173 results about "Treatment response" patented technology

The invention discloses a sugar chain marker combination for predicting prognosis of liver cancer transarterial chemoembolization and application of the sugar chain marker combination. The invention discovers and verifies that the combination composed of NA3Fb, NA4Fb and NA4F2b can be used as a novel marker for evaluating TACE treatment reaction and prognosis by analyzing a serum sugar chain map of a liver cancer patient for the first time. Based on the marker combination, a TACE-GT prognosis scoring model and system are established, and the progression risk of a patient within 12 months after a TACE operation can be effectively distinguished by noninvasively detecting the expression levels of three carbohydrate chains in serum of the patient and calculating a specific score. The detection method provided by the invention is based on a blood sample, and has the advantages of noninvasiveness and convenience, and a risk assessment system can provide individualized curative effect prediction and decision support for clinicians before and after TACE treatment, and has important values for realizing precise treatment of liver cancer and improving prognosis of patients.

The invention relates to an ultra-long-range medical record diagnosis and treatment question answering system and method based on a dynamic time sequence semantic map, belongs to the technical field of medical artificial intelligence and information processing, and solves the problems that in the prior art, the inference ability is insufficient when ultra-long-range medical records are processed. The system comprises a pre-processing module for pre-processing overlong-range medical record information of a patient to generate standardized data; the medical entity recognition module is used for recognizing event entities and descriptive entities in the standardized data; associating a time attribute for the event type entity to generate a time sequence entity tuple; outputting a medical entity recognition result; the dynamic semantic map construction module is used for constructing a specific dynamic semantic map of the corresponding patient; the text-map dual-mode fusion query engine is used for analyzing the medical problem proposed by the user and carrying out text-map dual-mode fusion query based on the specific dynamic semantic map; and the diagnosis and treatment reply module is used for generating a diagnosis and treatment reply result according to the text-map dual-mode fusion query result.

The invention provides an intelligent auxiliary diagnosis and treatment service system and method based on a large language model, and the system comprises a large language model engine and a medical knowledge dynamic updating device, and the medical knowledge dynamic updating device carries out the automatic and quantitative arbitration of the conflict between the new knowledge extracted from the literature and the existing knowledge of a knowledge graph, and carries out the automatic and quantitative arbitration of the conflict. A conflict detection unit identifies conflict knowledge pairs with mutually exclusive logic, an evidence intensity quantification unit calculates and synthesizes document timeliness, source authority and research type evidence grade multi-dimensional evidence intensity scores for two conflict parties, and a grading arbitration decision unit compares a score difference value with a dynamic threshold value. And making a grading decision of automatically adopting a high-score party, generating an artificial review task or marking dispute coexistence, and finally executing updating by a knowledge graph updating unit. The invention further provides a method corresponding to the system, double-chain collaboration of online diagnosis and treatment response and offline knowledge evolution is achieved, and the problems of automation and intelligentization of conflict processing in medical knowledge updating are solved.

The invention relates to the field of medical image processing and artificial intelligence, in particular to a pulmonary nodule treatment effect AI evaluation system which comprises an image acquisition module, an image registration module, a feature representation module, a multi-scale analysis module, a trajectory analysis module, a response prediction module and a decision support module. According to the system, accurate alignment of CT images before and after treatment is realized through a 4D registration technology, manifold representation of a pulmonary nodule state is constructed based on a differential geometry theory, and nodule features are mapped into points on a high-dimensional manifold; extracting features of different time and space scales by adopting multi-scale space-time analysis, and constructing a manifold trajectory representing a treatment response process; geodesic prediction is realized by using a Riemann geometric framework, and long-term curative effect is predicted from early treatment response; the system not only evaluates the current treatment effect, but also can provide personalized treatment suggestions and optimal follow-up visit plans.

The invention discloses a longitudinal CT image-assisted rectal cancer neoadjuvant radiotherapy and chemotherapy treatment reaction evaluation device, which comprises a focus area segmentation module, a rectum focus area detection module, a rectum focus area detection module, a rectum focus area detection module, a rectum focus area detection module, a rectum focus area detection module, a rectum focus area detection module, a rectum focus area detection module and a rectum focus area detection module, obtaining a standardized three-dimensional tumor area; a feature fusion module extracts deep learning features of a focus area after segmentation at two time points before neoadjuvant radiotherapy and chemotherapy and before an operation from the three-dimensional tumor area, and performs weighted fusion on the deep learning features at the two time points through a dynamic attention weight layer to obtain longitudinal comprehensive features representing tumor treatment response changes; and the prediction module fuses the longitudinal comprehensive characteristics and clinical baseline data to construct a multi-modal prediction model, and outputs a treatment response prediction result of the rectal cancer patient on neoadjuvant chemoradiotherapy, so that the prediction accuracy of pathological complete remission of the rectal cancer patient can be improved, and the prognosis of the patient and the utilization efficiency of medical resources can be improved.

Methods of treating a condition which is treatable with SSR-125543 or a pharmaceutically acceptable salt thereof in a subject in need thereof are provided. The methods of treatment include predicting a treatment response of a subject to a treatment with SSR-125543 and / or detecting a polymorphism genotype associated with a treatment response of a subject to treatment with SSR-125543. Sets of at least one polymorphism genotype useful in the predicting and / or detecting steps are also disclosed.

This invention belongs to the field of biomedical technology, specifically relating to the application of DNA damage response gene-related SNPs in the treatment and prognosis of acute myeloid leukemia (AML). This invention is the first to discover that single nucleotide polymorphisms (SNPs) in the DNA damage response pathway (focusing on three major functional modules: damage sensing, signal transduction, and DNA repair) are closely related to the clinical characteristics, treatment response, and survival prognosis of AML. This lays the foundation for constructing a multi-gene risk model based on the DNA damage response pathway and provides theoretical support for integrating germline SNP maps into precision medicine strategies for AML.

The present invention relates to a biomarker for predicting immunotherapeutic responsiveness based on spatial transcriptome analysis and uses thereof and, in particular, to: a marker composition for predicting the responsiveness of cancer patients to immunotherapy; a composition for predicting the responsiveness of cancer patients to immunotherapy; a kit for predicting the responsiveness of cancer patients to immunotherapy, comprising the composition; a method for providing information for predicting the responsiveness of cancer patients to immunotherapy; and a method for providing information for predicting the survival prognosis of cancer patients. The biomarker for predicting immunotherapeutic responsiveness, according to the present invention, was discovered by applying spatial transcriptome technology and analyzing cell group-specific gene expression values according to location information of cells in tissue sections, and can more precisely and accurately predict the responsiveness of cancer patients to immunotherapy and the survival prognosis of patients, thus enabling suitable treatments for patient groups, which may result in improved therapeutic effects and a reduction in pain and costs for patients.

The invention discloses a proton adaptive radiotherapy triggering decision method and system based on multi-modal data quantitative evaluation. The method comprises the following steps: acquiring image data, dose data and clinical data, respectively calculating a dosimetry scoring function, an anatomical structure scoring function and a proton specificity scoring function based on the image data and the dose data, obtaining an enhanced core quantitative score through weighted geometric average fusion, and obtaining the enhanced core quantitative score; determining parts, stages and prognosis adjustment parameters based on clinical data, and fusing a BMI dynamic evaluation factor, a treatment response prediction factor and a multi-factor interaction factor to obtain an enhanced clinical background adjustment coefficient; and calculating a final decision score in combination with the time modulation factor and the risk prediction index based on the LSTM, and outputting a grading decision result. According to the method, objective quantitative evaluation of deep fusion of multi-modal data is realized, specific risks of proton therapy are accurately captured, personalized intelligent decision is supported, and the method has good clinical operability.

The present application relates to the field of biomedical technology, and discloses an immune infiltration desert tumor benefit ICI treatment method based on a weighting strategy. The method obtains spatial transcriptome data of tumor tissue of a patient, divides the tumor microenvironment into functional regions through an adaptive grid division algorithm, and extracts a multi-scale feature vector; calculates dynamic weight coefficients of each functional region using a graph attention network, and generates a comprehensive response index after weighted integration; and finally outputs an individualized ICI treatment decision according to the comparison result of the index and a preset threshold. The present application realizes dynamic evaluation of tumor microenvironment heterogeneity based on spatial transcriptome data, and provides ICI treatment response prediction and clinical decision support for patients with immune infiltration desert tumors.

This disclosure generally relates to a molecular classification of cancer and particularly to molecular markers for predicting response to cancer therapy, including cancer immune therapy, and methods of use thereof.

The present invention relates to a method of determining the responsiveness of a cancer patient to treatment with a compound that inhibits a KRAS protein or a KRAS protein mutant or treatment with a compound that inhibits the interaction between MDM2 and p53, the method comprising measuring the survivin level in a first sample obtained from the patient prior to treatment with the compound, measuring the level of survivin in a second sample obtained from the patient during or after treatment with the compound, comparing the level of survivin in the second sample to the level of survivin in the first sample, and determining the level of survivin in the second sample when compared to the level of survivin in the first sample. When the survivin level in the second sample decreases, it is determined that the patient is responsive to treatment with the compound. The invention further relates to the use of survivin in compounds for determining the inhibition of KRAS protein or KRAS protein mutants, or in compounds for inhibiting the interaction between MDM2 and p53, or a pharmaceutical formulation comprising said compound that inhibits a KRAS protein or a KRAS protein mutant or said compound that inhibits an interaction between MDM2 and p53 in a method of treating cancer.

The present disclosure relates to a panel of ER regulated genes and method for using the panel for monitoring the response of an individual having cancer to treatment with amcenestrant, the method comprising determining a first ER activity score from a sample from the individual at a first time point before treatment with amcenestrant using the panel, then determining a second ER activity score from a sample from the individual at a second time point following administration of amcenestrant using the panel, and comparing the first ER activity score with the second ER activity score, wherein a decrease in the second ER activity score relative to the first ER activity score is predictive of target engagement of amcenestrant.

Methods of predicting whether a subject with chronic myelogenous leukaemia (CML) is at risk of relapse following cessation of a treatment for CML, whether a subject with CML will achieve TFR following cessation of a treatment for CML, and selecting a subject for treatment, wherein the subject has CML or has been diagnosed with CML are disclosed herein. The methods comprise determining the expression of one or more genes regulated by PRC1. Also provided are kits and antigen-binding molecules suitable for use in the disclosed methods.

Described herein are particle-driven radiogenomics systems and methods that can be used to identify imaging features for prediction of intratumoral and interstitial nanoparticle distributions in cancers (e.g., in low grade and / or high-grade brain cancers (e.g., gliomas, e.g., primary gliomas)). In certain embodiments, the systems and methods described herein extract and combine quantitative multi-dimensional data generated from structural, functional, and / or metabolic imaging. In certain embodiments, the combined multidimensional data is linked to intratumoral and interstitial nanoparticle distributions. For example, this linked data can be used to determine quantitative functional-metabolic multimodality particle-based imaging features and to predict treatment efficacy. These techniques provide an improved quantitative ability to measure treatment response and determine tumor progressions compared to traditional size-based imaging methods.

The present invention relates to a pharmaceutical composition for use in the treatment of both low-grade and high-grade glioblastoma in a human subject. The composition comprises a synergistic combination of the following active agents: R-Ketorolac, Bumetanide, and Niflumic acid. This specific combination is designed to exert a synergistic therapeutic effect against glioblastoma by targeting multiple molecular pathways involved in tumor progression, inflammation, and ion transport. In a further aspect of the invention, there is provided a method for selecting a subpopulation of glioblastoma subjects who are responsive to the aforementioned treatment. This method comprises identifying genetic profiles predictive of a favorable therapeutic response to the combined administration of R-Ketorolac, Bumetanide, and Niflumic acid.

The present invention relates to a biomarker for predicting an asthma treatment response to a biological agent and a use thereof, and specifically to a method for predicting an asthma treatment effect

The present application relates to the field of liver cancer immunotherapy efficacy evaluation method combining imageomics and deep learning, and specifically discloses a liver cancer immunotherapy efficacy evaluation method combining imageomics and deep learning. The method comprises the following steps: acquiring multi-modal medical images and clinical information of a liver cancer patient; performing standardization preprocessing and automatic lesion segmentation on the images; extracting features through a multi-scale deep network and realizing semantic alignment by using a cross-modal attention mechanism; fusing the images and the clinical data to construct a multi-source heterogeneous feature matrix; adopting a hierarchical model structure, modeling the spatial distribution of tumor immune microenvironment by using a graph neural network at the bottom layer, dynamically tracking the evolution of efficacy by using a gated recurrent unit at the upper layer, and finally outputting an immune response probability, a tumor load trend and a treatment response grade. The present application can objectively and quantitatively evaluate the efficacy of liver cancer immunotherapy, and improve the precision and automation level of individualized diagnosis and treatment decision-making.

Provided herein is a peptide array comprising a plurality of flagellin peptides corresponding to highly conserved peptide regions. For example, the peptide array comprises a plurality of Lachnospiraceae flagellin peptides, which can be selected from a hinge region of Lachnospiraceae flagellin. Also provided is antibody of fragment thereof that binds to one or more of the plurality of the flagellin peptides in the peptide array. The peptide array and antibody are useful in determining an immunosignature from a biological sample from a subject. The immunosignature is useful in diagnosis of an immune-mediated disease, in monitoring progression of an immune-mediated disease, in identifying subjects susceptible to certain treatments, and in monitoring treatment response.

The application discloses a 7T magnetic resonance head and neck tumor radiotherapy response evaluation system, belongs to the field of medical imaging and tumor radiotherapy, and comprises an ultrahigh-field multi-modal image acquisition module, a multi-phase accurate registration and radiomics feature extraction module, an early response biomarker identification module and an adaptive treatment decision support module. The high resolution, high signal-to-noise ratio and multi-nuclide imaging capability of the 7T ultrahigh-field magnetic resonance are utilized to acquire multi-modal images before and during treatment, more than 200 features are extracted through accurate registration and radiomics analysis, the treatment response is predicted based on the Delta feature and the deep learning model 2 to 3 weeks after the start of radiotherapy, the prediction accuracy reaches 87.5%, the prediction is 3 to 4 weeks earlier than the conventional evaluation, decision support is provided for timely adjustment of the treatment strategy, a closed-loop feedback mechanism is established, the clinical verification results are used to optimize the parameters of each module reversely, and continuous improvement is realized.

The invention provides an epileptic information management method and system, and relates to the technical field of medical information management. The method comprises the following steps: firstly, performing standardization processing on multi-source heterogeneous data of a patient, and constructing a multi-dimensional patient portrait containing static, dynamic and time sequence features; secondly, performing multi-level clustering on historical patients based on treatment response and disease course evolution similarity; generating a management suggestion according to the association degree of the to-be-managed patient and the clustering cluster and the current management scene; and finally, dynamically updating the portrait and the clustering affiliation based on treatment feedback. According to the invention, by mining time sequence features and establishing a closed loop feedback mechanism, precise classification and dynamic personalized management of epileptics are realized, and the accuracy of clinical treatment recommendation is significantly improved.

Provided are a method for predicting a treatment response to cancer immunotherapy, the method comprising a step of evaluating, from feces isolated from a patient, the enrichment of a strain of the order TANB77 according to GTDB phylogenetic classification or taxa belonging to the order TANB77, and a step of predicting a treatment response of the patient to cancer immunotherapy on the basis of the enrichment of the order TANB77 or the taxa; a probiotics composition for improving a treatment response; a fecal microbiota transplantation composition; and a method for screening for candidate prebiotics by using same.

Provided herein are methods for determining a subtype of a small cell lung cancer in a patient based on the express status of ASCL1, NEUROD1, and POU2F3, which are expressed in a mutually exclusive fashion. The subtype of the cancer can be used to determine the sensitivity of the cancer to certain anti-cancer therapies. As such, also provided are methods of treating patients having small cell lung cancer based on the subtyping results.

Embodiments of the invention encompass identification and characterization of microbiota having potential or demonstrated therapeutic effects for certain conditions in certain non-human animals. In some embodiments, the identification and characterization of microbiota may include identifying and characterizing the microbiota of potential donors, characterizing the potential donors, and identifying potentially therapeutic microbiota based on analysis of such data from multiple individuals. In some embodiments, the identification and characterization of microbiota may include testing, that is, administering microbiota of potential donors to animals suffering from a disease or condition, documenting any therapeutic response, and optionally identifying and characterizing the microbiota of recipients before and after treatment, and optionally identifying demonstrated therapeutic microbiota. Embodiments of the invention encompass compositions comprising the microbiotia, including solid oral compositions, methods of making these compositions, and methods of treatment of non-human animals suffering from various diseases and conditions with these compositions.

Disclosed herein are methods for identifying a subject as having NSCLC that is predicted or is likely to respond to treatment with an ALK inhibitor, for example crizotinib. The methods include identifying a sample including NSCLC tumor cells as ALK-positive or ALK-negative using immunohistochemistry (IHC) and scoring methods disclosed herein. A subject is identified as having NSCLC likely to respond to treatment with an ALK inhibitor if the sample is identified as ALK-positive and is identified as having NSCLC not likely to respond to treatment with an ALK inhibitor if the sample is identified as ALK-negative. According to certain embodiments of the methods, subjects predicted to respond to an ALK inhibitor may then be treated with an ALK inhibitor such as crizotinib.

The present invention provides a computer-implemented method for predicting a treatment response of a mammalian cancer patient to an anti-cancer immunotherapy, the method comprising: a) providing a plurality of features comprising gene expression levels of at least 8 of the following cancer promoting genes: PTGS2, CXCL1, CXCL2, CXCL5, CXCL6, IL1A, HAS2, IL11, IL1B, EREG, IL6, CXCL8 (IL8), MMP12, NRG1, VCAN, AREG previously measured in a sample obtained from a tumor of the mammalian cancer patient; b) calculating a score using the features provided in a), and c) predicting a treatment response of the mammalian cancer patient based on the score calculated in step b) Related methods and systems for predicting a treatment response are also provided.

This invention provides a method for evaluating the response of rheumatoid arthritis (RA) patients to Janus kinase (JAK) inhibitor treatment. By detecting specific antibody biomarkers in a patient's biological sample and comparing the data with reference values ​​for treatment response, it is possible to predict whether a patient will respond well to the JAK inhibitor or achieve disease remission. Furthermore, this invention includes a assay kit utilizing target peptide sequences that can bind to specific antibodies and supporting materials for detection, which helps to accurately predict the therapeutic efficacy response to JAK inhibitors.

The present invention discloses a set of novel epigenetic biomarkers for early prediction, treatment response, recurrence and prognosis monitoring of pancreatic cancer. Aberrant methylation of genes can be detected in tumor tissues and plasma samples from pancreatic cancer patients but not in normal healthy individual. The present disclosure also discloses primers and probes used herein.

The invention provides a transcranial magnetic stimulation treatment sleep disorder data analysis method, which comprises the following steps of: identifying a conversion process of sleep maintenance capability from continuous improvement to a stable plateau phase according to a sleep improvement degree in an early stage of consolidation and a synaptic connection daily attenuation degree, and determining a key turning time point when a transcranial magnetic stimulation treatment effect reaches a peak value; according to the weakening degree of the short interval treatment on the synaptic connection stability, the consolidation later-stage recovery blocking degree and the sleep time, the weekly decline time of the sleep maintaining ability is recognized, and a sleep ability decline record is obtained; dividing and integrating the key turning time point and the sleep ability decline record according to the adjusted treatment stage, evaluating the transcranial magnetic stimulation treatment effect improvement degree of the chronic insomnia patient, and obtaining an overall treatment response evaluation result.

Provided is a means to predict the anticancer therapeutic efficacy of 1-((5-(1R)-1-fluoroethyl)-1,3,4-oxadiazol-yl)methyl)-6-(4-methoxypyrrolo[2,1-f][1,2,4]triazin-5-yl)-2-methyl-1H-imidazo[4,5-b]pyridine or a derivative thereof in solid cancer and thereby specifying a subject who will be the most benefitted from treatment with the compound and treatment with the compound in combination with another drug and / or another therapy. As a biomarker for predicting therapeutic response to the compound, a genetic character predicted to impart sensitivity to CLK inhibition is used.