is constructed by taking the screened target gene as a label, and a regression model is trained to obtain a prediction model, so that the prediction model has the capability of predicting a gene target spot effective for treatment; finally, tumor transcriptome sequencing data of a patient are input into the prediction model, an output result serves as an individualized target gene prediction result, individualized treatment is conducted on the patient through the predicted target gene, and compared with related technologies, the screening efficiency of individualized treatment gene targets is effectively improved.

This invention discloses a method for constructing a benign / malignant prediction model based on CT images of pancreatic tumors. The method includes: acquiring preoperative pancreatic CT images through an image storage and transmission system; identifying and preprocessing a region of interest (ROI) based on the pancreatic cancer tumor region in the CT images; extracting radiomics features from the processed ROI; filtering the extracted features using a maximum correlation minimum redundancy algorithm to select features related to the benign / malignant nature of the pancreatic tumor; constructing a benign / malignant prediction model based on the selected features using a support vector machine model; and outputting the final prediction result. The method described in this invention enables rapid and accurate classification of benign / malignant pancreatic tumors, providing auxiliary information for clinical diagnosis and assisting physicians in developing personalized treatment plans.

This invention provides a gold microneedle radiofrequency therapy system and its control method. It employs ultra-micro CPC cooling technology and intelligent segmented emission technology, combining a composite output strategy of pulsed wave (PW) and continuous wave (CW) energy modes. This method allows for multiple controllable radiofrequency releases at a single point. The control method of the gold microneedle therapy system can intelligently select or fuse the optimal energy mode based on the therapy stage, tissue depth, and real-time impedance feedback, enabling intelligent segmented emission and achieving dynamic, precise, and safe personalized therapy.

The present application belongs to the technical field of disease prognosis prediction, and aims at the problem that the existing prognosis monitoring method for multidrug-resistant tuberculosis patients has certain limitations. The present application provides a multidrug-resistant tuberculosis prognosis prediction method, which integrates the manually made radiology features and the deep learning derived imaging features extracted from the baseline, two-month and six-month continuous CT scans, adopts a gated recurrent unit (GRU) network to process the data, and combines a multidrug-resistant tuberculosis prognosis prediction system to predict the treatment results of multidrug-resistant tuberculosis patients. The prediction method provided by the present application can accurately identify high-risk patients in the early stage of the treatment process, and provides strong support for personalized treatment decisions, optimization of intervention timing and reasonable allocation of public health resources. Moreover, the present application has higher sensitivity and specificity, and improves the potential of the accuracy of multidrug-resistant tuberculosis treatment result prediction and risk stratification.

Systems and methods are provided for collecting and processing data from liver cancer patients that can be used to personalize treatment plans for these patients, including by determining and considering their liver functional reserve LFR. Specifically, systems and methods are provided for using fLV (functional liver volume) and PHM (perfused hepatic mass), before and after treatment, to determine personalized treatment plans and improve overall survival rates.

The application discloses a triple-negative breast cancer cell channel grouping method, application and system, and relates to the technical field of tumor treatment, and can be used for treating triple-negative breast cancer. The method comprises the following steps: obtaining triple-negative breast cancer scRNA-seq data of a plurality of target samples; processing the triple-negative breast cancer scRNA-seq data to obtain first data of target tumor cells, and reprocessing the first data to obtain a channel correction enrichment score of a single target tumor cell; grouping the target tumor cells according to the correction enrichment score to obtain target tumor cell groups; and identifying characteristic activation channels of each target tumor cell group. The method can group triple-negative breast cancer tumor cells to obtain cell groups with different gene channel expression modes, and can formulate a treatment scheme according to the channel characteristics of each cell group, which has important reference value for clinical individualized treatment and prognosis prediction. The application also discloses a system for implementing the method and application of the method in finding clinical treatment targets and predicting prognosis.

The invention provides a gastric cancer multi-modal data cross-modal fusion and prognosis evaluation method based on a large model, and belongs to the technical field of multi-modal data processing. S2, data preprocessing; s3, feature extraction; s4, carrying out cross-modal data fusion; s5, performing model training; s6, prognosis evaluation. The accuracy and reliability of gastric cancer prognosis evaluation are improved, more powerful support is provided for clinical diagnosis and treatment, the requirement for personalized prognosis evaluation in clinical practice is met, and a doctor can make a more accurate personalized treatment scheme for a patient.

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).

The present invention relates to personalized therapeutic anti-cancer vaccines, methods for treating cancer in which such anti-cancer vaccines are used, and methods for manufacturing such vaccines. [Solution] An immunologically effective amount, (i) a polynucleotide comprising nucleotide sequences encoding a targeting unit, a dimerizing unit, and an antigenic unit, wherein the antigenic unit comprises at least one patient-presented covalent antigen sequence or one or more portions thereof; or (ii) A polypeptide encoded by the polynucleotide as defined in (i); or (iii) A dimeric protein consisting of two polypeptides encoded by the polynucleotide defined in (i); and Medicinally acceptable carriers Personalized therapeutic anti-cancer vaccines, including [specific ingredient / feature].

The invention relates to a glioblastoma M1 / M2 / M3 molecular typing method based on metabolic genes and application of the glioblastoma M1 / M2 / M3 molecular typing method. According to the method, expression quantity data of specific genes in a glioblastoma sample is detected, and the glioblastoma sample is divided into M1, M2 or M3 typing through a consistency clustering algorithm; on the basis, a model with specific gene expression quantity as input and typing attribution as output is constructed by adopting a nearest neighbor systolic center algorithm, and the model is an M1, M2 or M3 typing prediction model. By means of the method, M1 / M2 / M3 typing and individualized diagnosis of the patient can be rapidly and conveniently completed, and guidance of risk stratification and individualized treatment is provided for the patient.

The invention belongs to the technical field of tumor biology and organoids, and provides application of PARVA in promoting formation of tumor organoids. The PARVA comprises a PARVA protein or a PARVA coding gene; the application comprises application in preparation of a tumor organoid inducer, a tumor organoid culture kit and construction of a tumor organoid model. According to the invention, the formation efficiency and stability of the tumor organoid can be obviously improved, the difficulty in constructing the organoid with difficult-to-cultivate tumors such as lung cancer and bile duct cancer is solved, and a reliable model is provided for the fundamental research of the tumors; the constructed tumor organ model has a tissue structure, gene characteristics and drug responsiveness which are highly similar to in-vivo tumors, can be used for tumor generation mechanism research, antitumor drug high-throughput screening and clinical personalized treatment scheme evaluation, and has important scientific research value and clinical transformation significance.